JP2013062711A - Photographing device, photographed image processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a photographing device enabling finer specification of a scene to be photographed.SOLUTION: A photographing device comprises: image detection means that detects a scene on the basis of image data; odor detection means that detects a scene on the basis of odor data; and scene detection means that detects a scene on the basis of the scene detected by the image detection means and the scene detected by the odor detection means.

Description

  The present invention relates to a photographing apparatus, a photographed image processing method, and a program to be executed by a computer.

  2. Description of the Related Art In recent years, some photographing apparatuses such as digital still cameras and digital video cameras have a function of determining a photographing scene from image processing information and calculating an optimum exposure value for the determined photographing scene (Patent Document). 1). In the camera disclosed in Patent Document 1, the shooting scene to be determined is a close-up scene, a portrait scene, and a landscape according to the occupation ratio of the main subject area occupied by the main subject on the screen and the background area occupied by the background on the screen. It is determined which of the scenes corresponds to the shooting scene.

  Japanese Patent Application Laid-Open No. H10-228867 discloses a camera that can record information about sensed odor along with photographed image data. An example of an odor sensor that detects an odor is disclosed in Non-Patent Document 1.

JP 2000-75351 A JP 2010-87940 A

Tomoko Seyama, 1 other, “odor sensor using plasma organic thin film”, NTT Technical Journal, Nippon Telegraph and Telephone Corporation, December 1, 2003, 2003 vol. 15 No. 12, p. 47-50

  The camera disclosed in Patent Document 1 has a problem that even if the shooting scene is correctly determined, the shooting scenes are only roughly classified, so that shooting conditions cannot be set more finely.

  The present invention has been made in order to solve the above-described problems of the technology, and causes a photographing apparatus, a photographed image processing method, and a computer that can more specifically specify a scene to be photographed. It aims at providing the program for.

In order to achieve the above object, a photographing apparatus of the present invention provides:
Image detection means for detecting a scene based on image data;
Odor detection means for detecting a scene based on odor data;
Scene detection means for specifying a scene based on the scene detected by the image detection means and the scene detected by the odor detection means;
It is the structure which has.

The captured image processing method of the present invention includes:
Detect scenes based on image data,
Detect scenes based on odor data,
The scene is specified based on the scene detected from the image data and the scene detected from the odor data.

Furthermore, the program of the present invention is a program for causing a computer to execute,
Detect scenes based on image data,
Detect scenes based on odor data,
The computer is caused to execute processing for specifying a scene based on a scene detected from the image data and a scene detected from the odor data.

  According to the present invention, since the scene is specified in more detail, an effect specialized for the scene can be applied to the image.

It is a block diagram which shows one structural example of the imaging device of one Embodiment of this invention. 3 is a flowchart for explaining the operation of the photographing apparatus shown in FIG. 1. It is a block diagram which shows the example of 1 structure of the digital camera of this embodiment. It is a sequence diagram which shows an example of operation | movement of the digital camera of this embodiment. It is a table | surface which shows the example of the scene detection conditions based on image data. It is a flowchart which shows the procedure of the scene detection operation | movement which an image signal process part performs. It is a figure which shows the example of the scene detection based on odor data. It is a table | surface which shows the example of the scene specified by combining scene determination image data and scene determination odor data. It is a table | surface which shows the example of the setting information of image processing corresponding to the specified scene.

  A configuration of a photographing apparatus according to an embodiment of the present invention will be described. FIG. 1 is a block diagram showing an example of the configuration of a photographing apparatus according to an embodiment of the present invention.

  As shown in FIG. 1, the photographing apparatus 11 includes an image detection unit 12 that detects a scene based on image data, an odor detection unit 13 that detects a scene based on odor data that is information related to the detected odor, A scene detection unit for specifying a scene based on the scene detected by the image detection unit and the scene detected by the odor detection unit;

  A captured image processing method executed by the imaging apparatus 11 shown in FIG. 1 will be described. FIG. 2 is a flowchart for explaining the operation of the photographing apparatus shown in FIG.

  As shown in FIG. 2, the image detection means 12 detects a scene based on the image data (step 101), and the odor detection means 13 detects a scene based on the odor data (step 102). Further, the scene detection unit 14 specifies a scene based on the scene detected from the image data and the scene detected from the odor data (step 103).

  Note that the photographing apparatus of the present invention can also be applied to a camera of a camera-equipped mobile phone including a smartphone. Further, the function of the photographing apparatus of the present invention may be provided in an information processing apparatus such as a game machine, a tablet PC (Personal Computer), a notebook PC, a DVD (Digital Versatile Disk) recorder, and a Blu-ray Disc (registered trademark) recorder. Good. In the following, an embodiment in the case of a digital camera having the function of the photographing apparatus of the present invention will be described.

  The configuration of the digital camera of this embodiment will be described. FIG. 3 is a block diagram illustrating a configuration example of the digital camera of the present embodiment.

  As shown in FIG. 3, the digital camera of the present embodiment is captured by the module 21 including the lens 1 and the image sensor 2, the image signal processing unit 3 that processes a signal received from the image sensor 2, and the image sensor 2. A display device 5 for displaying the image, a storage unit 6 for storing the image data, a recording microphone 7, an odor sensor 8 for detecting the odor, and an MPU (Micro Processing Unit) 4 constituting each unit. .

  Light reflected by the subject such as sunlight is input to the light receiving unit (not shown) of the image sensor 2 through the lens 1. The module 21 is provided with a drive mechanism (not shown) that moves the lens 1 in the optical axis direction when a control signal related to position control is received from the image signal processing unit 3. The position of the lens 1 is adjusted so that the image is in focus. The imaging element 2 converts light input to the light receiving unit via the lens 1 into an electrical signal and transmits the electrical signal to the image signal processing unit 3. The display device 5 is, for example, a liquid crystal display. The storage unit 6 is a storage device such as a nonvolatile memory and a hard disk drive.

  The odor sensor 8 includes a plurality of sensors that generate an electrical signal when an odor substance is captured. Each of the plurality of sensors has different electric signal patterns output from the plurality of sensors depending on the odor to be detected because the magnitude of the generated electric signal differs depending on the type of odor substance. The odor sensor 8 transmits, to the MPU 4 as odor data, patterns of electric signals output from a plurality of sensors when the odor is detected. Note that the odor sensor disclosed in Patent Document 2 may be used as the odor sensor 8 of the present embodiment. In this case, the detection value pattern shown in the table of FIG. 2 of Patent Document 2 corresponds to the odor data. .

  The image signal processing unit 3 activates the image sensor 2 and the lens 1 when receiving from the MPU 4 a control signal including an instruction to start the operation of the still image / moving image live view mode. When the Bayer image data is received from the image sensor 2, the image signal processing unit 3 performs detection of AE (Auto Exposure) / AF (Auto Focus) using the Bayer image data, and the lens 1 so as to be in focus. The exposure control of the image sensor 2 is performed so as to obtain an appropriate light amount. Further, the image signal processing unit 3 applies WB (white balance), gamma correction, edge enhancement, noise reduction, and color to Bayer image data in both the still image live view mode and the moving image live view / recording mode. After processing such as processing, it is finally converted into YUV image data that is video data and transmitted to the MPU 4. YUV is a type of image color information, and means three types of information: luminance signal (Y), luminance signal and blue component difference (U), and luminance signal and red component difference (V). To do. When the image signal processing unit 3 receives an instruction for capturing a still image from the MPU 4 in the still image live view mode, the image signal processing unit 3 finally converts the YUV image data for one image displayed on the display device 5 into a JPEG (Joint Photograph Experts Group). ) Convert to data and send to MPU4.

  Further, when receiving the Bayer image data at a constant cycle, the image signal processing unit 3 detects a scene that the user is going to shoot from information such as the distance to the subject and the brightness of the image data, and a person is detected as the subject. A detection result including information such as whether or not to include is transmitted to the MPU 4. Hereinafter, this detection result information is referred to as “scene determination image data”. Further, when receiving the setting information related to the image processing from the MPU 4, the image signal processing unit 3 transmits YUV image data reflecting the setting information to the MPU 4. The scene detection operation and scene determination image data of the image signal processing unit 3 will be described in detail with reference to FIGS.

  The MPU 4 includes a memory 41 that stores a program. By executing processing according to the program, the control unit 42, the scene detection unit 43, and the scene determination unit 44 illustrated in FIG. 3 are virtually configured. The memory 41 stores in advance an odor reference table for specifying a scene from odor data. In the odor reference table, odor patterns indicating typical electric signal patterns of various odors are registered corresponding to each of a plurality of scenes. The electric signal pattern of the odor pattern corresponds to the electronic signal pattern of the odor data.

  When an instruction for starting a still image / moving image live view mode is input from the user via an operation unit (not shown), the control unit 42 receives a control signal including an operation start instruction from the image signal processing unit. When the YUV image data is received from the image signal processing unit 3, the YUV image data is transmitted to the display device 5 and the image is displayed on the display device 5. When an instruction for capturing a still image is input from the user via an operation unit (not shown), the control unit 42 stores the JPEG data received from the image signal processing unit 3 in the storage unit 6. At the time of moving image recording, the control unit 42 combines the YUV image data received from the image signal processing unit 3 and sound data that is sound data input via the microphone 7 to create moving image data, Is stored in the storage unit 6.

  Next, the scene detection unit 43 and the scene determination unit 44 will be described. The scene detection process based on odor data and the scene identification process based on two types of data can be performed in either live view / capture of a still image or live view / recording of a video. Here, a case of a still image live view will be described.

  The scene detection unit 43 refers to the odor reference table stored in advance in the memory 41 with respect to the odor data received from the odor sensor 8 and registers the received odor data in the odor reference table. Check against odor pattern. Then, the scene detection unit 43 selects an odor pattern closest to the received odor data, reads a scene corresponding to the selected odor pattern from the odor reference table, and holds the read scene as a detection result. Hereinafter, this detection result information is referred to as “scene determination odor data”.

  When the scene determination unit 44 requests the scene determination odor data from the scene determination unit 44 while holding the scene determination odor data, the scene detection unit 43 passes the stored scene determination odor data to the scene determination unit 44. This embodiment is characterized in that the scene determination odor data is used as one of data for specifying a scene.

  In the present embodiment, it is assumed that odor patterns of forest, tide, gunpowder, milk, cosmetics, animals, flowers, and food are registered in advance in the odor reference table stored in the memory 41. Here, the odors of cosmetics, animals, flowers, and food are not described in detail, but multiple types of odors may be registered for each of the genres of cosmetics, animals, flowers, and food.

  When the scene determination unit 44 receives the scene determination image data from the image signal processing unit 3, the scene determination unit 44 requests the scene detection odor data from the scene detection unit 43, and receives the scene determination odor data and the image signal processing unit 3 from the scene detection unit 43. The scene is specified using the scene determination image data received from the above. In other words, the scene determination unit 44 is based on two types of data: a scene detected by the image signal processing unit 3 from the Bayer image data and a scene detected by the scene detection unit 43 from the odor data received via the odor sensor 8. Identify the scene. Then, the scene determination unit 44 transmits setting information corresponding to the identified scene to the image signal processing unit 3.

  As setting information related to image processing, a setting information table describing optimum setting information for each scene may be stored in the memory 41 in advance. In this case, when the scene is specified, the scene determination unit 44 refers to the setting information table, reads setting information corresponding to the specified scene, transmits the setting information to the image signal processing unit 3, and sets the setting to the image signal processing unit 3. Make the change happen.

  By changing the setting of image processing corresponding to the scene specified by the scene determination unit 44, the position of the lens 1, the exposure of the image sensor 2, and the image processing of the image signal processing unit 3 are changed according to the scene. Is done. As a result, the YUV image data reflecting the setting change is sent to the display device 5 via the MPU 4, and an image of the data is displayed on the display device 5. In addition, since the setting change is also reflected in the captured image in the still image capture mode, the JPEG data reflecting the setting change is saved in the storage unit 6 when captured.

  Next, the operation of the digital camera of this embodiment will be described. FIG. 4 is a sequence diagram showing an example of the operation of the digital camera of this embodiment, and shows a case where the digital camera of this embodiment repeatedly specifies a scene at a constant cycle. Here, the case of the still image live view mode will be described.

  When the user inputs an instruction to activate the still image live view mode to the digital camera of this embodiment, each of the image signal processing unit 3 and the scene detection unit 43 of the MPU 4 starts scene detection. The image signal processing unit 3 and the scene detection unit 43 repeat scene detection at a constant cycle. In the example shown in FIG. 4, since the scene detection unit 43 takes a shorter time for scene detection than the image signal processing unit 3, the scene detection unit 43, when detecting a scene from odor data, displays information on the detected scene. Including scene determination odor data is included (number 201 shown in FIG. 4). The time required for scene detection may be shorter for the image signal processing unit 3 than for the scene detection unit 43.

  Subsequently, when the image signal processing unit 3 detects a scene from the Bayer image data, it transmits scene determination image data including information of the detected scene to the scene determination unit 44 of the MPU 4 (number 202 shown in FIG. 4). When the scene determination unit 44 receives the scene determination image data from the image signal processing unit 3, the scene determination unit 44 requests the scene detection odor data from the scene detection unit 43, and receives the scene determination odor data from the scene detection unit 43 (the numbers shown in FIG. 4). 203).

  The scene determination unit 44 specifies a scene based on two types of data, that is, scene determination image data and scene determination odor data (number 204 shown in FIG. 4). Thereafter, the scene determination unit 44 transmits setting information corresponding to the specified scene to the image signal processing unit 3 (number 205 shown in FIG. 4). As a result, the optimum value of the setting information related to image processing is notified to the image signal processing unit 3. Further, the scene determination unit 44 notifies the image signal processing unit 3 that the scene has been identified (number 206 shown in FIG. 4). When the image signal processing unit 3 receives information from the scene determination unit 44 that the scene identification has been completed, the image signal processing unit 3 detects the next scene using the Bayer image data received from the image sensor 2.

  In this way, the image signal processing unit 3 and the scene detection unit 43 have the same cycle until the user inputs an instruction to stop the still image live view mode to the digital camera of the present embodiment. The scene determination unit 44 identifies a scene based on two types of data, and sets image processing corresponding to the identified scene.

  Next, the operation when the image signal processing unit 3 detects a scene from the Bayer image data in the flowchart shown in FIG. 4 will be described. FIG. 5 is a table showing an example of scene detection conditions based on image data, and FIG. 6 is a flowchart showing a procedure of a scene detection operation executed by the image signal processing unit.

  In the table shown in FIG. The detection conditions are described corresponding to each detection scene. No. When it does not correspond to any of 1 to 7, it is determined that the scene is not detected (No. 8). The table shown in FIG. 5 is stored in the memory 41 in advance.

  In the table shown in FIG. 5, D is a threshold value indicating whether or not the distance from the digital camera of the present embodiment to the subject is a macro distance. Each of Bv1, Bv2, Bv3, and Bv4 is a threshold value for determining brightness (bright value), and a reference value for determining which one of landscape, twilight, and night view is included in the image. Become. The size relationship is Bv4 <Bv3 <Bv2 <Bv1. T1 and T2 are threshold values for determining the color temperature. The magnitude relationship is T2 <T1.

  No. in Table 5 5-No. When the detection conditions of the detection scene of No. 7 are compared, the daytime scenery is brighter than the nighttime scenery, which is a nighttime scenery. No. 5 detection condition is Bv1 or higher. The detection condition of 7 is smaller than Bv4. This is shown in Table 5. 2 and No. The same applies to the third detection condition.

  Also, in the shooting of twilight (No. 6 detection scene), the brightness is darker than in the case of daytime scenes and brighter than in the case of night scenes, so the brightness detection condition is Bv3 or more and less than Bv2. . This is also reflected in the color temperature, and the detection condition at the color temperature is T1 or more in the case of a landscape in the daytime, whereas T2 (<T1) or more in the case of twilight. In the detection condition with color information, the sky is reddish in twilight shooting, so the image contains “red”, whereas the daytime landscape contains “green” and “blue”. Yes.

  As shown in FIG. 6, the image signal processing unit 3 determines whether or not the distance is a macro distance by checking whether or not the distance from the own device to the subject is equal to or less than D (step 301). If the image signal processing unit 3 determines in step 301 that the distance from the device to the subject is equal to or less than D and is a macro distance, the image signal processing unit 3 checks whether or not a human face can be detected from the image (step 302). When the face is detected from the image, the image signal processing unit 3 determines that the person is a close-up scene (step 303). When the face cannot be detected from the image, the user captures a subject other than the person with a macro. Determination is made (step 304). In the case of step 303, No. 1 shown in FIG. 4 detection scenes.

  In step 301, when the image signal processing unit 3 determines that the distance from the apparatus to the subject is smaller than D and is not the macro distance, the image signal processing unit 3 checks whether or not a human face can be detected from the image (step 305). When detecting a face from the image, the image signal processing unit 3 refers to brightness, color temperature, and color information from the image data, and checks whether or not a landscape can be detected from the image (step 306). When the image signal processing unit 3 detects brightness ≧ Bv1 and color temperature ≧ T1 from the image data and detects blue and green as the color information, it determines that the scene is a scene where the user captures a person and a landscape (step 307). ). If the landscape cannot be detected in step 306, the image signal processing unit 3 refers to the brightness, color temperature, and color information to check whether the night scene can be detected from the image (step 308). When the image signal processing unit 3 detects brightness <Bv4 from the image data, the image signal processing unit 3 determines that the scene is a scene where the user captures a person and a night view (step 309). When neither a landscape nor a night view can be detected from the image, the image signal processing unit 3 determines that the scene is a scene in which the user mainly photographs a person (step 310).

  On the other hand, in step 305, when the image signal processing unit 3 cannot detect the human face from the image, the image signal processing unit 3 refers to the brightness, color temperature, and color information from the image data, and detects any of landscape, twilight, and night view from the image. It is determined whether it can be performed (steps 311, 313, 315). The image signal processing unit 3 detects brightness ≧ Bv1 and color temperature ≧ T1 from the image data, and when blue and green are detected as color information, the image signal processing unit 3 determines that the scene is a scene in which the user captures a landscape (step 312). When the image signal processing unit 3 detects Bv3 ≦ brightness <Bv2 and color temperature ≧ T2 from the image data, and detects red as the color information, the image signal processing unit 3 determines that the scene is a scene where the user captures twilight (step 314). If the image signal processing unit 3 detects brightness <Bv4 from the image data, the image signal processing unit 3 determines that the user is shooting a night scene (step 316). If none of the landscape, twilight, and night view can be detected from the image, the image signal processing unit 3 determines that the scene is not subject to scene detection (step 317).

  When the image signal processing unit 3 detects the user's shooting scene according to the procedure shown in FIG. 6 while referring to the table shown in FIG. 5, the items “macro” and “person” of the detected scene in the table shown in FIG. Any scene of “+ landscape”,..., “Night view” or “outside scene” is included in the scene determination image data as information of the detected scene.

  Next, the operation when the scene detection unit 43 detects a scene from odor data in the sequence diagram shown in FIG. 4 will be described with reference to FIG. FIG. 7 is a diagram showing an example of scene detection based on odor data. FIG. 7 is an example of a table showing combinations of detected odors and scenes registered in the odor reference table.

  As illustrated in FIG. 7, the scene detection unit 43 determines that the forest is included in the subject when the odor pattern closest to the odor data is the odor pattern of the forest. In addition, the scene detection unit 43 determines that the sea is included in the subject when the odor pattern closest to the odor data is a tidal odor pattern.

  Further, the scene detection unit 43 determines that fireworks are included in the subject when the odor pattern closest to the odor data is the explosive odor pattern. The scene detection unit 43 determines that the baby is included in the subject when the odor pattern closest to the odor data is the odor pattern of milk. The scene detection unit 43 determines that a woman is included in the subject when the odor pattern closest to the odor data is the odor pattern of cosmetics. The scene detection unit 43 determines that the pet is included in the subject when the odor pattern closest to the odor data is an animal odor pattern.

  In FIG. 7, “various flowers” are described corresponding to the smell of “various flowers”. This means that each of the multiple types of flowers corresponds to the odor of each of the multiple types of flowers, and that the odor and the name of the flower are combined according to the type of flower. Omitted. As for “various foods” shown in FIG. 7, as in the case of flowers, each of the plurality of types of food corresponds to the odor of each of the plurality of types of food.

  When the scene detection unit 43 detects a scene from odor data, it detects one or more elements of “forest”, “sea”,..., “Various foods” of the detection scene in the table shown in FIG. Included in scene determination odor data as scene information. The scene determination odor data includes subject information. When the scene detection unit 43 detects a plurality of elements from the odor data, the scene determination odor data includes two or more elements.

  In the present embodiment, when the scene detection unit 43 detects a plurality of elements from the odor data, a case has been described in which all detected elements are included in the scene determination odor data, but according to a predetermined priority order, The number of elements included in the scene determination odor data may be reduced. For example, if the priority is higher as the priority is higher, the priority is determined in advance as “baby> female> pet> forest> sea”, and the two elements having the highest priority are used as scene determination odor data. It may be included. The priority order is not limited to this case. However, in the case where the detected scene is “person + landscape” and “landscape” shown in FIG. 5, an element of “forest” or “sea” is required when specifying the scene. When the “forest” or “sea” element is detected, the element may always be included in the scene determination odor data.

  Next, an operation when the scene determination unit 44 specifies a scene based on two types of data will be described. FIG. 8 is a table showing an example of a scene specified by combining scene determination image data and scene determination odor data. A table shown in FIG. 8 is stored in the memory 41 in advance.

  As shown in FIG. 8, the scenes to be identified are classified into eight groups of “macro” to “night view” and “outside scene” corresponding to the scene information of the scene determination image data. As shown in FIG. 8, for each group specified by the scene determination image data, “fireworks”, “sea”,..., “Various foods”, etc., corresponding to the scene information of the scene determination odor data It is classified. For example, in a group classified as “macro” in the scene determination image data, the odor data is subdivided into “fireworks”, “various flowers”, “various foods”, and “other than that”.

  In a group of scenes determined to contain a person based on the scene determination image data, the person is subdivided into babies / women and the landscape is subdivided into forest / sea / fireworks based on the scene determination odor data. The subjects included other than the person are subdivided into pets / various flowers / various foods.

  In a group of scenes determined to be “landscape” based on the scene determination image data, the landscape is subdivided into forest / sea based on the scene determination odor data, and the subject included in the image together with the landscape is a pet / various flower. It is subdivided. A group of scenes determined to be “twilight” based on the scene determination image data is subdivided into the sea and other areas based on the scene determination odor data.

  The group of scenes determined to be “night scene” based on the scene determination image data is subdivided into fireworks and others based on the scene determination odor data. From the scene determination image data, in the group of scenes determined not to correspond to any of the seven groups “macro” to “night view”, the subject is forest / sea / fireworks / pet based on the scene determination odor data. / Various flowers / Various foods / Others.

  As shown in the table of FIG. 8, the scene determination unit 44 selects one group from eight classifications based on the information of the scene determination image data, and is included in the image based on the information of the scene determination odor data. The final scene is specified by selecting an element to be a subject. For example, when the scene determination image data includes information of “person + landscape” and the scene determination odor data includes information of “female”, the scene determination unit 44 performs No No. shown in the table of FIG. . It is identified as a scene of 8 “women + landscape”.

  Next, a specific example of the setting information notified to the image signal processing unit 3 corresponding to the scene specified by the scene determination unit 44 will be described. FIG. 9 is a table showing an example of image processing setting information corresponding to the scene specified by the scene determination unit. A table shown in FIG. 9 is stored in the memory 41 in advance.

  The scene determination unit 44 sets setting information for optimizing the contrast, brightness, sharpness, and hue of the image according to the subject specified by the scene determination odor data according to the specified scene. Notify Further, the scene determination unit 44 may instruct the image signal processing unit 3 to perform image conversion processing according to the subject. For example, if the subject is a woman, the image conversion processing is to erase detected face fine lines, spots, dullness, etc., or to enlarge parts such as eyes and mouth in the face. By eliminating fine lines, spots, dullness, etc., an effect of making the skin transparent is obtained, and by enlarging the eyes and mouth, an effect of increasing the presence of the eyes and mouth can be obtained.

  As shown in the setting when the small item shown in FIG. 9 is “baby”, the scene determination unit 44 turns on “smile shutter” for capturing an image when it is detected that the subject person is smiling. The setting to do so may be performed on the image signal processing unit 3.

  According to the present embodiment, not only image data but also odor data having a type of information different from that of an image is used as information for specifying a scene, so that a scene can be specified more finely. By specifying the scene in detail, an effect specific to the scene can be applied to the image. In addition, since the subject information is reflected in the odor data used for scene specification, it is possible to set image processing for the subject of the image data. Therefore, in this embodiment, it can suppress that an image process is set incorrectly, and the user can acquire the image of the scene which matched with the scene which he assumes.

  In the present embodiment, the scene detection from the Bayer image data and the scene detection from the odor data have been described as being separately executed by the image signal processing unit 3 and the MPU 4, but both of these scene detections are performed by the MPU 4. It may be executed. In this case, the MPU 4 receives the Bayer image data used for scene detection from the image signal processing unit 3, and detects a scene from the image data as described with reference to FIGS.

  In addition, baby is detected in odor data, but human face is not detected in image data, forest is detected from odor data, but landscape is not detected in image data, etc. There may be a case where the scene detected by the odor data and the scene detected by the image data do not match. In such a case, how to set the image processing may be determined. Since the scene can be specified more finely, the scene can be subdivided and the scene detection accuracy can be improved.

  The compression format of image data is not limited to JPEG, and the format of image data is not limited to Bayer image data and YUV image data. For example, when displaying an image on the display unit device 5, the control unit 42 may convert the YUV image data into RGB image data and transmit it to the display device 5.

  Further, in this embodiment, for the sake of simplicity of explanation, the case where the subject is moving or the case where shooting is backlit is excluded, and the case where the scene to be detected is a general scene has been described. The digital camera is provided with an acceleration sensor, and at least one of the camera shake detection performed by the acceleration sensor and the moving object detection and backlight determination performed by the image signal processing unit 3 is detected from each of the image data and the odor data. The scene to be identified may be further subdivided by combining the scenes.

1 Lens 2 Image Sensor 3 Image Signal Processing Unit 4 MPU
DESCRIPTION OF SYMBOLS 5 Display device 6 Memory | storage part 7 Microphone 8 Odor sensor 11 Imaging device 12 Image detection means 13 Odor detection means 14 Scene detection means

Claims (6)

Image detection means for detecting a scene based on image data;
Odor detection means for detecting a scene based on odor data;
Scene detection means for specifying a scene based on the scene detected by the image detection means and the scene detected by the odor detection means;
A photographing apparatus having The imaging device according to claim 1,
The image detecting means detects a scene based on the image data and then performs image processing on the image data based on a predetermined setting.
The scene detection unit causes the image detection unit to change the setting of the image processing corresponding to the specified scene. The imaging device according to claim 2,
The scene detection means includes
For each scene specified by a combination of a scene detected based on the image data and a scene detected based on the odor data, a table in which setting information related to the image processing is described is stored in advance. Referring to FIG. 4, the image capturing apparatus reads the setting information corresponding to the identified scene from the table and notifies the image detecting unit to change the image processing setting to the image detecting unit. In the imaging device according to claim 2 or 3,
The scene detection means includes
An imaging apparatus that identifies a subject from a scene detected based on the odor data, and causes the image detection unit to change the image processing setting corresponding to the identified subject. Detect scenes based on image data,
Detect scenes based on odor data,
A captured image processing method for specifying a scene based on a scene detected from the image data and a scene detected from the odor data. A program for causing a computer to execute,
Detect scenes based on image data,
Detect scenes based on odor data,
A program for causing a computer to execute a process of specifying a scene based on a scene detected from the image data and a scene detected from the odor data.

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