WO2019127192A1 - Procédé et appareil de traitement d'image - Google Patents

Procédé et appareil de traitement d'image Download PDF

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Publication number
WO2019127192A1
WO2019127192A1 PCT/CN2017/119291 CN2017119291W WO2019127192A1 WO 2019127192 A1 WO2019127192 A1 WO 2019127192A1 CN 2017119291 W CN2017119291 W CN 2017119291W WO 2019127192 A1 WO2019127192 A1 WO 2019127192A1
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WIPO (PCT)
Prior art keywords
living body
target
determining
joint
image
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PCT/CN2017/119291
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English (en)
Chinese (zh)
Inventor
周游
朱振宇
刘洁
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深圳市大疆创新科技有限公司
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Priority to PCT/CN2017/119291 priority Critical patent/WO2019127192A1/fr
Priority to CN201780022779.9A priority patent/CN109074661A/zh
Publication of WO2019127192A1 publication Critical patent/WO2019127192A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Definitions

  • Embodiments of the present application relate to the field of image processing, and more particularly, to an image processing method and apparatus.
  • Computer vision relies on the imaging system instead of the visual organ as an input sensitive means.
  • the most common is the camera, which can form a basic vision system by a dual camera.
  • the binocular camera system can capture two photos at the same moment and at different angles through two cameras, and then through the difference between the two photos and the position and angle relationship between the two cameras, the triangle relationship can be used to calculate the scene and The distance map of the camera, that is, the depth map can be obtained. In the final analysis, the binocular camera system obtains the depth information of the scene by the difference of two photos at different angles at the same time.
  • the embodiment of the present application provides an image processing method and device, which can fully consider the features of the limbs or joints of the living body in a high dynamic scene with a living body, so that the depth map calculation of the living body is more accurate.
  • an image processing method including: determining a pointing vector of a target point on a target living body in an image to at least one joint, and determining a positional relationship between the target point and at least one pixel point; a pointing vector, and the positional relationship, adjusting a penalty coefficient of a global energy function of a Semi-Global Matching (SGM) algorithm; based on a disparity of the at least one pixel, using the penalty coefficient adjusted
  • SGM Semi-Global Matching
  • an image processing apparatus including a determining unit and a calculating unit; wherein the determining unit is configured to: determine a pointing vector of a target point on a target living body in the image to at least one joint, and determine a positional relationship between the target point and at least one pixel point; the calculating unit is configured to: adjust a penalty coefficient of a global energy function of the SGM algorithm according to the pointing vector, and the positional relationship; based on the at least one pixel point The parallax of the target point is calculated by using the global energy function adjusted by the penalty coefficient.
  • an image processing apparatus comprising a memory and a processor, the memory storing code, the processor being capable of calling code in the memory to perform a method of determining a target point on a target living body in the image to at least a pointing vector at a joint, and determining a positional relationship of the target point with at least one pixel; adjusting a penalty coefficient of a global energy function of the SGM algorithm according to the pointing vector, and the positional relationship; based on the at least one The parallax of the pixel is calculated by using the global energy function adjusted by the penalty coefficient to calculate the parallax of the target point.
  • a computer storage medium storing code for determining a pointing vector of a target point on a target living body in the image to at least one joint, and determining the target point and a positional relationship of at least one pixel; adjusting a penalty coefficient of a global energy function of the SGM algorithm according to the pointing vector, and the positional relationship; and adjusting a penalty coefficient based on the parallax of the at least one pixel
  • the global energy function calculates the parallax of the target point.
  • a computer program product comprising code for determining a pointing vector of a target point on a target living body in an image to at least one joint, and determining the target point a positional relationship with at least one pixel; adjusting a penalty coefficient of a global energy function of the SGM algorithm according to the pointing vector, and the positional relationship; and adjusting the penalty coefficient based on the parallax of the at least one pixel
  • the global energy function calculates the parallax of the target point.
  • a pointing vector of a target point on a target living body in an image to at least one joint is determined, and a positional relationship between the target point and at least one pixel point is determined; according to the pointing vector, And the positional relationship, adjusting a penalty coefficient of the global energy function of the SGM algorithm; calculating a parallax of the target point by using a global energy function adjusted by the penalty coefficient based on the parallax of the at least one pixel point,
  • the penalty coefficient in the semi-global matching algorithm is adjusted by fully considering the features of the limb or joint of the living body, and the fixed penalty coefficient is avoided, so that the depth map calculation of the living body is more accurate.
  • FIG. 1 is a schematic diagram of the disconnection of depth information in a high dynamic scenario.
  • FIG. 2 is a schematic diagram of an image processing method according to an embodiment of the present application.
  • FIG. 3 is a schematic diagram of segmenting a living body using a PAF algorithm according to an embodiment of the present application.
  • Figure 4 is a schematic view of the ground.
  • Figure 5 is a schematic illustration of a limb vector field.
  • Figure 6 is a schematic illustration of a limb vector field.
  • Figure 7 is an image of a thermography.
  • FIG. 8 is a schematic diagram of an image processing apparatus according to an embodiment of the present application.
  • FIG. 9 is a schematic diagram of an image processing apparatus according to an embodiment of the present application.
  • FIG. 10 is a schematic view of a drone according to an embodiment of the present application.
  • a component when a component is “fixedly connected” or “connected” to another component in the embodiment of the present application, or when one component is “fixed” to another component, it may be directly on another component, or There can be a centered component.
  • the route planning obstacle avoidance needs to use the 3D depth map, that is, the quality of the depth map directly affects the success or failure of the obstacle avoidance.
  • the embodiments of the present application provide the following solutions, and better depth information can be obtained.
  • the depth information of the embodiment of the present application may be used for the obstacle avoidance of the UAV, and may also be used for other scenarios, which is not specifically limited in this embodiment of the present application.
  • the embodiment of the present application may use the image captured by the binocular camera to calculate the depth information, and may also use the image captured by the monocular camera to calculate the depth information, which is not specifically limited in the embodiment of the present application.
  • the embodiments of the present application can be used for aerial vehicles or other vehicles with multiple cameras, such as unmanned cars, auto-flying drones, VR/AR glasses, dual-camera mobile phones, smart cars with visual systems, etc. device.
  • FIG. 2 is a schematic flowchart of an image processing method 100 according to an embodiment of the present application.
  • the method 100 includes at least a portion of the following.
  • a pointing vector of a target point on the target living body in the image to at least one joint is determined, and a positional relationship of the target point with the at least one pixel point is determined.
  • each limb mentioned in the embodiment of the present application may be divided by a joint.
  • the limb mentioned in the embodiment of the present application may include a head, a hand, an upper arm, a lower arm, a thigh, and a lower leg. Department and so on.
  • the living body mentioned in the embodiment of the present application may be a person, and of course, other living bodies, such as cats, dogs, elephants, and birds.
  • the target living body may be segmented from the image in advance. Further, a pointing vector of the target point on the target living body to the at least one joint is determined, and a positional relationship between the target point and the at least one pixel point is determined.
  • the target point refers to a target pixel point
  • the at least one pixel point may be an adjacent pixel point of the target pixel point.
  • the limb joint of the living body can be determined; according to the vector field of the limb joint of the living body, the connection relationship of the limb joint of the living body is determined; according to the connection relationship of the limb joint of the living body, from the image Split the target living body.
  • the living body can be segmented by using Part Affinity Fields (PAFs).
  • PAFs Part Affinity Fields
  • a in FIG. 3 may be configured as a Part Confidence Maps as shown by b in FIG. 3, and further configured as a limb-associated field as shown by c in FIG. 3 ( Part Affinity Fields (PAF), so that the human body can be segmented according to the limb-associated field shown by c in FIG. 3, as shown by d in FIG.
  • PAF Part Affinity Fields
  • the joint portion of the human body for example, the wrist joint, the elbow joint, the shoulder joint, etc.
  • CNN Convolutional Neural Network
  • the body joint confidence map can be obtained for the human body k Value at position p It can be constructed as follows:
  • x j,k is the groundtruth position of the limb j of the human body k in the image
  • ⁇ defines an extension of the peak, wherein the peak corresponds to each visible limb of each human body.
  • the limb-associated field refers to the pointing relationship between the limb joints, for example, the shoulder joint points to the elbow joint, and the elbow joint points to the wrist joint.
  • x j1,k and x j2,k are the true positions of the joints j 1 and j 2 of the limb c connecting the human body k
  • the limb-associated vector field of the point p can be defined according to the following formula 2:
  • the set of points on the limb c can be defined as points within the line segment within the distance threshold, which satisfy the following Equation 3 and Equation 4:
  • limb width ⁇ l is the pixel distance
  • I the length of the limb
  • v ⁇ is a vector perpendicular to v.
  • connection relationship of the limb joint of the living body can be determined; and the connection relationship of the limb joint of the living body is segmented from the image according to the connection relationship of the limb joint of the living body The target living body.
  • the pointing vector of the target point to the at least one joint may be determined according to the pointing relationship of the limb joint of the target living body.
  • a pointing vector at any point in the lower arm to the joint can be obtained.
  • the target living body before the target living body is segmented from the image according to the connection relationship of the limb joint of the living body, the target is initially segmented from the image by using thermal imaging.
  • FIG. 7 shows a human body image acquired by means of thermal imaging.
  • the target living body may be initially segmented without using thermal imaging.
  • the living body may be segmented based on a minimum spanning tree graph segmentation method. Specifically, the following operations may be included.
  • graph G there are n vertex v and m edges e.
  • Step 3 Starting from the block area S 0 , in the set S 0 , each vertex is a self-forming subset (each pixel is a separate area).
  • Step 5 Calculate S q by S q-1 as described below.
  • Step 6 After traversing the m connection edge lines, return the final result S m , which is S.
  • the penalty coefficient of the global energy function of the Semi-Global Matching (SGM) algorithm is adjusted.
  • the global energy function adjusted by the penalty coefficient is used to calculate the parallax of the target point.
  • the SGM in addition to using the SGM algorithm to calculate the parallax of the target point, other algorithms may be used to calculate the parallax of the target point.
  • the SGM is used as an example to illustrate how to use the SGM to calculate the target. The parallax of the point.
  • a disparity map can be formed by selecting the disparity of each pixel, and a global energy function related to the disparity map can be set to minimize the energy function to achieve optimal solution for each pixel.
  • the energy function can be as shown in the following formula 6:
  • D refers to the disparity map
  • E(D) is the energy function corresponding to the disparity map
  • p, q represents a certain pixel in the image
  • N p refers to the adjacent pixel of the pixel p
  • C(p, D p ) Refers to the cost of the pixel when the current pixel disparity is D p
  • P 1 is a penalty coefficient, which is applicable to those pixels in which the disparity value of the pixel adjacent to the pixel p differs by 1 from the disparity value of p
  • P 2 It is a penalty coefficient, which is applicable to those pixels in which the disparity values of pixels adjacent to the pixel p differ by more than one from the disparity values of p.
  • Equation 7 For each one-dimensional solution, Equation 7 below can be used:
  • r refers to a direction pointing to the current pixel p, which can be understood here as an adjacent pixel point of the pixel p in the direction.
  • L r (p,d) represents the minimum cost value when the parallax of the current pixel point p is d along the current direction.
  • the minimum value can be selected from the minimum of 4 possible candidate values:
  • the first possible value is the smallest value when the current pixel is equal to the previous pixel's disparity value.
  • the second and third types may be the smallest cost + penalty coefficient P 1 when the current pixel is 1 (more than 1 or less) from the previous pixel disparity value.
  • the fourth possibility is that the difference between the current pixel point and the previous pixel disparity value is greater than 1, and its minimum cost value + penalty coefficient P 2 .
  • the generation value of the current pixel point can also be subtracted from the minimum value of the previous pixel point when taking different disparity values. This is because L r (p,d) will grow with the right shift of the current pixel. To prevent the value from overflowing, you can keep it at a small value.
  • C(p,d) can be calculated by the following formulas 8 and 9:
  • the multiple values for example, the cumulative value of the value of the eight directions, and the disparity value with the smallest accumulated value may be selected as the final disparity value of the pixel, for example, Accumulate by Equation 10 below:
  • the penalty coefficient P 1 and the penalty coefficient P 2 can be set larger, so that the target pixel point and the adjacent pixel can be increased.
  • the pixel points adopt the same probability of parallax. If the difference between the target pixel point and the adjacent pixel point is expected to be large, the penalty coefficient P 2 can be set smaller, and the penalty coefficient P 1 can be set larger.
  • the probability that the large target pixel points and the adjacent pixel points adopt a larger difference of the parallax if the difference between the target pixel point and the adjacent pixel point is expected to be small, the penalty coefficient P 1 can be set smaller, and the penalty will be The coefficient P 2 is set larger, which increases the probability that the difference between the target pixel and the adjacent pixel takes a smaller parallax.
  • the ground shown in FIG. 5 can be used as an example for description.
  • the ground in the 2D image, the ground is in the up and down direction, the depth changes sequentially, and in the left and right direction, the depth is basically the same.
  • the penalty coefficient is adjusted according to an angle between the pointing vector and a vector corresponding to the positional relationship.
  • the modulus of the difference between the angle and the 90 degree is positively correlated with the penalty coefficient.
  • the image shown in Fig. 6 is a bevel in the direction of extension of the arm, that is, in the direction from the elbow joint to the wrist joint, the distance from the lens is gradually changed, and the depth is changed, analogous to Fig. 5
  • the algorithm tends to choose different parallaxes in the direction of the elbow joint to the wrist joint.
  • the direction perpendicular to the arm, perpendicular to the elbow joint to the wrist joint is basically at a distance, that is, the depth is substantially the same, analogous to the left and right direction of the ground in Figure 5, dynamically increasing the penalty parameters P 1 and P 2
  • the algorithm tends to choose similar or even the same parallax.
  • determining, according to the limb edge of the target living body, the at least one pixel point is on the target living body.
  • the method 100 of the embodiment of the present application can be used to calculate the parallax of the target point.
  • the target point is a pixel point at the edge of the limb, and the pixel used to calculate the depth information is a pixel point other than the target living body, a smaller penalty P 2 can be set, and a larger penalty coefficient is further set. P 1 , thus allowing the calculated parallax jump.
  • only the target living body segmented by the PAF method in the embodiment of the present application may be referred to, and the pixel point of the edge of the target living body is determined, and the positional relationship between the pixel based on the edge and the adjacent pixel point is determined. , adjusting the penalty coefficient, regardless of the pointing vector of the target point to at least one joint.
  • the depth information of the target living body may be calculated according to the disparity of the at least one target point.
  • the depth information of the living body can be calculated.
  • the parallax can be inversely proportional to the depth.
  • the depth can be calculated by the following formula 11:
  • d is the depth
  • b is the distance between the left and right cameras
  • f is the focal length of the camera
  • d p is the disparity
  • the first speed may be determined according to the depth of the target living body, where the direction of the first speed is a direction from the target living body to the unmanned driving device; according to the first speed And a second speed determining a control speed for controlling the unmanned device, the second speed being a speed input by the controller; and controlling the flight of the unmanned device according to the control speed.
  • the magnitude of the first velocity is inversely proportional to the depth.
  • the unmanned device may be a drone or a driverless car. The following description will be made by taking an unmanned aerial vehicle as an example.
  • the Repulsive Force Field is used to "bounce" the drone to achieve the obstacle bypass.
  • the repulsion field can be constructed by referring to the following universal gravitation formula 12, and the specific expression can refer to Equation 13.
  • D x is the depth information of the living body, and the depth of each pixel of the living body can be averaged.
  • the speed corresponding to the repulsive field points away from the direction of the living body, and the user-controlled drone has a speed.
  • the two speeds are superimposed by the vector to generate a new speed.
  • the final planned speed as the speed loop command of the control system, Eventually, the obstacles will be bypassed.
  • a pointing vector of a target point on a target living body in an image to at least one joint is determined, and a positional relationship between the target point and at least one pixel point is determined; according to the pointing vector, And the positional relationship, adjusting a penalty coefficient of the global energy function of the SGM algorithm; calculating a parallax of the target point by using a global energy function adjusted by the penalty coefficient based on the parallax of the at least one pixel point,
  • the penalty coefficient in the semi-global matching algorithm is adjusted by fully considering the features of the limb or joint of the living body, and the fixed penalty coefficient is avoided, so that the depth map calculation of the living body is more accurate.
  • a complete strategy is proposed for the low-altitude flying drone, which is used for detecting the human body and other animals, and is used for optimizing the calculation of the depth map, thereby obtaining more accurate. Obstacle observation can guarantee the safety of people, and can better realize the planning of bypass for unmanned obstacle avoidance routes.
  • FIG. 8 is a schematic block diagram of an image processing apparatus 200 according to an embodiment of the present application. As shown in FIG. 8, the device 200 includes a determining unit 210 and a calculating unit 220;
  • the determining unit 210 is configured to: determine a pointing vector of the target point on the target living body in the image to the at least one joint, and determine a positional relationship between the target point and the at least one pixel point;
  • the calculating unit 220 is configured to: adjust a penalty coefficient of a global energy function of the semi-global matching SGM algorithm according to the pointing vector and the position relationship;
  • a parallax of the target point is calculated based on a parallax of the at least one pixel point using a global energy function adjusted by the penalty coefficient.
  • the calculating unit 220 is further configured to:
  • the penalty coefficient is adjusted according to an angle between the pointing vector and a vector corresponding to the positional relationship.
  • a modulus of the difference between the angle and 90 degrees is positively correlated with the penalty coefficient.
  • the device 200 further includes a first dividing unit 230, configured to:
  • the target living body is segmented from the image according to a connection relationship of the limb joints of the living body.
  • the determining unit 210 is further configured to:
  • the determining unit 210 is further configured to:
  • the device 200 further includes a second dividing unit 240, configured to:
  • the target living body is initially segmented from the image by means of thermal imaging.
  • the device 200 further includes a control unit 250, configured to:
  • the unmanned device is controlled according to the control speed.
  • the magnitude of the first velocity is inversely proportional to the depth.
  • image processing device may implement corresponding operations in the method 100, and for brevity, no further details are provided herein.
  • FIG. 9 is a schematic block diagram of an image processing apparatus 400 according to an embodiment of the present application.
  • the image processing device 400 may include a plurality of different components that may be integrated circuits (ICs), or portions of integrated circuits, discrete electronic devices, or other suitable for use in a circuit board (such as a motherboard). Modules, or additional boards, may also be incorporated as part of a computer system.
  • ICs integrated circuits
  • circuit board such as a motherboard
  • the image processing device can include a processor 410 and a storage medium 420 coupled to the processor 410.
  • Processor 410 may include one or more general purpose processors, such as a central processing unit (CPU), or a processing device or the like.
  • the processor 410 may be a complex instruction set computing (CISC) microprocessor, a very long instruction word (VLIW) microprocessor, and implements micro-processing of multiple instruction set combinations.
  • the processor may also be one or more dedicated processors, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and a digital signal processor. , DSP).
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • DSP digital signal processor
  • Processor 410 can be in communication with storage medium 420.
  • the storage medium 420 may be a magnetic disk, an optical disk, a read only memory (ROM), a flash memory, or a phase change memory.
  • the storage medium 420 can store instructions stored by the processor and/or can cache some of the information stored from the external storage device.
  • the image processing apparatus may include a display controller and/or display device unit 430, a transceiver 440, a video input output unit 450, an audio input output unit 460, and other input and output units 470.
  • These components included in image processing device 400 may be interconnected by a bus or internal connection.
  • the transceiver 440 can be a wired transceiver or a wireless transceiver, such as a WIFI transceiver, a satellite transceiver, a Bluetooth transceiver, a wireless cellular telephone transceiver, or combinations thereof.
  • a wireless transceiver such as a WIFI transceiver, a satellite transceiver, a Bluetooth transceiver, a wireless cellular telephone transceiver, or combinations thereof.
  • the video input and output unit 450 may include an image processing subsystem such as a camera, including a photo sensor, a charge coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) light. Sensor for use in shooting functions.
  • an image processing subsystem such as a camera, including a photo sensor, a charge coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) light. Sensor for use in shooting functions.
  • CCD charge coupled device
  • CMOS complementary metal-oxide semiconductor
  • the audio input and output unit 460 may include a speaker, a microphone, an earpiece, and the like.
  • other input and output devices 470 may include a storage device, a universal serial bus (USB) port, a serial port, a parallel port, a printer, a network interface, and the like.
  • USB universal serial bus
  • the image processing device 400 can perform the operations shown in the method 100.
  • the image processing device 400 can perform the operations shown in the method 100.
  • details are not described herein again.
  • the image processing device 400 or 400 may be located in a mobile device.
  • the mobile device can be moved in any suitable environment, for example, in the air (eg, a fixed-wing aircraft, a rotorcraft, or an aircraft with neither a fixed wing nor a rotor), in water (eg, a ship or submarine), on land. (for example, a car or train), space (for example, a space plane, satellite or detector), and any combination of the above.
  • the mobile device can be an unmanned car, an auto-flying drone, a VR/AR glasses, a dual-camera mobile phone, a smart car with a vision system, and the like.
  • FIG. 10 is a schematic block diagram of a removable device 500 in accordance with an embodiment of the present application.
  • the mobile device 500 includes a carrier 510 and a load 520.
  • the description of the mobile device in Figure 14 as a drone is for illustrative purposes only.
  • the load 520 may not be connected to the mobile device via the carrier 510.
  • the removable device 500 can also include a power system 530, a sensing system 540 and a communication system 550 and an image processing device 562 and a photographing system 564.
  • Power system 530 can include an electronic governor (referred to as an ESC), one or more propellers, and one or more electric machines corresponding to one or more propellers.
  • the motor and the propeller are disposed on the corresponding arm; the electronic governor is configured to receive a driving signal generated by the flight controller, and provide a driving current to the motor according to the driving signal to control the rotation speed and/or steering of the motor.
  • the motor is used to drive the propeller to rotate to power the UAV's flight, which enables the UAV to achieve one or more degrees of freedom of motion.
  • the UAV can be rotated about one or more axes of rotation.
  • the above-described rotating shaft may include a roll axis, a pan axis, and a pitch axis.
  • the motor can be a DC motor or an AC motor.
  • the motor can be a brushless motor or a brush motor.
  • the sensing system 540 is used to measure the attitude information of the UAV, that is, the position information and state information of the UAV in space, for example, three-dimensional position, three-dimensional angle, three-dimensional speed, three-dimensional acceleration, and three-dimensional angular velocity.
  • the sensing system may include, for example, a gyroscope, an electronic compass, an Inertial Measurement Unit ("IMU"), a vision sensor, a Global Positioning System (GPS), and a barometer. At least one of them.
  • the flight controller is used to control the flight of the UAV, for example, the UAV flight can be controlled based on the attitude information measured by the sensing system. It should be understood that the flight controller may control the UAV in accordance with pre-programmed program instructions, or may control the UAV in response to one or more control commands from the operating device.
  • Communication system 550 is capable of communicating with wireless terminal 590 via a terminal device 580 having communication system 570.
  • Communication system 550 and communication system 570 can include a plurality of transmitters, receivers, and/or transceivers for wireless communication.
  • the wireless communication herein may be one-way communication, for example, only the mobile device 500 may transmit data to the terminal device 580.
  • the wireless communication may be two-way communication, and the data may be transmitted from the mobile device 500 to the terminal device 580 or may be transmitted by the terminal device 580 to the mobile device 500.
  • terminal device 580 can provide control data for one or more of mobile device 500, carrier 510, and load 520, and can receive information transmitted by mobile device 500, carrier 510, and load 520.
  • the control data provided by terminal device 580 can be used to control the status of one or more of mobile device 500, carrier 510, and load 520.
  • a carrier 510 and load 520 include a communication module for communicating with the terminal device 580.
  • the image processing device 562 included in the mobile device shown in FIG. 10 can perform the method 100, which is not described herein for brevity.

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Abstract

Certains modes de réalisation de la présente invention concernent un procédé et un appareil de traitement d'image. Dans un scénario hautement dynamique avec un sujet humain, la présente invention prend pleinement en considération les caractéristiques de membres ou d'articulations du sujet humain, améliorant ainsi la précision de calcul d'une carte de profondeur du sujet humain. Le procédé comporte les étapes consistant à: déterminer un vecteur d'orientation orienté d'un point cible sur un sujet humain cible dans une image vers au moins une articulation, et déterminer une relation de position entre le point cible et au moins un point de pixel; et calculer une parallaxe du point cible selon le vecteur d'orientation, la relation de position, et une parallaxe du ou des points de pixels, un coefficient de pénalité d'une fonction d'énergie globale d'un algorithme de SGM étant ajusté selon le vecteur d'orientation et la relation de position, et la parallaxe du point cible étant calculée sur la base de la parallaxe du ou des points de pixels en utilisant la fonction d'énergie globale avec le coefficient de pénalité ajusté.
PCT/CN2017/119291 2017-12-28 2017-12-28 Procédé et appareil de traitement d'image WO2019127192A1 (fr)

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CN201780022779.9A CN109074661A (zh) 2017-12-28 2017-12-28 图像处理方法和设备

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