JP2013021462A - Terminal device, picture imaging method for terminal device, and picture imaging device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a terminal device, picture imaging method for the terminal device, and picture imaging device that are capable of selecting an optimum picture for an object around a specific object as well as the specific object, and capable of saving a photographer's trouble.SOLUTION: A terminal device performing radio communication with another terminal device through a radio base station device, comprises: an imaging part which images N (N is an integer equal to or greater than 2) pictures including a first and second objects, and outputs the imaged N pictures as N picture frames; and a control part which selects N' picture frames (N' is an integer satisfying 2≤N'≤N) in descending order of optimum degree for the first object from the N picture frames, and selects a picture frame having the maximum optimum degree for the second object from the selected N' picture frames, where the optimum degree is a value evaluated by a parameter showing how the imaged object looks in the picture.

Description

  The present invention relates to a terminal device, an image photographing method in the terminal device, and an image photographing device.

  Currently, wireless communication systems such as mobile phone systems and wireless local area networks (LANs) are widely used. In such a wireless communication system, not only a mobile phone but also a terminal device such as a multi-function mobile phone (or a smartphone), a terminal device for electronic books, and the like can be used, and various services can be provided. It has become like this.

  Also, terminal devices equipped with cameras are widely used. For example, an image such as a group photo or a landscape photo can be easily taken using a camera of a mobile phone. The captured image can be stored, for example, in a memory inside the terminal device or transmitted to another user as an attached file of an e-mail.

  Cameras provided in terminal devices, so-called digital cameras, etc., have various functions such as autofocus and autoflash so that even users who are inexperienced in camera operation can properly photograph a subject. Yes.

  In addition, digital cameras, etc. have also introduced photography technology that uses subject face detection, such as automatically focusing on the face of a specific subject, correcting the image quality of the face according to the surrounding conditions, etc. Can be done.

  Examples of the technology related to such a digital camera include the following. In other words, based on the distance between the detected faces and the smile level and inclination of each face, the degree of closeness between subjects is represented by a value that should also be referred to as “the closeness”, and the calculated closeness exceeds a predetermined threshold. There is one that starts shooting control in response.

  In addition, when the facial expression level detection unit detects that the smile level value of the subject person is equal to or greater than the smile level threshold value of the subject person stored in the threshold data storage unit, the automatic shooting unit performs automatic shooting. There is also.

  On the other hand, technologies related to terminal devices such as mobile phones include the following. That is, each of a plurality of subjects shown in the group photo image is compared with a subject image stored in the address book memory, and if a matching subject image exists, an e-mail address corresponding to the subject image is sent to the destination And create an email.

JP 2010-16796 A JP 2010-219739 A JP 2005-267146 A

  However, the technique related to the digital camera described above captures a subject whose closeness or smile level value is greater than or equal to a threshold value. Therefore, for example, when an image including a plurality of subjects, such as a group photo, is captured, the captured image may be an optimal image for a specific subject, but may not be an optimal image for surrounding subjects. Alternatively, the captured image may be an optimal image for a specific subject, or may not be an optimal image when the entire subject is included. Here, the optimal image is, for example, an image in which a subject in a certain captured image has a better image quality than other captured images.

  Further, in the above-described technology related to the terminal device, when a plurality of images are taken, the user selects an optimal image for each subject from the plurality of images, and then an e-mail corresponding to each subject is created. Will be sent. In this case, the user selects an optimum image of each subject from a plurality of images, and the user is troublesome.

  SUMMARY OF THE INVENTION Accordingly, an object of the present invention is to provide a terminal device, an image photographing method in the terminal device, and an image photographing device capable of selecting an optimal image not only for a specific subject but also for surrounding subjects. .

  Another object of the present invention is to provide a terminal device, an image photographing method in the terminal device, and an image photographing device that save the trouble of the photographer.

  In one aspect, a terminal device that performs wireless communication with another terminal device via a wireless base station device captures N (N is an integer of 2 or more) images including a first subject and a second subject. An imaging unit that outputs the captured N images as N image frames, and the highest N ′ (N 'Is an integer satisfying 2 ≦ N' ≦ N), and a control unit that selects an image frame having the maximum optimum degree of the second subject from the selected N ′ image frames. The optimum degree is an evaluation value based on a parameter representing a degree of reflection of the photographed subject in the image.

  It is possible to provide a terminal device, an image photographing method in the terminal device, and an image photographing device that can select an optimal image not only for a specific subject but also for surrounding subjects. In addition, it is possible to provide a terminal device, an image photographing method in the terminal device, and an image photographing device that save the trouble of the photographer.

FIG. 1 is a diagram illustrating a configuration example of a terminal device. FIG. 2 is a diagram illustrating a configuration example of the terminal device. FIG. 3 shows an example of a telephone directory database. FIG. 4 is a diagram illustrating a configuration example of the camera unit. FIG. 5 is a flowchart showing an example of the selection operation. FIG. 6 is a flowchart showing an example of the optimum degree calculation operation. FIG. 7 is a flowchart showing an example of an image selection operation for a specific subject. FIG. 8 is a flowchart showing an example of the optimum image selection operation. FIG. 9A to FIG. 9F are diagrams for explaining examples of the selection operation. FIG. 10A is a diagram for explaining an example of edge processing, and FIG. 10B is a diagram illustrating an example of each subject extracted by area division. FIG. 11A to FIG. 11F are diagrams for explaining examples of the selection operation. FIG. 12 is a flowchart showing an example of the transmission operation. FIG. 13 is a diagram illustrating an example of a transmission confirmation screen. FIG. 14 is a diagram illustrating an example of an image selection operation.

  Hereinafter, modes for carrying out the present invention will be described.

[First Embodiment]
First, the first embodiment will be described. FIG. 1 is a diagram illustrating a configuration example of a terminal device 100 according to the first embodiment. The terminal device 100 is, for example, a mobile phone, and can perform wireless communication with another terminal device 100 ′ via the wireless base station device 200. The terminal device 100 may be an image capturing device that captures an image, such as a digital camera.

  The terminal device 100 includes an imaging unit 101 and a control unit 102.

  The imaging unit 101 captures N (N is an integer equal to or greater than 2) images including the first subject and the second subject, and outputs the captured N images as N image frames.

  The control unit 102 selects, from N image frames, the top N ′ (N ′ is an integer satisfying 2 ≦ N ′ ≦ N) image frames from the image frame having the maximum optimality of the first subject. Then, from the selected N ′ image frames, an image frame having the maximum optimum degree of the second subject is selected. The optimum degree is, for example, an evaluation value based on a parameter representing the degree of reflection in a photographed subject image.

  As described above, the image selected by the control unit 102 is also an image frame selected from the top N ′ image frames from the image frame having the maximum optimality of the first subject. Therefore, the selected image is an optimal image for the first subject. In addition, the image selected by the control unit 102 is also an image frame in which the optimum degree of the second subject is the maximum from the selected N ′ image frames. Therefore, the selected image is an optimal image not only for the first subject but also for the second subject.

  Therefore, the terminal device 100 can select an image that is optimal not only for a specific subject but also for surrounding subjects. In addition, the terminal device 100 selects an image from a plurality of image frames, so that it is possible to save time and effort for the photographer to select an optimal image from the plurality of images.

[Second Embodiment]
<Configuration example of terminal device>
Next, a second embodiment will be described. FIG. 2 is a diagram illustrating a configuration example of the terminal device 100 according to the second embodiment. The terminal device 100 is, for example, a mobile phone or a personal computer, and can perform wireless communication with other terminal devices via a wireless base station device. The terminal device 100 may be a digital camera, for example, and in this case, may be an image photographing device.

  The terminal device 100 includes a display 110, an operation unit 120, a flash memory 130, a RAM (Random Access Memory) 140, a ROM (Read Only Memory) 150, a camera unit 160, a communication unit 170, a CPU (Central Processing Unit) 180, and And a bus 190. The display 110, the operation unit 120, the flash memory 130, the RAM 140, the ROM 150, the camera unit 160, the communication unit 170, and the CPU 180 are connected to each other via a bus 190.

  Note that the imaging unit 101 in the first embodiment corresponds to the camera unit 160, for example. In addition, the control unit 102 in the first embodiment corresponds to, for example, the flash memory 130, the RAM 140, the ROM 150, and the CPU 180.

  The display 110 is configured by a thin display panel such as an LCD (Liquid Crystal Display). For example, a touch sensor may be superimposed on the surface to be used as a touch panel. The display 110 can display an image captured by the camera unit 160, an image stored in the flash memory 130, or a menu screen. The display on the display 110 is performed, for example, when the CPU 180 reads out image data from the flash memory 130 and outputs the image data to the display 110 according to an operation on the operation unit 120.

  The operation unit 120 includes, for example, a plurality of operation keys, and by operating the corresponding operation keys, it is possible to perform input of characters, creation of mail, instructions for transmission, and the like. The operation unit 120 includes, for example, a shutter key for taking an image.

  The flash memory 130 is, for example, a rewritable semiconductor memory in which stored data does not disappear even when the terminal device 100 is turned off. The flash memory 130 stores an image database 131 and a telephone directory database 132.

  The image database 131 stores images taken by the camera unit 160 as image data. For example, the image data is stored in the image database 131 as image data in the RGB color space, and RGB pixel values (or gradation values) are stored for each pixel (or pixel). In addition to the image data in the RGB color space, the image database 131 may store, for example, image data in a YUV (or YCbCr) color space. In this case, for example, the camera unit 160 may perform conversion processing for converting image data in the RGB color space into image data in the YUV (or YCbCr) color space, and the converted data may be stored in the image database 131. .

  The phone book database 132 stores information related to the phone book in the terminal device 100. FIG. 3 is a diagram illustrating an example of the telephone directory database 132. The phone book database 132 has, for example, one or a plurality of entries, and each entry has an ID (or identification number), name, reading, telephone number, email address, personal information, A memo and a face photo are stored as information about the phone book. The face photograph is, for example, a face photograph of a person matching the ID, name, phone number, mail address, personal information, and memo in the telephone book database 132, and the face image data of the person is the phone book database 132. Is remembered. For example, when the information about the phone book is registered in the terminal device 100, image data of a person's face taken by the camera unit 160 is stored in the phone book database 132 as a face photo.

  Returning to FIG. 2, the RAM 140 is a readable / writable storage device, and is used as a working memory when the CPU 180 reads out a program from the ROM 150 and executes various processes, for example. The RAM 140 can appropriately store data when the CPU 180 performs processing or after the processing ends.

  The ROM 150 is a readable storage device, and stores, for example, programs for executing various processes. The CPU 180 can read a program from the ROM 150 and execute various processes in response to an operation by the operation unit 120.

  The camera unit 160 can capture an image and output the captured image to the display 110 or the flash memory 130 as image data. The camera unit 160 captures a plurality of images including a plurality of subjects (for example, N images for a specified number of times, N is an integer of 2 or more) by one operation of the operation unit 120 (for example, pressing a shutter key). Can do. The camera unit 160 can output the image data to the display 110 or the flash memory 130 for each image frame by outputting the captured N images as N image frames.

  FIG. 4 is a diagram illustrating a configuration example of the camera unit 160. The camera unit 160 includes a lens 161, an image sensor 162, an A / D conversion unit 163, a DSP (Digital Signal Processor) 164, a brightness sensor 165, and an acceleration sensor 166.

  The imaging element 162 is a CCD (Charge Coupled Device), a CMOS (Complementary Metal Oxide Semiconductor), or the like, and converts image light input via the lens 161 into an electrical signal. The electrical signal is output as image data.

  The A / D conversion unit 163 converts the image data output from the image sensor 162 from an analog signal to a digital signal.

  The DSP 164 can output the image data output from the A / D conversion unit 163 to the display 110, the flash memory 130, or the CPU 180 via the bus 190 in response to an instruction from the CPU 180. The DSP 164 can also output the image data output from the A / D converter 163 to the brightness sensor 165.

The brightness sensor 165 can detect the brightness of the captured image. For example, the RGB color space image data output from the DSP 164 is converted into an HUV color space, and the converted V (Value: brightness) is converted. Can be detected as the brightness of the image. For example, when the pixel value (or image data) of a certain pixel in the RGB color space is (R, G, B), the brightness sensor 165 is
V = MAX (R, G, B) (1)
This equation (1) is calculated for all the pixels of the image, and the highest V can be output to the DSP 164 as “brightness”.

Alternatively, the brightness sensor 165 may convert image data in the RGB color space into a YUV color space (or YCbCr color space), and detect the converted Y (luminance) as brightness. For example, when the pixel value of a certain pixel is (R, G, B), the brightness sensor 165 is
Y = 0.2999R + 0.587G + 0.114B (2)
May be calculated for all the pixels of the image, and the highest Y may be output as “brightness” to the DSP 164.

  The acceleration sensor 166 can detect the movement (or acceleration or shake) of the terminal device 100 in the left-right direction or the up-down direction, and can output it to the DSP 164. Examples of the acceleration sensor 166 include a piezoresistive method and a capacitance method. The piezoresistive acceleration sensor 166 can detect the acceleration in the triaxial direction by detecting, for example, a change in the position of a diaphragm formed by thinning the surface of a silicon semiconductor in an annular shape with a piezoresistive element. The capacitance type acceleration sensor 166 can detect acceleration in three axial directions by forming a capacitance with a movable electrode and a fixed electrode integrated with a weight and detecting a change in capacitance value, for example. it can. The acceleration sensor 166 can detect “blurring” when an image is captured by appropriately outputting the detected acceleration to the DSP 164.

  Returning to FIG. 2, the communication unit 170 can perform wireless communication with another terminal device via a wireless base station or the like. For example, the communication unit 170 can perform frequency conversion (up-conversion) on a baseband signal that has been subjected to error correction coding processing, modulation processing, and the like by the CPU 180 and output the result as a radio signal. Further, the communication unit 170 can also convert (down-convert) the radio signal received from the radio base station into a baseband signal and output it to the CPU 180. In this case, the CPU 180 can extract image data before being converted into a radio signal by performing demodulation processing, error correction decoding processing, or the like on the frequency-converted baseband signal. In order to perform frequency conversion, the communication unit 170 includes, for example, a transmission antenna, a reception antenna, an A / D conversion circuit, a D / A conversion circuit, a frequency converter, a band pass filter (BPF), and the like.

  The CPU 180 can perform various processes by appropriately reading out a program stored in the ROM 150 and executing the program. The CPU 180 is, for example, a program for performing image recognition processing, a program for performing photographing quality inspection processing, a program for performing destination search processing, and a program for performing transmission processing for transmitting a selected image. Can be read from the ROM 150 and each process can be executed. For example, the CPU 180 can realize the image recognition function 181 by performing image recognition processing, and can realize the shooting quality inspection function 182 by performing shooting quality inspection processing. Further, for example, the CPU 180 can realize the destination search function 183 by performing destination search processing, and can realize the transmission function 184 by performing transmission processing.

  The CPU 180 uses the image recognition function 181 to search the face photo image data stored in the phone book database 132 for an image that matches or resembles an image stored in the image database 131, and matches which phone book information. Can be determined. Further, the CPU 180 can select an optimum image for an arbitrary subject from the images stored in the image database 131 by the photographing quality inspection function 182, for example. Further, the CPU 180 can search, for example, the destination of the image stored in the image database 131 from the telephone directory database 132 by the destination search function 183. Further, the CPU 180 can transmit the image data to the transmission destination searched from the telephone directory database 132 by the transmission function 184, for example.

  Note that the CPU 180 or the DSP unit 164 performs, for example, defective pixel correction processing, preprocessing such as digital clamping and digital gain control, and image quality correction processing such as sharpness and white balance on the image data of the captured image. be able to. The CPU 180 or the DSP unit 164 can convert the image data into gradations suitable for output to the display 110, storage in the flash memory 130, and the like through such conversion processing.

<Operation Example of Terminal Device 100>
Next, an operation example of the terminal device 100 will be described. The operation of the terminal device 100 includes two operations, for example, an image selection operation and a transmission operation for the selected image. 5 to 11F illustrate an image selection operation of the terminal device 100, and FIGS. 12 and 13 illustrate an example of a transmission operation. First, an example of the image selection operation will be described, and then an example of the transmission operation of the selected image will be described.

<1. Image selection operation>
FIG. 5 is a flowchart showing an example of an image selection operation. FIG. 9A to FIG. 9F are diagrams for explaining an example of the image selection operation, and this operation example will be described with reference to these drawings as appropriate. FIG. 5 is also an example of an operation executed by the CPU 180 of the terminal device 100, for example. For example, in FIG. 5, the processing of S15 is an operation performed by the CPU 180 executing the image recognition program, and the other processing is an operation performed by the CPU 180 executing the photographing quality inspection program.

  The CPU 180 starts processing by appropriately reading out an image recognition program stored in the ROM 150, for example (S10).

  Next, the CPU 180 starts photographing the subject (S11). For example, when the shutter key is pressed, the CPU 180 receives an operation signal instructing to take a picture from the operation unit 120 and instructs the camera unit 160 to take a picture (or take an image). The DSP unit 164 of the camera unit 160 receives this instruction, operates the lens 161, the image sensor 162, and the A / D conversion unit 163 to start photographing. The DSP unit 164 can output image data of an image captured by the imaging element 162 via the A / D conversion unit 163 to the image database 131 or the display 110 based on an instruction from the CPU 180.

  In the second embodiment, when a shutter key is pressed once, a plurality of images (the prescribed number N: N is an integer equal to or greater than 2) (or N image frames, hereinafter referred to as second image frames). In the following embodiments, “image frame” may be referred to as “image” as appropriate) is taken. For example, the DSP unit 164 can continuously capture a prescribed number N of images at regular intervals, and can store the image data of the captured N images in the image database 131, respectively.

  FIG. 9A shows an example of an image when a prescribed number N of images are taken. In the case of only one photograph (or image), for example, the subject A may appear unsharp but the subject B may appear blurry. However, there is a higher possibility that the subject A and the subject B are not blurred when shooting a plurality of photos than when shooting a single photo. Therefore, in the second embodiment, a plurality of (N) images are taken.

  In the second embodiment, it is assumed that a plurality of subjects are captured in each of the N captured images. In the example of FIG. 9A, nine subjects, subject A to subject I, are shown in one photo as a group photo. The subject may be not only a person but also a person other than a person such as a building or a vehicle. Examples thereof are shown in FIGS. 11A to 11F, and details thereof will be described later.

  Returning to FIG. 5, the CPU 180 then sets “1” to the number-of-sheets loop counter i (S12). The number loop counter i is, for example, a counter for counting the number of captured images. In the second embodiment, since N images are captured by pressing the shutter once, the number loop counter i can transition from “1” to “N”. For example, the CPU 180 stores “1” as the number-of-sheets loop counter i in the RAM 140 and performs the following processing on the “first” image.

  Next, the CPU 180 stores the photographed image in the image database 131 (S13). For example, the CPU 180 instructs the DSP unit 164 of the camera unit 160 to store the captured N images in the image database 131, and the DSP unit 164 receives the instruction and receives the image data of the N images. Is stored in the image database 131. Note that the processing of S12 and S13 may be reversed.

  Next, the CPU 180 divides the “1” -th image using the pixel value of the pixel (S14). The CPU 180 can extract a subject shown in each image, for example, by performing region division. For example, the CPU 180 reads out the “first” image from the image database 131, and uses the image data (for example, the pixel value (or gradation value) of each pixel) in the “first” image to perform first-order differentiation processing. The region is divided by extracting the edge by using (1).

  FIG. 10A is a diagram for explaining edge processing. For example, the CPU 180 performs the following processing for all pixels of the “1” -th image data. That is, a difference Δx between pixel values of a pixel p1 and an adjacent pixel px1 adjacent to the pixel p1 in the X-axis direction (or raster scan direction) is calculated. Further, the CPU 180 calculates a difference Δy between pixel values of adjacent pixels py1 adjacent to the pixel p1 in the Y-axis direction (or a direction orthogonal to the raster scan direction on the same plane). Then, the CPU 180

Is calculated. The CPU 180 compares the calculated d with the edge threshold dth. For example, when the calculated d exceeds the edge threshold dth, the pixel p1 is determined as the edge of the image, and when the calculated d is equal to or less than the edge threshold dth, It is determined that the pixel p1 is not an edge. In the subject, the pixel value between adjacent pixels in the edge portion changes more greatly than the pixel value between adjacent pixels in a portion other than the edge. Therefore, when the difference between the pixel values of adjacent pixels is squared in the X-axis direction and the Y-axis direction and the value obtained by adding these values is larger than the edge threshold value dth, the pixel p1 is determined as a pixel located in the edge portion. can do.

  Then, for example, the CPU 180 sequentially proceeds with processing for each pixel in the X-axis direction, and stores a pixel value up to the pixel in the image database 131 if there is a pixel determined to be an edge of the image again. If the process proceeds sequentially in the X-axis direction and there is no pixel that is determined again as the edge of the image, the pixel values are stored in the image database 131 up to the last pixel in the X-axis direction. When the processing proceeds to the end pixel in the X-axis direction, one pixel is then advanced in the Y-axis direction, and the edge processing is sequentially advanced in the X-axis direction. The image data stored in the image database 131 in this way is stored as image data in the edge region of the image, and each subject is divided into regions from the captured image.

  FIG. 10B is a diagram illustrating an example of a subject that is divided into regions by the CPU 180. For example, the CPU 180 extracts the edge of the “1” image and divides the region to extract the subjects A to I shown in the “1” image by dividing the region. Can do.

  The edge processing described above is an example. For example, the pixel values of each pixel are weighted by a matrix operation on nine pixels adjacent in the X-axis direction and the Y-axis direction, and each pixel value is compared with a threshold value. The edge may be extracted with

  Returning to FIG. 5, the CPU 180 then detects the presence or absence of a subject by pattern matching processing for each divided area (S15). For example, the CPU 180 detects whether each of the divided objects is similar or dissimilar to the face photograph stored in the phone book database 132. Thereby, for example, it is confirmed whether or not a person stored in the phone book database 132 is reflected in the photographed image. If the person is reflected in the photographed image, for example, the mail address stored in the telephone book database 132 is addressed. It is possible to send an image taken to the camera.

  The pattern matching process is performed as follows, for example. That is, the CPU 180 compares the pixel value of each pixel of the divided image with the pixel value of each pixel of the image in the face photograph stored in the phone book database 132. The CPU 180 compares the two pixel values with respect to all pixels of the divided image (or all pixels of the image in the face photograph). Then, the CPU 180 can determine whether the two pixel values match or the number of pixels whose difference is within a certain range is within the pattern matching threshold range.

  For example, the CPU 180 may determine that the subject of the captured image matches or is similar to the subject stored in the phone book database 132 when the number of pixels that match or are within a certain range is within the pattern matching threshold range. it can. On the other hand, the CPU 180 can determine that the subject of the captured image is dissimilar to the subject stored in the phone book database 132 when the number of pixels that match or in a certain range exceeds the range of the pattern matching threshold.

  In order to determine whether or not the coincidence or difference is within a certain range, the pixel position of the image divided into the region and the pixel position of the image stored in the phone book database 132 are, for example, It is desirable to have the same face position. For example, the CPU 180 sequentially stores the image data stored in the telephone directory database 132 in the X-axis direction from the top of the head. Further, the CPU 180 also stores the image data sequentially from the upper part of the subject's head in the X-axis direction for the subject extracted by area division.

  The pattern matching process described above is an example. For example, a difference in luminance value (Y) is calculated for two pixels, that is, a face photograph pixel stored in the phone book database 132 and a region image pixel. It is also possible to determine whether the square is within a threshold value. Further, the pattern matching process may be performed by the cross-correlation normalized for the two pixels.

  Returning to FIG. 4, the CPU 180 then adds “1” to the number-of-sheets loop counter i (S 16). For example, the CPU 180 can store a value obtained by adding one loop counter i in the RAM 140.

  Next, the CPU 180 determines whether or not the added loop counter i exceeds the specified number N (S17). For example, the CPU 180 determines by reading and comparing the loop counter i stored in the RAM 140 and the specified number N.

  When the added loop counter i does not exceed the specified number N (Yes in S17), the CPU 180 performs the processing from S13 to S16 on the image corresponding to the value of the loop counter i. For example, the CPU 180 performs region division on the “2” -th image (S14), and performs pattern matching processing (S15) for each region. Next, the CPU 180 performs region division on the “third” image, and performs pattern matching processing on each region. The CPU 180 sequentially repeats this, and performs the processes of S13 to S16 on each of the images up to the prescribed number N, thereby dividing the area of each of the N images.

  When the CPU 180 extracts the subject in the “j” -th area of the “i” -th image by area division (S14) (S14), for example, in the image database 131, “i” and “j”. And can be stored in a parameter area. As a result, the CPU 180, the DSP 164, and the like access image data in the “j” th “j” area subject by accessing an area in which “i” is “j” as a parameter in the image database 131. Can be read out.

  On the other hand, when the added loop counter i becomes equal to or greater than the prescribed number N (No in S17), the CPU 180 ends the region division process for up to the prescribed number N of images.

  Next, the CPU 180 calculates an optimum degree for an area in which the subject is captured among the divided areas (S18). The optimum degree will be described later.

  FIG. 6 is a flowchart showing an example of a subject optimum degree calculation operation, which is a detailed operation example of the processing of S18 in FIG. In this process, for example, the optimum degree of the subject is calculated in each of the prescribed number N of images. In the example of FIG. 9A, the optimality of the subjects A to I is calculated for the “first” image, and this is performed for all the prescribed number N of images. For example, the processing is as follows.

  When the CPU 180 shifts to the process of S18 and starts the present process (S180), the CPU 180 sets “1” to the loop counter i for number of sheets (S181). The CPU 180 calculates the optimum degree for each photographed image.

  Next, the CPU 180 sets “1” in the area loop counter j (S182). The area loop counter j is, for example, a counter for counting the number of divided areas (or the number of extracted subjects) for each captured image.

  Next, the CPU 180 calculates the optimality of the subject in the “1” -th area of the “1” -th image (S183). In the second embodiment, the optimum degree is calculated for each divided region in each image.

  Here, the optimum degree is a numerical value (for example, a value obtained by an evaluation function using each index that affects the image appearance (or appearance) such as “brightness”, “contrast”, “blurring state” as a parameter ( (Or evaluation value).

  Among the parameters of the evaluation function, “brightness” is detected by the brightness sensor 165 of the camera unit 160, for example, and is quantified as brightness V, brightness Y, and the like. For example, the highest brightness V or luminance Y value among the subjects in the region j can be set as “brightness”. For example, the CPU 180 instructs the DSP 164 of the camera unit 160 to detect “brightness” in the “j” -th region of the “i” -th image. Then, the DSP 164 reads out each pixel value (or image data) in the “j” -th region of the “i” -th image from the image database 131, and outputs it to the brightness sensor 165. May be detected.

  Alternatively, instead of the brightness sensor 165, the CPU 180 may detect “brightness”. In this case, for example, the CPU 180 may perform the same processing as the brightness sensor 165 described above to detect “brightness”.

Further, among the parameters of the evaluation function, the “contrast” can be, for example, a ratio of the highest luminance (Y _MAX ) to the lowest luminance (Y _MIN ) in the image. The ratio of the highest luminance to the lowest luminance can be obtained in a certain area. For example, the CPU 180 reads all pixel values of the region j from the image database 131, calculates the expression (2), and calculates the ratio (Y _MIN / Y _MAX ) between the highest luminance Y _MAX and the lowest luminance Y _MIN. By doing so, the contrast of the subject in the region j can be calculated.

  Furthermore, the “blurring state” among the parameters of the evaluation function can be, for example, the acceleration detected by the acceleration sensor 166 when each of N images is taken. The DSP 164 of the camera unit 160 can store the acceleration of each image detected by the acceleration sensor 166 in the flash memory 130, and the CPU 180 reads the acceleration of each image stored in a predetermined area of the flash memory 130. Can be evaluated.

As the evaluation function, for example, any one of the detected “brightness”, “contrast”, and “blurring state” can be used as the output of the evaluation function (= optimum degree) as it is, or these parameters are combined. A thing can also be used as an output (= optimum) of an evaluation function. In the latter case, as the evaluation function, for example,
Evaluation function = a × “brightness” + b × “contrast” + c × “blurred state” (4)
And so on. In this case, a, b, and c can be predetermined coefficient values. For example, the CPU 180 can calculate the optimum degree of the subject by calculating Expression (4).

  Next, the CPU 180 adds “1” to the area loop counter j (S184), and determines whether or not the added value of the area loop counter j exceeds M (S185). For example, M is the number of regions (or the number of subjects) divided in one image.

  When the area loop counter j does not exceed M (Yes in S185), the CPU 180 proceeds to the process of S183 again and repeats the above-described process. For example, in the example of FIG. 9A, when the CPU 180 calculates the optimum degree of the “1” -th area (for example, the subject A) of the “1” -th image, “2” of the “1” -th image. The optimality is calculated for the “th region (for example, subject B). Further, the CPU 180 calculates the optimum degree for all the divided areas (the number of M areas) in the “1” -th image such as the “3” -th area.

  On the other hand, when the added area loop counter j exceeds M (No in S185), the CPU 180 adds “1” to the number counter i (S186). For example, in the example of FIG. 9A, when the CPU 180 calculates the optimum degree for the subject I (for example, the “9th” area) of the “first” image, the area loop counter exceeds M, The counter i is “2”.

  Next, the CPU 180 determines whether or not the added number-of-sheets loop counter i exceeds the specified number N (S187). When the added loop counter i for the number of sheets does not exceed the specified number N (Yes in S187), the CPU 180 proceeds to the process of S182 and repeats the process described above. In the example of FIG. 9A, the CPU 180 proceeds to the processing of S182, assuming that the added number counter “2” does not exceed the specified number N (for example, 10), and the “2” th sheet Process the image. Then, the CPU 180 calculates the optimum degree for all subjects of the “second” image (No in S183 to S185), adds the number counter i (S186), and performs processing for the image for the specified number N.

  On the other hand, when the added number counter i is equal to or greater than the prescribed number N (No in S187), the CPU 180 calculates the optimum degree for all the subjects of the prescribed number N of images, so Is terminated (S188).

  Returning to FIG. 4, the CPU 180 then selects a plurality of uppermost images (for example, N ′, where N ′ is an integer satisfying 2 ≦ N ′ ≦ N) from the image with the highest degree of optimality in the region where the specific subject is captured. An image (or N ′ image frames) is selected (S19).

  FIG. 7 is a flowchart showing an example of an image selection operation for a specific subject. For example, a detailed operation example in the process of S19 of FIG. 5 is shown. For example, in the example of FIG. 9A, the top three images (for example, image 1-1 to image 1-3) are selected in order from the image with the highest degree of optimality for the specific subject A. For example, the processing is as follows.

  When the CPU 180 shifts to the processing of S19 and starts this processing (S190), it sets “1” to the number counter i (S191), and then sets “1” to the area counter j (S192). . For example, in the example of FIG. 9A, the subsequent processing is performed for the “1” th region (for example, the subject A) of the “1” th image.

  Next, the CPU 180 detects whether or not the optimum degree of the area including the specific subject in the image including the specific subject is higher than that detected so far (S193).

  When the optimality of the region including the specific subject in the image including the specific subject is higher than that detected so far (Yes in S193), the CPU 180 may use, for example, the optimality as the maximum optimality data of the specific subject. Can be stored in a predetermined area of the image database 131 (S194).

  Next, the CPU 180 adds “1” to the area loop counter j (S195), and determines whether or not the added area loop counter j exceeds the number M of divided areas (S196). (Yes in S196) shifts to the process of S193 and repeats the above process.

  The loop from S193 to S193 via SYes and again to S193 will be described below with reference to FIG. 9A as an example. For example, when the “1” -th area of the “1” -th image is a specific subject, the CPU 180 determines that the optimality of the “1” -th area detected so far is “ It is detected whether it is higher than the optimality of the “1” th area (S193). In this case, the CPU 180 stores the “first” image in a predetermined area of the image database 131 because there is no optimality detected so far (Yes in S193, S194).

  Then, the CPU 180 adds “1” to the area loop counter j (S195), the area loop counter j becomes “2”, and the “2” -th area (for example, subject B) of the “1” -th image. To be processed. Since the area loop counter j is “2”, the number of divided areas does not exceed “9” (Yes in S196), and the CPU 180 performs the process of S193 again.

  Since the “2” -th area is not a specific subject (No in S193), the CPU 180 adds “1” to the area loop counter j without performing the image saving process (S194) (S195). Note that the CPU 180 can determine, for example, whether the subject is a specific subject based on the number in the divided area. When the “1” -th area (subject A) is a specific subject, the CPU 180 stores an area number “1” that becomes a specific subject in a predetermined area of the RAM 140, for example, and this number and the area loop counter j Can be detected as to whether or not the subject is a specific subject.

  Then, the area loop counter j becomes “3” (S195), and since the “3” -th area (for example, the subject C) is the processing target but is not a specific subject, the CPU 180 again sets the area loop counter j to “ 1 ”is added. Thereafter, processing is performed for all the divided areas (all subjects) of the “first” image.

  Next, “1” is added to the area loop counter j (S195), and when the added area loop counter j exceeds the area division number M (No in S196), the CPU 180 adds “1” to the number counter i. (S197). By adding the counter i for the number of sheets, the next image becomes a processing target.

  It is determined whether or not the number M of divided areas in one image (or image frame) is not exceeded (S196). If not (Yes in S196), the process proceeds to S193 and the above-described process is repeated. For example, the processing in the loop from S193 to S196 for the “2” th image is as follows. That is, since the “1” -th region (subject A) of the “2” -th image is a specific subject, the CPU 180 determines that the optimality of the region is the “1” -th region of the “1” -th image. It is detected whether the degree of optimization is higher than (S193). When the optimality of the subject A is higher in the “2” image than in the “1” image, the CPU 180 deletes the “1” image stored in the predetermined area of the image database 131. The “2” th image is overwritten (S194).

  Then, the CPU 180 adds “1” to the area counter j (S197), determines whether or not the specified number N is not exceeded (S198), and when it does not exceed the specified number N (Yes in S198), proceeds to S193. Then, the above process is repeated. In the example of FIG. 9B, the processing is performed on the “2” image from the “1” image.

  In the processes of S193 and S194, the optimality between the images is compared for a specific subject, and the image with the highest optimality is stored in the image database 131. In this example, the top N ′ images are selected in order from the highest degree of optimality, so that the images for the plurality of images are stored in a predetermined area of the image database 131. be able to. For example, when N ′ = 3 images are selected, for example, the CPU 180 stores “1” to “3” images in a predetermined area of the image database 131. Then, when the specific subject in the “4th” image is the comparison target, the CPU 180 determines that the optimality of the specific subject among the “1” to “3” images is “ The optimum degree of the specific subject in the 4th image is compared (S193). When the optimum degree of the specific subject in the “4th” sheet is higher than the lowest optimum degree among the “1” to “3” pieces, the CPU 180 selects the image having the lowest optimum degree of the specific subject as the image database 131. And the “4th” image is overwritten in a predetermined area of the image database. On the other hand, when the optimum degree of the specific subject in the “4th” is lower than that in the “1” to “3” images, the CPU 180 has “4” pieces. Avoid saving eye images.

  Then, the CPU 180 stores the image with the lowest degree of optimization among the three images as a comparison target in a predetermined area of the RAM 140, and repeats the process for the prescribed number N of images. Thus, for example, the top N ′ images can be stored in the image database 131 in order from the highest degree of optimization of a specific subject. For example, in the example of FIG. 9A, three images 1-1 to 1-3 are selected and stored in the image database 131.

  When the added number counter i is equal to or greater than the specified number N (No in S198), the CPU 180 ends the selection process for a plurality of sheets (S199).

  Returning to FIG. 4, when the selection process of a plurality of sheets (S19 or S190 to S199) is completed, the CPU 180 selects an image that maximizes the optimum degree of optimization for other subjects from the selected plurality of images. (S20). For example, in the example of FIG. 9C, the optimum degree of the other subjects B to I is compared with the top three images (images 1-1 to 1-3) having the highest degree of optimum for the specific subject A. The sum is calculated. Then, the image (image 1-3) having the highest total value is selected (for example, FIG. 9D to FIG. 9F). The image selected in this way becomes an optimal image, for example.

  The optimum image is, for example, an image having the maximum degree of optimum for a certain subject (a specific subject A in the example of FIG. 9C) in the captured image (for example, image 1-1 to image 1 in FIG. 9C). 3) is an image (for example, image 1-3) having the maximum sum of the optimum degrees of other subjects (for example, subjects B to I). Or an optimal image is an image with a good image | photographing condition of the to-be-photographed object in the captured image compared with another captured image, for example.

  FIG. 8 is a flowchart showing an example of an optimum image selection operation in which such an optimum image is selected. For example, when the CPU 180 proceeds to the process of S20 in FIG. 5, the processes from S200 to S210 shown in FIG. Do. For example, the processing is as follows.

  When the CPU 180 starts this processing (S200), it sets “1” to the loop counter i for number of sheets (S201), and sets “1” to the loop counter for region j (S202).

  Next, the CPU 180 calculates the optimum degree of each subject other than the specific subject (S203). For example, the CPU 180 sets the “1” -th area (subject) of the “1” -th image as a processing target, but does not calculate the optimum degree because the subject is a specific subject. For example, the CPU 180 calculates the optimum degree after the “2” -th region (for example, the subject B).

  Next, the CPU 180 adds “1” to the area loop counter j (S204), and detects whether or not the added area loop counter j exceeds the number M of divided areas (S205). When the added area loop counter j does not exceed M (Yes in S205), the CPU 180 proceeds to S203 and repeats the above-described processing.

  For example, the CPU 180 calculates the optimum degree for the “2” -th region (for example, the subject B) of the “1” -th image in S203. In this case, since the optimum degree of each area is calculated (S183 in FIG. 6), for example, the CPU 180 reads the optimum degree of the “2” area of the “first” image from the RAM 140. You may make it process by. Then, the CPU 180 sequentially increments the area counter j by “1”, from the “2” -th area (for example, the subject B) to the “9” -th area (for example, the subject I) of the “1” -th image. Is calculated or read from the RAM 140.

  When the added area loop counter j is equal to or greater than the number M of divided areas (No in S205), the CPU 180 calculates and stores the total optimum degree of the subject in the image (S206). For example, the CPU 180 calculates the total value of the optimum degrees of the subjects B to I shown in the “first” image, and stores them in the RAM 140.

  When there is one other subject other than the specific subject in the photographed image, the optimum degree of the other subject is stored instead of the total value.

  Next, the CPU 180 adds “1” to the number counter i (S207), and determines whether or not the added number counter i does not exceed the number N ′ (S208). In the example of FIG. 9B, N ′ is “3”, for example.

  When the added number counter i does not exceed the number N ′ (Yes in S208), the process proceeds to S202, and the above-described process is repeated for the “i” th image. For example, the CPU 180 calculates the total optimum degree for subjects other than the specific subject for the “second” image. The CPU 180 calculates the total optimum degree of subjects other than the specific subject for each of the selected number N ′ of images.

  On the other hand, when the added number counter i becomes equal to or greater than the number N ′ (No in S208), the CPU 180 calculates the total optimum degree of subjects other than the specific subject from the N ′ images. The image with the highest is selected (S209). In the example of FIG. 9C, the CPU 180 selects an image 1-3 that maximizes the sum of the optimum degrees of subjects B to I for the three images 1-1 to 1-3.

  A plurality of images (for example, N ′ images) are selected in order from the highest degree of optimization for a specific subject by a plurality of image selection processing (S19 or S190 to S199). For example, for a specific subject among a plurality of subjects, a plurality of higher-order images are selected in order from the best image appearance (or appearance).

  Furthermore, in the second embodiment, an image is selected in consideration of not only the specific subject but also the appearance (or appearance) of the image of another subject other than the specific subject. Therefore, the terminal device 100 calculates the total value of the optimality of other subjects for each of a plurality of images selected in order from the highest optimality for a specific subject (S206), and the total value is The largest image is selected (S209). As a result, the image selected in S209 considers not only the specific subject but also the appearance (or appearance) of the image of the other subject other than the specific subject. The most suitable image is selected. In addition, since the selected image is selected from the N photographed images, the user does not miss a photo opportunity and re-takes the image as compared with the case where the photographed image is one, thereby saving the user's trouble. it can.

  For example, in the case of a group photo or the like, not only a specific subject but also the appearance (or appearance) of an image of a surrounding subject may be considered, and the overall appearance may be considered. Then, there may be a case where a photograph with a good appearance as a whole is selected from a plurality of photographs, and the terminal device 100 according to the second embodiment considers such a situation and only a specific subject. In addition, it is possible to select an optimal photograph for the surrounding subject.

  For example, in the example of FIGS. 9A to 9F, three images (image 1-1 to image 1-3) are selected from the one having the highest degree of optimization for a specific subject A. Among these, for the image 1-1, the subject F and the subject I have “blurring”, so the optimality is lower than that of other subjects. In addition, in the image 1-1, the subject C is a shadow, and “brightness” is lower than that of the other subjects, so that the optimum degree is also lower than that of the other subjects. Therefore, for the image 1-1, although the appearance (or appearance) of the subject A is better than the other images, the total optimum degree of the other subjects is lower than those of the images 1-2 and 1-3. Become. Therefore, the appearance (or appearance) of the image 1-1 including other subjects is not better than that of the images 1-2 and 1-3.

  In addition, regarding the image 1-2, the subject I has “blurring”, the subject C has a shadow, and the total optimum degree of other subjects is lower than that of the image 1-3.

  On the other hand, compared with other images, the image 1-3 does not have “blurring” or “shadow” with respect to other subjects, and the total optimum degree is higher than those of the other images 1-1 and 1-2. Therefore, when not only the specific subject A but also the appearance (or appearance) of other subjects B to I is considered, the image 1-3 is selected.

  In the example of FIGS. 9A to 9F, the specific subject is described as the subject A. However, the subject shown in the photographed image such as the subject B and the subject C by the loop of S193 to S196. Are sequentially selected as specific subjects, and an optimal image in each is selected. Accordingly, the image 1-3 is selected in the examples of FIGS. 9A to 9F, but when the specific subject is the subject B, another image (for example, the image 1-8) is selected. In some cases. When the specific subject is the subject C, another image (for example, the image 1-6) may be selected. Of course, for the specific subjects B and C, the same image 1-3 as that for the subject A may be selected. In the second embodiment, the same or different images are selected for each subject in the image.

  For example, when the subject other than the specific subject A is only the subject B, the CPU 180 selects the image with the maximum degree of optimum for the subject B from the plurality of images selected for the specific subject A. (S209). If there are a plurality of other subjects, the image having the maximum total value is selected.

  Further, the specific subject may be, for example, a subject stored as a face photograph in the phone book database 132. In this case, for example, since processing is performed on subjects stored as face photographs in the phone book database 132 and processing is not performed on all subjects included in the photographed image, processing is performed on all subjects. The processing speed can be increased as compared with.

<2. Other image selection operation examples>
Next, as an example of operation, an example in which a subject other than a human is shown in a captured image will be described. FIG. 11A to FIG. 11F are diagrams for explaining an operation example. The processing is the same regardless of whether the subject of the captured image is a human or a non-human subject. 11A to 111F, the subject J represents a human, the subject K represents a building, a tree, and a house, and the subject L represents a vehicle.

  Even in this example, the CPU 180 captures a prescribed number N of images by pressing the shutter once (for example, FIG. 11A). Then, the CPU 180 extracts subject J to subject L by area division for each image (for example, S14 in FIG. 5), and calculates the optimum degree for the subject J to subject L extracted for each image (for example, FIG. 5). S18 or S183 in FIG. 6).

  Next, the CPU 180 selects a plurality of pieces (for example, three pieces) in order from the highest degree of optimality for the specific subject J (for example, FIG. 11B to FIG. 11C, S19 in FIG. 5 or FIG. 7). S194).

  Next, the CPU 180 calculates the total value of the optimum degrees of the other subjects K and L for the selected plurality of images, and selects the image having the maximum total value, for example, the image 2-3 (for example, FIG. 11 ( D) to FIG. 11 (E), S20 in FIG. 5 or S209 in FIG.

  In the example of FIG. 11C, since the subject L in the image 2-1 has “blurring”, the sum of the optimum degrees of the subject K and the subject L is lower than that of the other images. In the image 2-2, since the subject K is a shadow, the “brightness” is lower than that of the other images, and therefore the total optimum degree of the subject K and the subject L is also lower than the other. Therefore, the total optimality of the subject K and the subject L is highest in the image 2-3, and the image 2-3 is selected.

  For example, even when the specific subject is the subject K, a plurality of images are selected from the N images in order from the highest degree of optimality of the subject K, and other images are selected from the selected plurality of images. An image having the highest total value of the optimum degrees of the subjects J and L is selected. The selected image may be the same image as when the specific subject is the subject J, or may be a different image.

  Even if the subject is other than a human (or even if the subject includes something other than a human), a plurality of images are selected in order from the highest degree of optimality of the specific subject, and from among the plurality of images, The image with the highest sum of the optimum degrees of other subjects is selected. Therefore, the last selected image (for example, S209) is an image that is optimal not only for a specific subject but also for surrounding subjects among a plurality of captured images.

<3. Sending operation after selecting images>
After selecting an image, the terminal device 100 according to the second embodiment can transmit the image by e-mail or the like to the subject in the selected image. Hereinafter, the transmission operation after image selection will be described. FIG. 12 is a flowchart showing an example of the transmission operation, and FIG. 13 is a diagram showing an example of a confirmation screen in the transmission operation. The transmission operation is performed as the transmission function 184 by the CPU 180 reading a program for transmission processing from the ROM 150, executing the transmission processing, and executing the processing, for example.

  When starting the transmission operation (S30), the terminal device 100 searches the telephone directory database 132 for the transmission destination of each subject for the selected image (S31). For example, the CPU 180 searches the images stored as face photos in the phone book database 132 for images that match or are similar to a specific subject in the selected image. When the CPU 180 finds an image that matches or resembles a specific subject from the phone book database 132, the CPU 180 reads the email address of the subject corresponding to the image from the phone book database 132. The CPU 180 can transmit the selected image to the mail address. Note that the selected image is, for example, an image selected by the process of S209.

  The search for whether or not the image stored as the face photograph in the telephone book database 132 matches or is similar to the selected image is, for example, a pattern matching process performed when detecting the presence or absence of a subject in the divided area (see FIG. 5). S15).

  The pattern matching process in this process is performed as follows, for example. That is, the CPU 180 reads the selected image stored in the image database 131, and reads the pixel value of a specific subject from the selected image. Further, the CPU 180 reads out pixel values of an image stored as a face photograph in the phone book database 132, for example. Then, the CPU 180 compares the pixel value of the selected image with the pixel value of the image stored as a face photograph, and determines whether or not the number of pixels that match or are within a certain range is greater than or equal to the pattern matching threshold value. You can also

  Next, the CPU 180 determines whether or not the target image is a good image for the subject with respect to the selected image (or target image) for the destination subject (S32).

  In this transmission operation, the selected image is not transmitted as it is to the transmission destination, but the selected image is a selection image that does not look good for the subject (or a selection image that does not have a good image quality, or a selection that does not look good (or looks)). Images, etc.) can be prevented from being sent.

  For example, even when the image with the highest degree of optimality for a specific subject (or a plurality of images from the highest image) is the selected image, the image quality is not good compared with other images, or the image quality is not good In some cases, too.

  Therefore, in this example of the transmission operation, it is further determined whether or not the selected image is an optimum image to be transmitted to the transmission destination by determining the degree of reflection of the subject (S32). The discrimination threshold can be set to a threshold sufficient for the image quality of a captured image, or a threshold sufficient for an image transmitted to a transmission destination, for example. For example, the CPU 180 discriminates an image that is not well reflected when the optimum degree of a specific subject in the selected image is smaller than the determination threshold value, and determines that the image that is good in reflection state when the optimum degree of the specific subject of the selected image is equal to or more than the determination threshold value. Can be determined. In this case, the optimality can be the sum of the optimalities of all subjects in the selected image as well as a specific subject.

  When the CPU 180 determines that the selected image is not a good image for the subject (No in S32), the CPU 180 allows the user to select whether or not to transmit the selected image (S35).

  FIG. 13 is a diagram illustrating an example of a selection screen displayed on the display 110. For example, “Send” or “Cancel” is selected by an operation by the user operation unit 120 or a touch panel operation on the surface of the display 110. The CPU 180 can receive from the operation unit 120 or the display 110 a signal corresponding to the “transmission” or “cancel” selection operation and determine whether or not to transmit the signal accordingly. For example, the CPU 180 may read the selected image from the image database 131 and display it on the display 110 before displaying the screen shown in FIG. 13 on the display 110. For example, the CPU 180 determines that the selected image is transmitted when the “Send” button 111 is operated (Yes in S35), and determines that the selected image is not transmitted when the “Cancel” button 112 is operated (No in S35). can do.

  The selection screen is stored in, for example, the flash memory 130, the RAM 140, or the ROM 150, and can be displayed by being read from the flash memory 130 or the like and output to the display 110 when the CPU 180 performs this processing (S35). it can.

  When transmitting the selected image (Yes in S35), or when the target image is a good image for the subject (Yes in S32), the CPU 180 transmits the selected image to the searched destination (S33). For example, the CPU 180 can create an e-mail addressed to the e-mail address searched in the telephone directory database 132 and transmit the selected image as an attached file of the e-mail. For example, the CPU 180 creates e-mail data to which the selected image is attached and outputs it to the communication unit 170. The communication unit 170 converts the signal into a radio signal by frequency conversion or the like, and transmits it to a transmission destination via a radio base station or the like. Thereby, the selected image can be transmitted to the transmission destination of the subject. Note that the CPU 180 may transmit the selected image as an image file to an e-mail address destination instead of an e-mail.

  On the other hand, when the CPU 180 does not transmit the selected image (No in S35), the CPU 180 ends the transmission operation without transmitting the selected image (S34).

  As described above, in the example of the transmission operation, the selected image is transmitted to the subject (S33), and the photographer of the terminal device 100 does not have to perform an operation of selecting which image is better from the plurality of images. Therefore, it is possible to save the photographer's troubles such as selection work. If the selected image is not an image sufficient for transmission or the image quality is not sufficient, the selected image is not transmitted to the subject (No in S35). For this reason, since an image with poor image quality or the like is not transmitted, the communication cost can be reduced compared to a case where all the selected images are transmitted.

[Other embodiments]
Next, other embodiments will be described. In the second embodiment, an example in which one image is selected as the selected image (S209) has been described. For example, the selected image is not only one image but also a plurality of images (for example, N ″, N ″ is an integer satisfying 2 ≦ N ″ ≦ N ′) (or N ″ images). Frame) may be selected.

  FIG. 14 is a diagram illustrating an example of an image selection operation for selecting a plurality of images. The same reference numerals are given to the same processing portions as those in FIG. 8 in the second embodiment. In addition, the CPU 180 selects a plurality of sheets in order from the one with the highest total degree of optimization of subjects other than the specific subject (S211). In this case, for example, the CPU 180 can cause the display 110 to display the result of tank attachment in order from the image with the highest rank in the processing of S211. For example, the CPU 180 can store the images in the predetermined area in order from the image with the highest rank in the processing of S211 in the image database 131, and output the image to the display 110 in order from the image with the highest rank. In this case, the display 110 sequentially displays the highest images.

  The number selected in S211 may be, for example, the number selected by the user. For example, the CPU 180 displays a screen for inputting the selected number on the display 110 so that the user can input the number. Then, the CPU 180 can store the inputted number of sheets in, for example, the RAM 140, read out the number of sheets from the RAM 140 in the processing of S211 and select images for the number of read out sheets.

  Thereby, since a plurality of selected images are displayed on the display 110, for example, it is possible to confirm an optimum image or a transmitted image from a plurality of actually captured selected images.

  The above is summarized as an appendix.

(Appendix 1)
In a terminal device that performs wireless communication with other terminal devices via a wireless base station device,
An imaging unit that captures N (N is an integer greater than or equal to 2) images including a first subject and a second subject, and outputs the captured N images as N image frames;
For the N image frames, the top N ′ (N ′ is an integer satisfying 2 ≦ N ′ ≦ N) image frames are selected from the image frames with the highest degree of optimality of the first subject and selected. A control unit that selects an image frame having the maximum degree of optimality of the second subject from the N ′ image frames thus obtained,
The terminal device according to claim 1, wherein the optimum degree is an evaluation value based on a parameter representing a degree of reflection of the photographed subject in the image.

(Appendix 2)
When the first and second subjects and the third subject are photographed in the N image frames, the control unit performs the second subject on the selected N ′ image frames. The terminal device according to claim 1, wherein an image frame having a maximum sum of the optimality of the subject and the optimality of the third subject is selected.

(Appendix 3)
Furthermore, an operation unit and a storage unit are provided,
The control unit stores the storage of the first subject in the storage unit as the transmission destination of the first subject when transmission with respect to the image frame having the maximum optimality of the second subject is not selected by the operation of the operation unit. The terminal device according to appendix 1, wherein an image frame having the maximum degree of optimality of the second subject is not transmitted to another terminal device.

(Appendix 4)
The control unit is configured such that when the optimality of the first subject with respect to the image frame with the maximum optimality of the second subject is equal to or greater than a determination threshold, or with respect to the image frame with the maximum optimality of the second subject. When the optimality of the first subject is lower than the determination threshold and transmission with respect to the image frame with the maximum optimality of the second subject is selected by the operation of the operation unit, the storage unit stores the first The terminal device according to appendix 3, wherein an image frame having the maximum degree of optimality of the second subject is transmitted to the other terminal device stored as a transmission destination of the subject.

(Appendix 5)
The control unit is configured such that, of the selected N ′ image frames, the top N ″ (N ″ is 2 ≦ N ″ ≦ N ′) from the image frame having the maximum optimality of the second subject. 2. The terminal device according to claim 1, wherein (integral integer) image frames are selected as image frames having the maximum degree of optimality of the second subject.

(Appendix 6)
Furthermore, a display unit is provided,
The control unit causes the display unit to sequentially display the N ″ image frames having the maximum optimality of the second subject on the display unit in order from the image frame having the maximum optimality of the second subject. The terminal device according to appendix 5.

(Appendix 7)
The parameter includes the highest luminance value or the highest brightness among the pixels of the photographed subject, the ratio of the highest luminance value to the lowest luminance value, or the acceleration when the N images are taken. The terminal device according to Supplementary Note 1, wherein the terminal device is any one or a combination thereof.

(Appendix 8)
In an image capturing method in a terminal device that includes an image capturing unit that captures an image and performs wireless communication with another terminal device via a wireless base station device,
The imaging unit captures N (N is an integer of 2 or more) images including the first subject and the second subject, and outputs the captured N images as N image frames.
For the N image frames, the top N ′ (N ′ is an integer satisfying 2 ≦ N ′ ≦ N) image frames are selected from the image frames with the highest degree of optimality of the first subject and selected. Selecting an image frame having the maximum degree of optimality of the second subject from the N ′ image frames obtained,
The image photographing method according to claim 1, wherein the optimum degree is an evaluation value based on a parameter representing a degree of reflection in the image of the photographed subject.

(Appendix 9)
An imaging unit that captures N (N is an integer greater than or equal to 2) images including a first subject and a second subject, and outputs the captured N images as N image frames;
For the N image frames, the top N ′ (N ′ is an integer satisfying 2 ≦ N ′ ≦ N) image frames are selected from the image frames with the highest degree of optimality of the first subject and selected. A control unit that selects an image frame having the maximum degree of optimality of the second subject from the N ′ image frames thus obtained,
The image photographing apparatus according to claim 1, wherein the optimum degree is an evaluation value based on a parameter representing a degree of reflection of the photographed subject in the image.

DESCRIPTION OF SYMBOLS 100: Terminal device 101: Imaging part 102: Control part 110: Display 120: Operation part 130: Flash memory 131: Image database 132: Telephone directory database 140: RAM 150: ROM
160: Camera unit 164: DSP
165: Brightness sensor 166: Acceleration sensor 170: Communication unit 180: CPU

Claims (5)

In a terminal device that performs wireless communication with other terminal devices via a wireless base station device,
An imaging unit that captures N (N is an integer greater than or equal to 2) images including a first subject and a second subject, and outputs the captured N images as N image frames;
For the N image frames, the top N ′ (N ′ is an integer satisfying 2 ≦ N ′ ≦ N) image frames are selected from the image frames with the highest degree of optimality of the first subject and selected. A control unit that selects an image frame having the maximum degree of optimality of the second subject from the N ′ image frames thus obtained,
The terminal device according to claim 1, wherein the optimum degree is an evaluation value based on a parameter representing a degree of reflection of the photographed subject in the image.   When the first and second subjects and the third subject are photographed in the N image frames, the control unit performs the second subject on the selected N ′ image frames. The terminal device according to claim 1, wherein an image frame having a maximum total value of the optimality of the subject and the optimality of the third subject is selected. Furthermore, an operation unit and a storage unit are provided,
The control unit stores the storage of the first subject in the storage unit as the transmission destination of the first subject when transmission with respect to the image frame having the maximum optimality of the second subject is not selected by the operation of the operation unit. The terminal device according to claim 1, wherein an image frame having the maximum degree of optimality of the second subject is not transmitted to another terminal device. In an image capturing method in a terminal device that includes an image capturing unit that captures an image and performs wireless communication with another terminal device via a wireless base station device,
The imaging unit captures N (N is an integer of 2 or more) images including the first subject and the second subject, and outputs the captured N images as N image frames.
For the N image frames, the top N ′ (N ′ is an integer satisfying 2 ≦ N ′ ≦ N) image frames are selected from the image frames with the highest degree of optimality of the first subject and selected. Selecting an image frame having the maximum degree of optimality of the second subject from the N ′ image frames obtained,
The image photographing method according to claim 1, wherein the optimum degree is an evaluation value based on a parameter representing a degree of reflection in the image of the photographed subject. An imaging unit that captures N (N is an integer greater than or equal to 2) images including a first subject and a second subject, and outputs the captured N images as N image frames;
For the N image frames, the top N ′ (N ′ is an integer satisfying 2 ≦ N ′ ≦ N) image frames are selected from the image frames with the highest degree of optimality of the first subject and selected. A control unit that selects an image frame having the maximum degree of optimality of the second subject from the N ′ image frames thus obtained,
The image photographing apparatus according to claim 1, wherein the optimum degree is an evaluation value based on a parameter representing a degree of reflection of the photographed subject in the image.

Images