208 results about "Vision Disparity" patented technology

The invention discloses a binocular stereoscopic vision matching method combining depth characteristics. The binocular stereoscopic vision matching method comprises: obtaining a depth characteristic pattern from left and right images through a convolutional neural network; calculating a truncation similarity measurement degree of pixel depth characteristics by taking the depth characteristics as the standard, and constructing a truncation matching cost function combining color, gradients and depth characteristics to obtain a matched cost volume; processing the matched cost volume by adopting a fixed window, a variable window and a self-adaptive weight polymerization or guide filtering method to obtain a cost volume polymerized by a matching cost; selecting an optimal parallax error of the cost volume by adopting WTA (Wireless Telephony Application) to obtain an initial parallax error pattern; then finding a shielding region by adopting a double-peak test, left-right consistency detection, sequence consistency detection or shielding constraint algorithm, and giving a shielding point to a parallax error value of a same-row point closest to the shielding point to obtain a parallax error pattern; and filtering the parallax error pattern by adopting a mean value or bilateral filter to obtain a final parallax error pattern. By adopting the binocular stereoscopic vision matching method combining the depth characteristics, the incorrect matching rate of three-dimensional matching can be effectively reduced, the images are smooth and image edges including edges of small objects are effectively kept.

The invention discloses a fast 3D face identifying method based on double-eye passive solid sight, which includes the following steps: 1) a non-contact short shaft parallel binocular stereo vision system is built by applying two high-definition digital cameras; 2) after system calibration is finished, face detection and collection based on a haar-AdaBoost sorting machine is carried out on a preview frame image for obtaining corresponding upper and lower stereoscopic vision graph pairs and estimating a sight difference; image correction is carried out on a face area for obtaining the upper and lower stereoscopic vision graph pairs vertical to the polar lines inside and outside the area; 3) the accurate location on the eyes and a spex nasi is captured by applying a Bayesian and the haar-AdaBoost sorting machines as well as point cloud 3D information for building a benchmark triangle; 4) the corresponding sub pixels in the middle and small areas are matched by applying the pyramidal parallel search solid graph of a phase relevant arithmetic based on a complex wavelet; 5) pose normalizing and hole filling are carried out on the faces under different poses by applying the built benchmark triangle; 6) expression normalization is carried out on different faces based on the suppose that the surface geodesic distance of the face is invariable; 7) the 3D faces after normalization are identified by utilizing the arithmetic. The method has the beneficial effects of: mainly solving the problems of being hard to fast and automatically obtain the passive stereoscopic vision and identifying the 3D point cloud information of the dense and accurate face under different poses and expressions, thus leading the 3D face identifying process to be faster, more hidden, safer and more reliable.

A system, device within a system, and a method for enhancing visual perception in a two-dimensional image is disclosed, providing the induction of retinal disparity in the viewer by a device with which the two-dimensional image is viewed. With fusion of the retinally disparate images of the two-dimensional image, the viewer experiences the enhancement of his or her visual perception in the two dimensional image, which includes enhancement of depth perception and enhancement in clarity perception.

The invention relates to a binocular vision positioning method for target grabbing of an underwater robot, and belongs to the field of computer vision. The method is mainly used for accurately acquiring three-dimensional information of a grabbed target when an underwater robot works. The method comprises the following steps: double-target positioning: calculating internal and external parameters of left and right cameras; target detection: positioning a target object detection frame; binocular image correction: carrying out distortion correction and stereo correction, and determining a right image target area; binocular image stereo matching: extracting image feature points, describing the feature points, performing stereo matching, and removing mismatching; and calculating the three-dimensional information of the target in the image under the left camera coordinate. According to the method, the accurate parallax value is obtained by extracting feature points, removing unstable featurepoints through non-maximum suppression, constructing a binary descriptor, matching the feature points and removing mismatching. Through the scheme, the binocular stereo matching robustness can be improved, and meanwhile, the three-dimensional information of the detection target can be accurately obtained, so that the real-time positioning requirement on the target when the underwater robot grabsthe target is met.

The invention discloses a method and a system based on binocular stereoscopic vision for passenger flow density estimation. The system comprises binocular parallel cameras, a DSP (Digital Signal Processor), a communication module, a video output module and the like, and the system is adopted for estimating passenger flow density. The method comprises the following steps: (1) calibrating a left camera and a right camera to obtain the internal and external parameters of the left camera and the right camera; calculating the accurate principal point difference of the left camera and the right camera; (2) collecting a left image and a right image in real time and carrying out position correction, and adopting a self-adapting window stereo matching method to obtain a current frame parallax error image; (3) splitting a foreground human body target to obtain a binary foreground human body target image; (4) generating a dimensional mapping image of the foreground human body target; (5) calculating the degree of congestion, and obtaining the density estimation through the non-linear relationship between the degree of congestion and the number of people. The method is independent of the influences of scene illumination change, shadow, perspective effect and blocking, and the system has the characteristics that the equipment is simple and the accuracy rate of the density estimation is high.

The invention discloses a terminal screen display parameter adjustment method comprising the steps that eyeball identification information is acquired, and a focusing area corresponding to the eyeball identification information is confirmed in a screen display interface; and screen display parameters of all areas of the screen display interface are adjusted respectively according to coverage scope of the focusing area so that vision disparity of the screen display interface can be realized. The invention also discloses a terminal screen display parameter adjustment device. Use duration of a terminal battery can be effectively guaranteed, and vision disparity of the terminal display interface can be realized so that the terminal display effect can be enhanced.

The invention relates to an edge-based parallax image calculation method applied to binocular stereo vision. The method includes: acquiring gradient images of a scene on the basis of depth images, shot by two depth cameras, of the same scene; acquiring a minimum matching cost value and a second minimum matching cost value of first pixel points in the gradient images according to a difference cost function; and determining that parallax of the first pixel point is invalid parallax if a ratio of the minimum matching cost value to the second minimum matching cost value is greater than a preset ratio. According to the embodiment of the invention, a low texture region or a repeated texture region corresponding to the first pixel point can be determined in the case where the ratio of the minimum matching cost value to the second minimum matching cost value is greater than the preset ratio, then it is determined that the parallax of the first pixel point is the invalid parallax, thus calculating parallax for pixel points outside high-level texture regions can be reduced, accurately calculate the parallax is more facilitated, and then an edge-based stereo matching parallax image is output on the basis of the parallax.

The invention discloses a depth estimation method based on binocular vision and laser radar fusion. The method comprises the following steps: registering data acquired by a laser radar and a binocularcamera through joint calibration; obtaining a laser radar disparity map according to a joint calibration result; obtaining a binocular disparity map through a binocular stereo matching algorithm, performing confidence analysis on the binocular disparity map, removing points with low confidence in the binocular disparity map, and obtaining the binocular disparity map after confidence processing; performing feature extraction and fusion on the obtained laser radar disparity map and the binocular disparity map after confidence processing; carrying out further feature extraction and parallax regression through a cascade hourglass structure; adopting relay supervision to utilize output of front and back cascaded hourglass structures; and outputting an accurate and dense disparity map after fusion. According to the method, a more effective network structure is designed, the features of the laser radar disparity map and the binocular disparity map are better extracted and fused, and a more accurate disparity map is obtained.

The invention discloses a serial channel fusion pedestrian detection method based on binocular vision. According to the method, a parallax image is generated according to a binocular RGB image, a convolutional neural network is constructed, an RGB image channel and a parallax image channel are connected in series to serve as input of the convolutional neural network, the complementary relation between the RGB image and the parallax image is learnt, a part with a predicted value greater than a set value is selected from a detection result output by the convolutional neural network, non-maximumsuppression is performed on the part, and a final pedestrian position is obtained. Through the method, since the RGB color image has rich color information and the parallax image is invariant to illumination, pedestrian detection can be more robust and accurate by combining the RGB image and the parallax image.

The invention provides an object detection method and a device. The object detection method comprises steps that: disparity images are acquired from a stereo camera for object area imaging; planes are detected from the disparity images; object surface candidates are selected from the planes detected from the disparity images according to characteristics of a physical surface of an object; the object surface candidates are converted into front view surfaces, and the front view surfaces are object surface images acquired by utilizing the stereo camera to shoot from a visual angle just pointing to the physical surface of the object; the object is detected at least partially on the basis of the front view surfaces after conversion. According to the method and the device, influence of original deformation can be eliminated, the object is detected by utilizing already-extracted non-deformation characteristics and/or a classifier and on the basis of the front view surfaces after conversion.

The invention provides a binocular image super-resolution method based on multi-scale feature fusion. Firstly, a feature extraction module adopts mixed jump type residual connection, pooling blocks of an improved void space pyramid form a loopback structure for extracting multi-scale features of an image, and then an expansion residual and the loopback structure are alternately cascaded to fuse the extracted features; then, a parallax attention module is introduced to obtain a corresponding relation in the binocular image, useful information of an integrated image pair is integrated, and a transitional expansion residual block is used to learn the network capability of stereo matching features to obtain a parallax image of the binocular image; and finally, mapping a low-dimensional space to a high-dimensional space by using four expanded residual blocks, reconstructing a super-resolution left (right) graph through sub-pixel convolution, and applying FReLU to the whole network to improve the efficiency of capturing spatial correlation. According to the method, the multi-scale features of the image are extracted by using the expansion residual loop structure, the excellent super-resolution performance is realized, and the method has wide applicability.

The invention discloses a method for detecting the overall dimension of a running vehicle based on binocular vision. The method comprises the following steps: calibrating and correcting a binocular camera; performing moving object recognition and tracking on the corrected view to obtain a vehicle feature region; enabling the identified vehicle surface to be subjected to texture enhancement processing, so that the problem of low detection precision of a weak texture surface is solved; based on vehicle driving scene characteristics, providing a stereo matching algorithm based on time sequence propagation to generate a standard disparity map, and improving the vehicle overall dimension measurement precision; performing three-dimensional reconstruction on the generated disparity map to generate a point cloud map; providing a space coordinate fitting algorithm, fitting a plurality of frames of point cloud images of the tracked vehicle, generating a standard vehicle overall dimension image. The problem that the overall dimension of the vehicle cannot be completely displayed through a single frame of point cloud image is solved. The method is not limited by the vehicle speed in measurement effect, high in measurement precision, wide in measurement range and low in cost. The binocular camera has the advantages of being flexible in structure, convenient to install and suitable for measurement of all road sections.

The invention provides an underwater environment three-dimensional reconstruction method based on binocular vision, and the method comprises the following steps: 1, collecting and obtaining an underwater image, carrying out the underwater calibration of a binocular camera, and obtaining the needed related parameters of the binocular camera; 2, preprocessing the collected underwater image, including image denoising, image enhancement, image sharpening, image restoration and underwater image defogging; 3, performing feature detection on the preprocessed binocular image in the step 2, and performing stereo matching by using an improved Census and NCC fused stereo matching algorithm to obtain a disparity map containing depth information; and 4, performing three-dimensional reconstruction on the disparity map in the step 3 by using a PCL three-dimensional reconstruction method introducing a moving least square method, and restoring an underwater three-dimensional environment in the image. The invention introduces the moving least square method to solve the problems of point cloud discretization and point cloud vulnerability, thus visually reflecting a three-dimensional effect by a processing effect from multiple angles, and restoring the underwater three-dimensional environment.

The invention relates to a stereoscopic-vision-based rapid workpiece sorting method and device. A conveyor belt is arranged on a working platform. A binocular camera of which the base line is parallelto the moving direction of the conveyor belt is arranged above the working platform. The side portion of the working platform is provided with a sorting module comprising a mechanical arm. A controller is connected with a displayer, the sorting module, the conveyor belt and the binocular camera. An ROI area is determined through a binocular image. Coordinates and a parallax error of a workpiece in a depth map are obtained through stereo matching, and time consumption is reduced. According to the conveying speed of the conveyor belt and the time interval of former and latter frames of images,the corresponding position of the workpiece in the latter frame of the image is calculated on the basis of the position of the workpiece in the former frame of the depth map. The error is corrected and the correctness is improved through stereo matching at intervals of M frames. Through the method and the device, the stereo matching area is reduced; the calculated quantity is small; there is no need to conduct stereo matching on each frame of the image; the time consumption is reduced; the practicality is high; stereo matching of sorting can be rapidly implemented on relatively low-price hardware equipment with poor performance; the hardware dependency is reduced; and compared with a similar hardware platform, the sorting success rate is increased, and the production efficiency is high.

The invention discloses a suspension preview control method based on a binocular vision technology. The suspension preview control method comprises the following steps: S1, acquiring a pavement image in a driving direction in front of a vehicle in real time; S2, identifying the road surface image by using a target detection algorithm, and judging whether a deceleration strip or pit instantaneous road surface excitation exists or not; S3, obtaining a parallax error by combining a stereo matching algorithm with the position information of the deceleration strip or the pit; S4, estimating the distance of the deceleration strip or the pit according to a binocular distance measurement algorithm in combination with the internal and external parameters of the binocular camera and the parallax value, and calculating the time when the vehicle arrives at the deceleration strip or the pit by the vehicle ECU according to the vehicle speed information; S5, designing a suspension preview controller according to a model predictive control principle, and switching to the suspension preview controller in advance before the estimated time when the vehicle arrives at the deceleration strip or the pit. The invention further provides a suspension control device. According to the invention, the arrangement condition of the suspension of the vehicle is matched with the driving condition of the vehicle, and the driving comfort and safety of the vehicle are improved.

The invention belongs to the technical field of image processing, and discloses an unsupervised monocular view depth estimation method based on multi-scale unification, and the method comprises the following steps: S1, carrying out the pyramid multi-scale processing of an input stereo image pair; s2, constructing a network framework for encoding and decoding; s3, transmitting the features extracted in the encoding stage to a reverse convolutional neural network to realize feature extraction of input images of different scales; s4, performing unified up-sampling on the disparity maps of different scales to an original input size; s5, performing image reconstruction by using the input original image and the corresponding disparity map; s6, constraining the accuracy of image reconstruction; s7, training a network model by adopting a gradient descent method; and S8, fitting a corresponding disparity map according to the input image and a pre-training model. According to the method, networktraining does not need to be supervised by using real depth data, the binocular image which is easy to obtain is used as a training sample, the obtaining difficulty of network training is greatly reduced, and the problem of depth map holes caused by low-scale disparity map blurring is solved.

The invention discloses a method for automatically detecting obstacles based on binocular vision. The method comprises the following steps: receiving image information from two paths of binocular vision cameras, the image information including a V-disparity map and a U-disparity map; calculating a conversion formula between image coordinates of the image information and coordinate systems according to the coordinate systems of the two paths of cameras; comparing image information that identical time codes of the received image information correspond to, and obtaining a disparity map; and obtaining related lines of the disparity map according to the disparity map, and obtaining the position information of obstacles according to the conversion formula and the related lines of the disparity map. The method provided by the invention can utilize binocular vision to simulate eyes of humans and the process of stereoscopic perception of humans, and automatically detect obstacles for road driving, and the method has strong adaptability, good real-time performance, and good concealing performance.