CN110189368B - Image registration method, mobile terminal and computer readable storage medium - Google Patents
Image registration method, mobile terminal and computer readable storage medium Download PDFInfo
- Publication number
- CN110189368B CN110189368B CN201910472038.1A CN201910472038A CN110189368B CN 110189368 B CN110189368 B CN 110189368B CN 201910472038 A CN201910472038 A CN 201910472038A CN 110189368 B CN110189368 B CN 110189368B
- Authority
- CN
- China
- Prior art keywords
- image
- feature point
- registered
- pair set
- point matching
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
Abstract
The image registration method disclosed by the invention carries out rough matching on the reference image and the image to be registered to obtain a first characteristic point matching pair set M1 between a characteristic point set P1 of the reference image and the image to be registered P2; calculating a first homography matrix H1 according to the first feature point matching pair set M1; performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2; calculating a second homography matrix H2 according to the second feature point matching pair set M2; and correcting the image to be registered by using the second homography matrix H2 to obtain a target image. In addition, the invention also discloses the mobile terminal and a computer readable storage medium. Thus, the image registration method provided by the invention can be used for carrying out feature point fine matching according to the mapping feature points, and can greatly improve the matching precision in the feature point matching pair, thereby improving the image registration precision.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to an image registration method, a mobile terminal, and a computer readable storage medium.
Background
With the continuous development of electronic technology, mobile terminals (such as smart phones, tablet computers and the like) have more and more powerful functions, and users can use the mobile terminals to take photos anytime and anywhere and record drops in work or life. Currently, image processing scenes (e.g., multi-frame image denoising, image stitching, image panorama stitching, etc.) require image registration.
Image registration generally refers to a process of matching and overlaying two or more images acquired under different conditions (different imaging devices, imaging positions, angles, etc.), and may also refer to a process of matching one image to be registered to another reference image. For example, when a user holds a mobile terminal or an image capturing device to capture images, due to shake caused by the holding, there is a deviation between the captured two images, and the two images are not completely aligned, and thus the two images need to be processed by using an image registration technique.
However, in the prior art, the error of image registration is large and the accuracy is small.
Disclosure of Invention
In view of the above, the present invention provides an image registration method, a mobile terminal and a computer readable storage medium for solving the above-mentioned technical problems.
First, to achieve the above object, the present invention provides an image registration method, which is applied to a mobile terminal, and the method includes:
respectively detecting and describing characteristic points of a reference image and an image to be registered to obtain a characteristic point set P1 of the reference image and a description subset D1 of each characteristic point, and a characteristic point set P2 of the image to be registered and a description subset D2 of each characteristic point;
performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2;
calculating a first homography matrix H1 according to the first feature point matching pair set M1;
performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2;
calculating a second homography matrix H2 according to the second feature point matching pair set M2;
and correcting the image to be registered by using the second homography matrix H2 to obtain a target image.
Optionally, the calculating a second homography matrix H2 according to the second feature point matching pair set M2 includes:
performing sparsification treatment on the second characteristic point matching pair set M2 to obtain a third characteristic point matching pair set M3;
And calculating the second homography matrix H2 according to the third feature point matching pair set M3.
Optionally, the thinning processing is performed on the second feature point matching pair set M2 to obtain a third feature point matching pair set M3, including:
dividing the reference image into N1 x N2 image sub-blocks;
for each image sub-block, counting the feature point matching pair numbers falling into the image sub-block in the second feature point matching pair set M2;
and reserving Nm pairs of feature point matching pairs in each image sub-block, and deleting the rest feature point matching pairs to obtain a third feature point matching pair set M3.
Optionally, the reserving Nm pair feature point matching pairs in each image sub-block includes:
and reserving the previous Nm pairs of feature point matching pairs in each image sub-block according to the detection sequence in the feature point detection process.
Optionally, the performing coarse matching on the reference image and the image to be registered includes:
performing rough matching on the reference image and the image to be registered by using a nearest neighbor (recently) comparison nearest neighbor mode based on a first threshold value;
the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 includes:
And according to the first homography matrix H1, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold, wherein the second threshold is smaller than the first threshold.
Optionally, the calculating the first homography matrix H1 according to the first feature point matching pair set M1 includes:
calculating a first homography matrix H1 for the first feature point matching pair set M1 by using a first random sampling consensus RANSAC algorithm, wherein the first RANSAC algorithm adopts a first error threshold;
the calculating the second homography matrix H2 according to the third feature point matching pair set M3 includes:
and for the third feature point matching pair set M3, calculating a second homography matrix by using a second RANSAC algorithm, wherein the second RANSAC algorithm adopts a second error threshold value, and the second error threshold value is smaller than the first error threshold value.
Optionally, the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second set M2 of feature point matching pairs between the feature point sets P1 and P2, including:
for each feature point of the set of feature points P1 of the reference image Calculating the initial mapping point +.>
Calculating feature pointsCharacteristic points +.>First Euclidean distance d between descriptors corresponding to each other 1 ；
According to the first Euclidean distance d between the descriptors 1 Euclidean distance weights between descriptorsCalculating a second Euclidean distance d between descriptors 2 ＝w·d 1 Wherein, distance point->The closer the feature points->The smaller the corresponding weight w, and the distance point +.>The farther the feature points ∈ ->The larger the corresponding weight w;
and according to the second Euclidean distance between the descriptors, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold value to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2.
Optionally, the detecting and describing the feature points of the reference image and the image to be registered respectively includes:
and (3) respectively detecting and describing the characteristic points of the reference image and the image to be registered by using an acceleration robust feature SURF algorithm.
Further, to achieve the above object, the present invention also provides a mobile terminal including a memory, at least one processor, and at least one program stored on the memory and executable by the at least one processor, the at least one program implementing the steps of any one of the methods described above when executed by the at least one processor.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium storing at least one program executable by a computer, which when executed by the computer, causes the computer to perform the steps in the above method.
Compared with the prior art, the image registration method provided by the invention is used for respectively detecting and describing the characteristic points of the reference image and the image to be registered to obtain a characteristic point set P1 of the reference image and a description subset D1 of each characteristic point, and a characteristic point set P2 of the image to be registered and a description subset D2 of each characteristic point; performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2; calculating a first homography matrix H1 according to the first feature point matching pair set M1; performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2; calculating a second homography matrix H2 according to the second feature point matching pair set M2; and correcting the image to be registered by using the second homography matrix H2 to obtain a target image. According to the image registration method provided by the invention, the characteristic points are precisely matched according to the mapping characteristic points, so that the matching precision in the characteristic point matching pair can be greatly improved, and the image registration precision is improved.
Drawings
Fig. 1 is a schematic diagram of a hardware structure of a mobile terminal implementing various embodiments of the present invention;
fig. 2 is a schematic diagram of a communication network system according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of an image registration method according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a reference image and an image to be registered provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a first set of feature point matching pairs M1 according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a second set of feature point matching pairs M2 provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of an image to be registered and a target image provided by an embodiment of the present invention;
FIG. 8 is a second flowchart of an image registration method according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a third set of feature point matching pairs M3 provided by an embodiment of the present invention;
fig. 10 is a third flowchart of an image registration method according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the following description, suffixes such as "module", "component", or "unit" for representing elements are used only for facilitating the description of the present invention, and have no specific meaning per se. Thus, "module," "component," or "unit" may be used in combination.
The terminal may be implemented in various forms. For example, the terminals described in the present invention may include mobile terminals such as cell phones, tablet computers, notebook computers, palm computers, personal digital assistants (Personal Digital Assistant, PDA), portable media players (Portable Media Player, PMP), navigation devices, wearable devices, smart bracelets, pedometers, and fixed terminals such as digital TVs, desktop computers, and the like.
The following description will be given taking a mobile terminal as an example, and those skilled in the art will understand that the configuration according to the embodiment of the present invention can be applied to a fixed type terminal in addition to elements particularly used for a moving purpose.
Referring to fig. 1, which is a schematic diagram of a hardware structure of a mobile terminal implementing various embodiments of the present invention, the mobile terminal 100 may include: the wireless communication device comprises an RF (Radio Frequency) unit 101, a WiFi module 102, an audio output unit 103, an A/V (audio/video) input unit 104, a sensor 105, a display unit 106, a user input unit 107, an interface unit 108, a memory 109, a processor 110, a power supply 111 and the like, wherein the number of the processors 110 is at least one. Those skilled in the art will appreciate that the mobile terminal structure shown in fig. 1 is not limiting of the mobile terminal and that the mobile terminal may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The following describes the components of the mobile terminal in detail with reference to fig. 1:
the radio frequency unit 101 may be used for receiving and transmitting signals during the information receiving or communication process, specifically, after receiving downlink information of the base station, processing the downlink information by the processor 110; and, the uplink data is transmitted to the base station. Typically, the radio frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 101 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication, global System for Mobile communications), GPRS (General Packet Radio Service ), CDMA2000 (Code Division Multiple Access, CDMA 2000), WCDMA (Wideband Code Division Multiple Access ), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access, time Division synchronous code Division multiple Access), FDD-LTE (Frequency Division Duplexing-Long Term Evolution, frequency Division Duplex Long term evolution), and TDD-LTE (Time Division Duplexing-Long Term Evolution, time Division Duplex Long term evolution), etc.
WiFi belongs to a short-distance wireless transmission technology, and a mobile terminal can help a user to send and receive e-mails, browse web pages, access streaming media and the like through the WiFi module 102, so that wireless broadband Internet access is provided for the user. Although fig. 1 shows a WiFi module 102, it is understood that it does not belong to the necessary constitution of a mobile terminal, and can be omitted entirely as required within a range that does not change the essence of the invention.
The audio output unit 103 may convert audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output as sound when the mobile terminal 100 is in a call signal reception mode, a talk mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 103 may also provide audio output (e.g., a call signal reception sound, a message reception sound, etc.) related to a specific function performed by the mobile terminal 100. The audio output unit 103 may include a speaker, a buzzer, and the like.
The a/V input unit 104 is used to receive an audio or video signal. The a/V input unit 104 may include a graphics processor (Graphics Processing Unit, GPU) 1041 and a microphone 1042, the graphics processor 1041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphics processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 can receive sound (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, and the like, and can process such sound into audio data. The processed audio (voice) data may be converted into a format output that can be transmitted to the mobile communication base station via the radio frequency unit 101 in the case of a telephone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting the audio signal.
The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and the proximity sensor can turn off the display panel 1061 and/or the backlight when the mobile terminal 100 moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; as for other sensors such as fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured in the mobile phone, the detailed description thereof will be omitted.
The display unit 106 is used to display information input by a user or information provided to the user. The display unit 106 may include a display panel 1061, and the display panel 1061 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 107 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the mobile terminal. In particular, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 1071 or thereabout by using any suitable object or accessory such as a finger, a stylus, etc.) and drive the corresponding connection device according to a predetermined program. The touch panel 1071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends the touch point coordinates to the processor 110, and can receive and execute commands sent from the processor 110. Further, the touch panel 1071 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The user input unit 107 may include other input devices 1072 in addition to the touch panel 1071. In particular, other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc., as specifically not limited herein.
Further, the touch panel 1071 may overlay the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or thereabout, the touch panel 1071 is transferred to the processor 110 to determine the type of touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of touch event. Although in fig. 1, the touch panel 1071 and the display panel 1061 are two independent components for implementing the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 may be integrated with the display panel 1061 to implement the input and output functions of the mobile terminal, which is not limited herein.
The interface unit 108 serves as an interface through which at least one external device can be connected with the mobile terminal 100. For example, the external devices may include a wired or wireless headset port, an external power (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to at least one element within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and the external device.
Memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a storage program area that may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and a storage data area; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, memory 109 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The processor 110 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by running or executing software programs and/or modules stored in the memory 109 and calling data stored in the memory 109, thereby performing overall monitoring of the mobile terminal. The processor 110 may include at least one processing unit; preferably, the processor 110 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The mobile terminal 100 may further include a power source 111 (e.g., a battery) for supplying power to the respective components, and preferably, the power source 111 may be logically connected to the processor 110 through a power management system, so as to perform functions of managing charging, discharging, and power consumption management through the power management system.
Although not shown in fig. 1, the mobile terminal 100 may further include a bluetooth module or the like, which is not described herein.
In order to facilitate understanding of the embodiments of the present invention, a communication network system on which the mobile terminal of the present invention is based will be described below.
Referring to fig. 2, fig. 2 is a schematic diagram of a communication network system according to an embodiment of the present invention, where the communication network system is an LTE system of a general mobile communication technology, and the LTE system includes a UE (User Equipment) 201, an e-UTRAN (Evolved UMTS Terrestrial Radio Access Network ) 202, an epc (Evolved Packet Core, evolved packet core) 203, and an IP service 204 of an operator that are sequentially connected in communication.
Specifically, the UE201 may be the terminal 100 described above, and will not be described herein.
The E-UTRAN202 includes eNodeB2021 and other eNodeB2022, etc. The eNodeB2021 may be connected with other eNodeB2022 by a backhaul (e.g., an X2 interface), the eNodeB2021 is connected to the EPC203, and the eNodeB2021 may provide access from the UE201 to the EPC 203.
EPC203 may include MME (Mobility Management Entity ) 2031, hss (Home Subscriber Server, home subscriber server) 2032, other MMEs 2033, SGW (Serving Gate Way) 2034, pgw (PDN Gate Way) 2035 and PCRF (Policy and Charging Rules Function, policy and tariff function entity) 2036, and so on. The MME2031 is a control node that handles signaling between the UE201 and EPC203, providing bearer and connection management. HSS2032 is used to provide registers to manage functions such as home location registers (not shown) and to hold user specific information about service characteristics, data rates, etc. All user data may be sent through SGW2034 and PGW2035 may provide IP address allocation and other functions for UE201, PCRF2036 is a policy and charging control policy decision point for traffic data flows and IP bearer resources, which selects and provides available policy and charging control decisions for a policy and charging enforcement function (not shown).
IP services 204 may include the internet, intranets, IMS (IP Multimedia Subsystem ), or other IP services, etc.
Although the LTE system is described above as an example, it should be understood by those skilled in the art that the present invention is not limited to LTE systems, but may be applied to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems.
Based on the above-mentioned hardware structure of the mobile terminal 100 and the communication network system, various embodiments of the method of the present invention are presented. The mobile terminal in the embodiment of the invention can be any mobile terminal with a shooting function.
Referring to fig. 3, fig. 3 is a flowchart illustrating steps of an image registration method according to an embodiment of the present invention, where the method is applied to a mobile terminal, as shown in fig. 3, and the method includes:
step 301, detecting and describing feature points of a reference image and an image to be registered respectively to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point.
In the step, the mobile terminal respectively detects and describes characteristic points of a reference image and an image to be registered to obtain a characteristic point set of the reference image And a descriptor set of the respective feature pointsAnd a feature point set of the images to be registered +.>And descriptor set of the respective feature points +.>Wherein n is 1 N being the total number of feature points in the reference image 2 And the total number of the feature points in the image to be registered is the total number of the feature points. In some embodiments of the present invention, the mobile terminal may use a SURF (speeded up robust feature) algorithm to detect and describe feature points of the reference image and the image to be registered respectively.
Step 302, performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2.
In this step, the mobile terminal performs rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set between the feature point sets P1 and P2Specifically, the mobile terminal may perform rough matching on the reference image and the image to be registered by using a nearest neighbor method based on a first threshold value, so as to obtain the first feature point matching pair set M1.
For example, referring to fig. 4 and 5, assuming that the reference image is the image shown in fig. 4 (4 a) and the image to be registered is the image shown in fig. 4 (4 b), the first set of feature point matching pairs M1 obtained in step 302 is shown by the line in fig. 5.
Step 303, calculating a first homography matrix H1 according to the first feature point matching pair set M1.
In this step, the mobile terminal calculates a first homography matrix H1 according to the first feature point matching pair set M1. Specifically, the mobile terminal may calculate, for the first set of feature point matching pairs M1, a first homography matrix H1 using a first random sampling consensus RANSAC algorithm, where the first RANSAC algorithm employs a first error threshold.
And step 304, performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second feature point matching pair set M2 between the feature point sets P1 and P2.
In this step, the mobile terminal performs fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second feature point matching pair set between feature point sets P1 and P2Specifically, the mobile terminal may perform rough matching on the reference image and the image to be registered by using a nearest neighbor to next neighbor method based on a second threshold, so as to obtain the first feature point matching pair set M2, where the second threshold is smaller than the first threshold.
It will be appreciated that after rough matching of the reference image and the image to be registered, more pairs of mismatching may occur due to errors, such as intersecting lines as shown in fig. 5. By performing the fine matching on the reference image and the image to be registered in this step 304, a second feature point matching set M2 with a lower matching pair error rate can be obtained, as shown in fig. 6, where the lines in fig. 6 are reduced by a lot of intersecting lines compared with the lines in fig. 5.
Step 305, calculating a second homography matrix H2 according to the second feature point matching pair set M2.
In this step, the mobile terminal calculates a second homography matrix H2 according to the second feature point matching pair set M2. Specifically, the mobile terminal may calculate, for the second set of feature point matching pairs M2, a second homography matrix H2 using a second RANSAC algorithm, where the second RANSAC algorithm employs a second error threshold, and the second error threshold is smaller than the first error threshold.
In some embodiments of the present invention, the method for calculating the second homography matrix H2 by the mobile terminal according to the second feature point matching pair set M2 may specifically include: performing sparsification treatment on the second characteristic point matching pair set M2 to obtain a third characteristic point matching pair set M3; and calculating the second homography matrix H2 according to the third feature point matching pair set M3.
And 306, correcting the image to be registered by using the second homography matrix H2 to obtain a target image.
In the step, the mobile terminal corrects the image to be registered by using the second homography matrix H2 to obtain a target image. Referring to fig. 7, the image shown in (7 a) in fig. 7 is an image to be registered, and the image shown in (7 b) is a target image registered by the image registration method provided by the embodiment of the present invention.
In this embodiment, the image registration method performs feature point detection and description on a reference image and an image to be registered respectively, so as to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point; performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2; calculating a first homography matrix H1 according to the first feature point matching pair set M1; performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2; calculating a second homography matrix H2 according to the second feature point matching pair set M2; and correcting the image to be registered by using the second homography matrix H2 to obtain a target image. Therefore, the image registration method performs feature point fine matching according to the mapping feature points, and can greatly improve the matching precision in the feature point matching pairs, thereby improving the image registration precision.
Optionally, the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second set M2 of feature point matching pairs between the feature point sets P1 and P2, including:
for each feature point of the set of feature points P1 of the reference imageCalculating the initial mapping point +.>
Calculating feature pointsCharacteristic points +.>First Euclidean distance d between descriptors corresponding to each other 1 ；
According to the first Euclidean distance d between the descriptors 1 Euclidean distance weights between descriptorsCalculating a second Euclidean distance d between descriptors 2 ＝w·d 1 Wherein, distance point->The closer the feature points->The smaller the corresponding weight w, and the distance point +.>The farther the feature points ∈ ->The larger the corresponding weight w;
and according to the second Euclidean distance between the descriptors, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold value to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2.
In this embodiment, the mobile terminal may specifically perform fine matching on the reference image and the image to be registered in the above manner.
Referring to fig. 8, fig. 8 is a second flowchart of an image registration method according to an embodiment of the present invention, where the image registration method is applied to a mobile terminal, as shown in fig. 8, and the method includes:
step 801, feature point detection and description are performed on a reference image and an image to be registered respectively, so as to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point.
Step 802, performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2.
Step 803, calculating a first homography matrix H1 according to the first feature point matching pair set M1.
Step 804, performing fine matching on the reference image and the image to be registered according to the first homography matrix H1, to obtain a second feature point matching pair set M2 between the feature point sets P1 and P2.
The steps 801 to 804 are the same as the steps 301 to 304 in the embodiment of fig. 3, and are not repeated here.
And 805, performing sparsification processing on the second feature point matching pair set M2 to obtain a third feature point matching pair set M3.
In this step, the mobile terminal performs a thinning process on the second feature point matching pair set M2 to obtain a third feature point matching pair set M3. Specifically, the mobile terminal may divide the reference image into N1 x N2 image sub-blocks; for each image sub-block, counting the feature point matching pair numbers falling into the image sub-block in the second feature point matching pair set M2; and reserving Nm pairs of feature point matching pairs in each image sub-block, and deleting the rest feature point matching pairs to obtain a third feature point matching pair set M3.
It can be appreciated that, because there may be a case where the detected feature points are unevenly distributed, for example, as shown in fig. 6, by performing the thinning processing on the second feature point set M2 in this step 805, a third feature point matching pair set M3 is obtained, as shown by a line in fig. 9, where the feature point distribution in the third feature point matching pair set M3 is more even than the feature point distribution in the second feature point set M2.
Step 806, calculating the second homography matrix H2 according to the third feature point matching pair set M3.
In this step, the mobile terminal calculates the second homography matrix H2 according to the third feature point matching pair set M3. Specifically, the mobile terminal may calculate, for the third set of feature point matching pairs M3, a second homography matrix using a second RANSAC algorithm, where the second RANSAC algorithm employs a second error threshold, and the second error threshold is smaller than the first error threshold.
And step 807, correcting the image to be registered by using the second homography matrix H2 to obtain a target image.
The step 807 is the same as the step 306 in the embodiment of fig. 3 of the present invention, and will not be described herein.
In this embodiment, the image registration method performs feature point detection and description on a reference image and an image to be registered respectively, so as to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point; performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2; calculating a first homography matrix H1 according to the first feature point matching pair set M1; performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2; performing sparsification treatment on the second characteristic point matching pair set M2 to obtain a third characteristic point matching pair set M3; calculating the second homography matrix H2 according to the third feature point matching pair set M3; and correcting the image to be registered by using the second homography matrix H2 to obtain a target image. Therefore, the image registration method performs feature point fine matching according to the mapping feature points, and can greatly improve the matching precision in the feature point matching pairs, thereby improving the image registration precision. By adopting the characteristic point sparsification strategy, the distribution of the characteristic point matching pairs in the registration process can be more uniform.
Optionally, the thinning processing is performed on the second feature point matching pair set M2 to obtain a third feature point matching pair set M3, including:
dividing the reference image into N1 x N2 image sub-blocks;
for each image sub-block, counting the feature point matching pair numbers falling into the image sub-block in the second feature point matching pair set M2;
and reserving Nm pairs of feature point matching pairs in each image sub-block, and deleting the rest feature point matching pairs to obtain a third feature point matching pair set M3.
In this embodiment, the mobile terminal performs the thinning process on the second feature point matching pair set M2 in the above manner, to obtain a third feature point matching pair set M3.
Optionally, the reserving Nm pair feature point matching pairs in each image sub-block includes:
and reserving the previous Nm pairs of feature point matching pairs in each image sub-block according to the detection sequence in the feature point detection process.
In this embodiment, the mobile terminal may keep the previous Nm pairs of feature point matching pairs in each image sub-block according to the detection sequence in the feature point detection process.
Optionally, the performing coarse matching on the reference image and the image to be registered includes:
Performing rough matching on the reference image and the image to be registered by using a nearest neighbor (recently) comparison nearest neighbor mode based on a first threshold value;
the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 includes:
and according to the first homography matrix H1, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold, wherein the second threshold is smaller than the first threshold.
In the embodiment of the present invention, the value of the first threshold may be specifically α 1 The value of the second threshold value may be α 1 ＝0.5。
Optionally, the calculating the first homography matrix H1 according to the first feature point matching pair set M1 includes:
calculating a first homography matrix H1 for the first feature point matching pair set M1 by using a first random sampling consensus RANSAC algorithm, wherein the first RANSAC algorithm adopts a first error threshold;
the calculating the second homography matrix H2 according to the third feature point matching pair set M3 includes:
and for the third feature point matching pair set M3, calculating a second homography matrix by using a second RANSAC algorithm, wherein the second RANSAC algorithm adopts a second error threshold value, and the second error threshold value is smaller than the first error threshold value.
In the embodiment of the present invention, the value of the first error threshold may be specifically β 1 The value of the second error threshold may be β 1 ＝10。
Referring to fig. 10, fig. 10 is a third flowchart of an image registration method according to an embodiment of the present invention, where the image registration method is applied to a mobile terminal, as shown in fig. 10, and the method includes:
in step 1001, feature points of the reference image and the image to be registered are detected and described by using an acceleration robust feature SURF algorithm, so as to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point.
In the step, the mobile terminal uses a SURF algorithm to detect and describe feature points of the reference image and the image to be registered.
Step 1002, performing rough matching on the reference image and the image to be registered, to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2.
Step 1003, calculating a first homography matrix H1 according to the first feature point matching pair set M1.
Step 1004, performing fine matching on the reference image and the image to be registered according to the first homography matrix H1, to obtain a second feature point matching pair set M2 between the feature point sets P1 and P2.
Step 1005, calculating a second homography matrix H2 according to the second feature point matching pair set M2.
And 1006, correcting the image to be registered by using the second homography matrix H2 to obtain a target image.
The steps 1002 to 1006 are the same as the steps 302 to 306 in the embodiment of fig. 3, and are not repeated here.
In this embodiment, the image registration method uses an acceleration robust feature SURF algorithm to detect and describe feature points of a reference image and an image to be registered respectively, so as to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point; performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2; calculating a first homography matrix H1 according to the first feature point matching pair set M1; performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2; calculating a second homography matrix H2 according to the second feature point matching pair set M2; and correcting the image to be registered by using the second homography matrix H2 to obtain a target image. Therefore, the image registration method performs feature point fine matching according to the mapping feature points, and can greatly improve the matching precision in the feature point matching pairs, thereby improving the image registration precision.
Those of ordinary skill in the art will appreciate that all or part of the steps of implementing the methods of the above embodiments may be implemented by at least one program instruction associated hardware, where the at least one program may be stored in the memory 109 of the mobile terminal 100 as shown in fig. 1 and executed by the processor 110, where the at least one program when executed by the processor 110 implements the steps of:
respectively detecting and describing characteristic points of a reference image and an image to be registered to obtain a characteristic point set P1 of the reference image and a description subset D1 of each characteristic point, and a characteristic point set P2 of the image to be registered and a description subset D2 of each characteristic point;
performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2;
calculating a first homography matrix H1 according to the first feature point matching pair set M1;
performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2;
calculating a second homography matrix H2 according to the second feature point matching pair set M2;
And correcting the image to be registered by using the second homography matrix H2 to obtain a target image.
Optionally, the calculating a second homography matrix H2 according to the second feature point matching pair set M2 includes:
performing sparsification treatment on the second characteristic point matching pair set M2 to obtain a third characteristic point matching pair set M3;
and calculating the second homography matrix H2 according to the third feature point matching pair set M3.
Optionally, the thinning processing is performed on the second feature point matching pair set M2 to obtain a third feature point matching pair set M3, including:
dividing the reference image into N1 x N2 image sub-blocks;
for each image sub-block, counting the feature point matching pair numbers falling into the image sub-block in the second feature point matching pair set M2;
and reserving Nm pairs of feature point matching pairs in each image sub-block, and deleting the rest feature point matching pairs to obtain a third feature point matching pair set M3.
Optionally, the reserving Nm pair feature point matching pairs in each image sub-block includes:
and reserving the previous Nm pairs of feature point matching pairs in each image sub-block according to the detection sequence in the feature point detection process.
Optionally, the performing coarse matching on the reference image and the image to be registered includes:
Performing rough matching on the reference image and the image to be registered by using a nearest neighbor (recently) comparison nearest neighbor mode based on a first threshold value;
the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 includes:
and according to the first homography matrix H1, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold, wherein the second threshold is smaller than the first threshold.
Optionally, the calculating the first homography matrix H1 according to the first feature point matching pair set M1 includes:
calculating a first homography matrix H1 for the first feature point matching pair set M1 by using a first random sampling consensus RANSAC algorithm, wherein the first RANSAC algorithm adopts a first error threshold;
the calculating the second homography matrix H2 according to the third feature point matching pair set M3 includes:
and for the third feature point matching pair set M3, calculating a second homography matrix by using a second RANSAC algorithm, wherein the second RANSAC algorithm adopts a second error threshold value, and the second error threshold value is smaller than the first error threshold value.
Optionally, the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second set M2 of feature point matching pairs between the feature point sets P1 and P2, including:
for each feature point of the set of feature points P1 of the reference imageCalculating the initial mapping point +.>
Calculating feature pointsCharacteristic points +.>First Euclidean distance d between descriptors corresponding to each other 1 ；
According to the first Euclidean distance d between the descriptors 1 Euclidean distance weights between descriptorsCalculating a second Euclidean distance d between descriptors 2 ＝w·d 1 Wherein, distance point->The closer the feature points->The smaller the corresponding weight w, and the distance point +.>The farther the feature points ∈ ->The larger the corresponding weight w;
and according to the second Euclidean distance between the descriptors, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold value to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2.
Optionally, the detecting and describing the feature points of the reference image and the image to be registered respectively includes:
And (3) respectively detecting and describing the characteristic points of the reference image and the image to be registered by using an acceleration robust feature SURF algorithm.
In this embodiment, the mobile terminal detects and describes feature points of a reference image and an image to be registered respectively, so as to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point; performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2; calculating a first homography matrix H1 according to the first feature point matching pair set M1; performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2; calculating a second homography matrix H2 according to the second feature point matching pair set M2; and correcting the image to be registered by using the second homography matrix H2 to obtain a target image. Therefore, the mobile terminal performs feature point fine matching according to the mapping feature points, and can greatly improve the matching precision in the feature point matching pairs, so that the image registration precision is improved.
Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the methods of the above embodiments may be implemented by at least one program instruction-dependent hardware, and the at least one program may be stored on a computer-readable storage medium, the at least one program when executed comprising the steps of:
respectively detecting and describing characteristic points of a reference image and an image to be registered to obtain a characteristic point set P1 of the reference image and a description subset D1 of each characteristic point, and a characteristic point set P2 of the image to be registered and a description subset D2 of each characteristic point;
performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2;
calculating a first homography matrix H1 according to the first feature point matching pair set M1;
performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2;
calculating a second homography matrix H2 according to the second feature point matching pair set M2;
and correcting the image to be registered by using the second homography matrix H2 to obtain a target image.
Optionally, the calculating a second homography matrix H2 according to the second feature point matching pair set M2 includes:
performing sparsification treatment on the second characteristic point matching pair set M2 to obtain a third characteristic point matching pair set M3;
and calculating the second homography matrix H2 according to the third feature point matching pair set M3.
Optionally, the thinning processing is performed on the second feature point matching pair set M2 to obtain a third feature point matching pair set M3, including:
dividing the reference image into N1 x N2 image sub-blocks;
for each image sub-block, counting the feature point matching pair numbers falling into the image sub-block in the second feature point matching pair set M2;
and reserving Nm pairs of feature point matching pairs in each image sub-block, and deleting the rest feature point matching pairs to obtain a third feature point matching pair set M3.
Optionally, the reserving Nm pair feature point matching pairs in each image sub-block includes:
and reserving the previous Nm pairs of feature point matching pairs in each image sub-block according to the detection sequence in the feature point detection process.
Optionally, the performing coarse matching on the reference image and the image to be registered includes:
Performing rough matching on the reference image and the image to be registered by using a nearest neighbor (recently) comparison nearest neighbor mode based on a first threshold value;
the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 includes:
and according to the first homography matrix H1, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold, wherein the second threshold is smaller than the first threshold.
Optionally, the calculating the first homography matrix H1 according to the first feature point matching pair set M1 includes:
calculating a first homography matrix H1 for the first feature point matching pair set M1 by using a first random sampling consensus RANSAC algorithm, wherein the first RANSAC algorithm adopts a first error threshold;
the calculating the second homography matrix H2 according to the third feature point matching pair set M3 includes:
and for the third feature point matching pair set M3, calculating a second homography matrix by using a second RANSAC algorithm, wherein the second RANSAC algorithm adopts a second error threshold value, and the second error threshold value is smaller than the first error threshold value.
Optionally, the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second set M2 of feature point matching pairs between the feature point sets P1 and P2, including:
for each feature point of the set of feature points P1 of the reference imageCalculating the initial mapping point +.>
Calculating feature pointsCharacteristic points +.>First Euclidean distance d between descriptors corresponding to each other 1 ；
According to the first Euclidean distance d between the descriptors 1 Euclidean distance weights between descriptorsCalculating a second Euclidean distance d between descriptors 2 ＝w·d 1 Wherein, distance point->The closer the feature points->The smaller the corresponding weight w, and the distance point +.>The farther the feature points ∈ ->The larger the corresponding weight w;
and according to the second Euclidean distance between the descriptors, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold value to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2.
Optionally, the detecting and describing the feature points of the reference image and the image to be registered respectively includes:
And (3) respectively detecting and describing the characteristic points of the reference image and the image to be registered by using an acceleration robust feature SURF algorithm.
In this embodiment, when the at least one program is executed, feature point detection and description are performed on a reference image and an image to be registered respectively, so as to obtain a feature point set P1 of the reference image and a description subset D1 of each feature point, and a feature point set P2 of the image to be registered and a description subset D2 of each feature point; performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2; calculating a first homography matrix H1 according to the first feature point matching pair set M1; performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2; calculating a second homography matrix H2 according to the second feature point matching pair set M2; and correcting the image to be registered by using the second homography matrix H2 to obtain a target image. In this way, when the at least one program is executed, the feature point fine matching is performed according to the mapped feature points, so that the matching precision in the feature point matching pair can be greatly improved, and the precision of image registration can be improved.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. An image registration method applied to a mobile terminal, the method comprising:
respectively detecting and describing characteristic points of a reference image and an image to be registered to obtain a characteristic point set P1 of the reference image and a description subset D1 of each characteristic point, and a characteristic point set P2 of the image to be registered and a description subset D2 of each characteristic point;
performing rough matching on the reference image and the image to be registered to obtain a first feature point matching pair set M1 between the feature point sets P1 and P2;
calculating a first homography matrix H1 according to the first feature point matching pair set M1;
performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2;
calculating a second homography matrix H2 according to the second feature point matching pair set M2;
Correcting the image to be registered by using the second homography matrix H2 to obtain a target image;
wherein the performing coarse matching on the reference image and the image to be registered includes:
performing rough matching on the reference image and the image to be registered by using a nearest neighbor (recently) comparison nearest neighbor mode based on a first threshold value;
the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 includes:
according to the first homography matrix H1, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold, wherein the second threshold is smaller than the first threshold;
the performing fine matching on the reference image and the image to be registered according to the first homography matrix H1 to obtain a second feature point matching pair set M2 between the feature point sets P1 and P2, including:
for each feature point of the set of feature points P1 of the reference imageCalculating the initial mapping point +.>
Calculating feature pointsCharacteristic points +. >First Euclidean distance d between descriptors corresponding to each other 1 ；
According to the first Euclidean distance d between the descriptors 1 Euclidean distance weights between descriptorsCalculating a second Euclidean distance d between descriptors 2 ＝w·d 1 Wherein, distance point->The closer the feature points->The smaller the corresponding weight w, and the distance point +.>The farther the feature points ∈ ->The larger the corresponding weight w;
and according to the second Euclidean distance between the descriptors, performing fine matching on the reference image and the image to be registered by using a nearest neighbor and next neighbor mode based on a second threshold value to obtain a second characteristic point matching pair set M2 between the characteristic point sets P1 and P2.
2. The image registration method according to claim 1, wherein the calculating a second homography matrix H2 from the second set of feature point matching pairs M2 includes:
performing sparsification treatment on the second characteristic point matching pair set M2 to obtain a third characteristic point matching pair set M3;
and calculating the second homography matrix H2 according to the third feature point matching pair set M3.
3. The image registration method according to claim 2, wherein the thinning-out processing is performed on the second feature point matching pair set M2 to obtain a third feature point matching pair set M3, including:
Dividing the reference image into N1 x N2 image sub-blocks;
for each image sub-block, counting the feature point matching pair numbers falling into the image sub-block in the second feature point matching pair set M2;
and reserving Nm pairs of feature point matching pairs in each image sub-block, and deleting the rest feature point matching pairs to obtain a third feature point matching pair set M3.
4. The image registration method according to claim 3, wherein the retaining the Nm pair feature point matching pairs in each image sub-block includes:
and reserving the previous Nm pairs of feature point matching pairs in each image sub-block according to the detection sequence in the feature point detection process.
5. The image registration method according to claim 2, wherein the calculating a first homography matrix H1 from the first set of feature point matching pairs M1 includes:
calculating a first homography matrix H1 for the first feature point matching pair set M1 by using a first random sampling consensus RANSAC algorithm, wherein the first RANSAC algorithm adopts a first error threshold;
the calculating the second homography matrix H2 according to the third feature point matching pair set M3 includes:
and for the third feature point matching pair set M3, calculating a second homography matrix by using a second RANSAC algorithm, wherein the second RANSAC algorithm adopts a second error threshold value, and the second error threshold value is smaller than the first error threshold value.
6. The image registration method according to any one of claims 1 to 5, wherein the feature point detection and description of the reference image and the image to be registered, respectively, includes:
and (3) respectively detecting and describing the characteristic points of the reference image and the image to be registered by using an acceleration robust feature SURF algorithm.
7. A mobile terminal comprising a memory, at least one processor and at least one program stored on the memory and executable by the at least one processor, the at least one program when executed by the at least one processor implementing the steps of the method of any of the preceding claims 1-6.
8. A computer-readable storage medium storing at least one program executable by a computer, wherein the at least one program, when executed by the computer, causes the computer to perform the steps in the method of any one of the preceding claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910472038.1A CN110189368B (en) | 2019-05-31 | 2019-05-31 | Image registration method, mobile terminal and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910472038.1A CN110189368B (en) | 2019-05-31 | 2019-05-31 | Image registration method, mobile terminal and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110189368A CN110189368A (en) | 2019-08-30 |
| CN110189368B true CN110189368B (en) | 2023-09-19 |
Family
ID=67719623
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910472038.1A Active CN110189368B (en) | 2019-05-31 | 2019-05-31 | Image registration method, mobile terminal and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110189368B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111932593B (en) * | 2020-07-21 | 2024-04-09 | 湖南中联重科智能技术有限公司 | Image registration method, system and equipment based on touch screen gesture correction |
| CN112017218A (en) * | 2020-09-09 | 2020-12-01 | 杭州海康威视数字技术股份有限公司 | Image registration method and device, electronic equipment and storage medium |
| CN113362380A (en) * | 2021-06-09 | 2021-09-07 | 北京世纪好未来教育科技有限公司 | Image feature point detection model training method and device and electronic equipment thereof |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106023187A (en) * | 2016-05-17 | 2016-10-12 | 西北工业大学 | Image registration method based on SIFT feature and angle relative distance |
| CN106296570A (en) * | 2016-07-28 | 2017-01-04 | 北京小米移动软件有限公司 | Image processing method and device |
| CN106558074A (en) * | 2015-09-18 | 2017-04-05 | 河北工业大学 | Coarse-fine combination matching algorithm in assemble of the satellite based on rotational transformation matrix |
| WO2017107700A1 (en) * | 2015-12-21 | 2017-06-29 | 努比亚技术有限公司 | Image registration method and terminal |
| CN106919944A (en) * | 2017-01-20 | 2017-07-04 | 南京航空航天大学 | A kind of wide-angle image method for quickly identifying based on ORB algorithms |
| CN108447084A (en) * | 2018-03-17 | 2018-08-24 | 北京工业大学 | Stereo matching compensation method based on ORB features |
| CN108550165A (en) * | 2018-03-18 | 2018-09-18 | 哈尔滨工程大学 | A kind of image matching method based on local invariant feature |
| WO2018184255A1 (en) * | 2017-04-06 | 2018-10-11 | 华为技术有限公司 | Image correction method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9147226B2 (en) * | 2012-09-10 | 2015-09-29 | Nokia Technologies Oy | Method, apparatus and computer program product for processing of images |
-
2019
- 2019-05-31 CN CN201910472038.1A patent/CN110189368B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106558074A (en) * | 2015-09-18 | 2017-04-05 | 河北工业大学 | Coarse-fine combination matching algorithm in assemble of the satellite based on rotational transformation matrix |
| WO2017107700A1 (en) * | 2015-12-21 | 2017-06-29 | 努比亚技术有限公司 | Image registration method and terminal |
| CN106023187A (en) * | 2016-05-17 | 2016-10-12 | 西北工业大学 | Image registration method based on SIFT feature and angle relative distance |
| CN106296570A (en) * | 2016-07-28 | 2017-01-04 | 北京小米移动软件有限公司 | Image processing method and device |
| CN106919944A (en) * | 2017-01-20 | 2017-07-04 | 南京航空航天大学 | A kind of wide-angle image method for quickly identifying based on ORB algorithms |
| WO2018184255A1 (en) * | 2017-04-06 | 2018-10-11 | 华为技术有限公司 | Image correction method and device |
| CN108447084A (en) * | 2018-03-17 | 2018-08-24 | 北京工业大学 | Stereo matching compensation method based on ORB features |
| CN108550165A (en) * | 2018-03-18 | 2018-09-18 | 哈尔滨工程大学 | A kind of image matching method based on local invariant feature |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110189368A (en) | 2019-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110365347B (en) | Power amplifier control method, device and computer readable storage medium | |
| CN108512625B (en) | Anti-interference method for camera, mobile terminal and storage medium | |
| CN109195213B (en) | Mobile terminal screen control method, mobile terminal and computer readable storage medium | |
| CN108198150B (en) | Method for eliminating image dead pixel, terminal and storage medium | |
| CN110189368B (en) | Image registration method, mobile terminal and computer readable storage medium | |
| CN108038834B (en) | Method, terminal and computer readable storage medium for reducing noise | |
| CN109934769B (en) | Method, terminal and storage medium for long screenshot of screen | |
| CN109584897B (en) | Video noise reduction method, mobile terminal and computer readable storage medium | |
| CN107680059A (en) | A kind of determination methods of image rectification, terminal and computer-readable recording medium | |
| CN110086993B (en) | Image processing method, image processing device, mobile terminal and computer readable storage medium | |
| CN110052024B (en) | Game vibration adjusting method, adjusting device, mobile terminal and storage medium | |
| CN109445945B (en) | Memory allocation method of application program, mobile terminal, server and storage medium | |
| CN108848321B (en) | Exposure optimization method, device and computer-readable storage medium | |
| CN113301251B (en) | Auxiliary shooting method, mobile terminal and computer readable storage medium | |
| CN113301253B (en) | Auxiliary shooting method for astronomical image, mobile terminal and storage medium | |
| CN108595600B (en) | Photo classification method, mobile terminal and readable storage medium | |
| CN110069320B (en) | Classification correction method, terminal, system and storage medium for application program | |
| CN109462829B (en) | Call transfer method, device and computer readable storage medium | |
| CN109711850B (en) | Secure payment method, device and computer readable storage medium | |
| CN109684020B (en) | Theme switching method, device and computer readable storage medium | |
| CN112954225B (en) | Multi-frame picture shooting method and device and computer readable storage medium | |
| CN112822548B (en) | Screen projection display method and device, mobile terminal and storage medium | |
| CN112532838B (en) | Image processing method, mobile terminal and computer storage medium | |
| CN113301252B (en) | Image photographing method, mobile terminal and computer readable storage medium | |
| CN111476231B (en) | Image area identification method, device and computer readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |