Dalian University of Technology

Peng Cheng Laboratory

Wei Zhong

Zhongxuan Luo

Xin Fan

Risheng Liu

Haojie Li

Zhihui Wang

Shengquan Li

Boqian Liu

Deyun Lv

Weiqiang Kong

Xinchen Ye

Lin Lin

Shenglun Chen

Jinyuan Liu

Zhuoxiao Li

Yuankai Xiang

The present invention discloses a method for infrared small target detection based on a depth map in a complex scene, and belongs to the field of target detection. An infrared image is collected, the image is binarized by using priori knowledge of a to-be-detected target and adopting a pixel value method, the binary image is further limited based on deep priori knowledge, then static and dynamic scoring strategies are formulated to score a candidate connected component in the morphologically processed image, and an infrared small target in a complex scene is detected finally. The method can screen out targets within a specific range, has high reliability; has strong robustness; is simple in program and easy to implement, can be used in sea, land, and air, and has a significant advantage under a complex jungle background.

Method for infrared small target detection based on depth map in complex scene

The present invention discloses a method for fully automatically detecting chessboard corner points, and belongs to the field of image processing and computer vision. Full automatic detection of chessboard corner points is completed by setting one or a plurality of marks with colors or certain shapes on a chessboard to mark an initial position, shooting an image and conducting corresponding processing, using a homography matrix H calculated by initial pixel coordinates of a unit grid in a pixel coordinate system and manually set world coordinates in a world coordinate system to expand outwards, and finally spreading to the whole chessboard region. The method has the advantages of simple procedure and easy implementation; the principle of expanding outwards by a homography matrix is used, so that the running speed of the algorithm is fast; and the corner points obtained by a robustness enhancement algorithm is more accurate, so that the situation of inaccurate corner point detection in the condition of complex illumination is avoided.

Method for fully automatically detecting chessboard corner points

The present invention discloses a disparity estimation optimization method based on upsampling and exact rematching, which conducts exact rematching within a small range in an optimized network, improves previous upsampling methods such as neighbor interpolation and bilinear interpolation for disparity maps or cost maps, and works out a propagation-based upsampling method by the way of network so that accurate disparity values can be better restored from disparity maps in the upsampling process.

Disparity estimation optimization method based on upsampling and exact rematching

A disparity image fusion method for multiband stereo cameras belongs to the field of image processing and computer vision. The method obtains pixel disparity confidence information by using the intermediate output of binocular disparity estimation. The confidence information can be used to judge the disparity credibility of the position and assist disparity fusion. The confidence acquisition process makes full use of the intermediate output of calculation, and can be conveniently embedded into the traditional disparity estimation process, with high calculation efficiency and simple and easy operation. In the disparity image fusion method for multiband stereo cameras proposed by the method, the disparity diagrams participating in the fusion are obtained according to the binocular images of the corresponding bands, which makes full use of the information of each band and simultaneously avoiding introducing uncertainty and errors.

Disparity image fusion method for multiband stereo cameras

The present invention belongs to the field of image processing and computer vision, and discloses an acceleration method of depth estimation for multiband stereo cameras. In the process of depth estimation, during binocular stereo matching in each band, through compression of matched images, on one hand, disparity equipotential errors caused by binocular image correction can be offset to make the matching more accurate, and on the other hand, calculation overhead is reduced. In addition, before cost aggregation, cost diagrams are transversely compressed and sparsely matched, thereby reducing the calculation overhead again. Disparity diagrams obtained under different modes are fused to obtain all-weather, more complete and more accurate depth information.

Acceleration method of depth estimation for multiband stereo cameras

The present invention discloses a method for adaptively detecting chessboard sub-pixel level corner points. Adaptive detection of chessboard sub-pixel level corner points is completed by marking position of an initial unit grid on a chessboard, using a homography matrix H calculated by pixel coordinates of four corner points of the initial unit grid in a pixel coordinate system and world coordinates in a world coordinate system to expand outwards, adaptively adjusting size of an iteration window in the process of expanding outwards, and finally spreading to the whole chessboard region.

Method for adaptively detecting chessboard sub-pixel level corner points

The present invention proposes a bi-level optimization-based infrared and visible light fusion method, adopts a pair of infrared camera and visible light camera to acquire images, and relates to the construction of a bi-level paradigm infrared and visible light image fusion algorithm, which is an infrared and visible light fusion algorithm using mathematical modeling. Binocular cameras and NVIDIA TX2 are used to construct a high-performance computing platform and to construct a high-performance solving algorithm to obtain a high-quality infrared and visible light fusion image. The system is easy to construct, and the input data can be acquired by using stereo binocular infrared and visible light cameras respectively; the program is simple and easy to implement; and the fusion image is divided into an image domain and a gradient domain for fusion by means of mathematical modeling according to different imaging principles of infrared and visible light cameras.

Bi-level optimization-based infrared and visible light fusion method

The present invention provides an infrared and visible light fusion method, and belongs to the field of image processing and computer vision. The present invention adopts a pair of infrared binocular camera and visible light binocular camera to acquire images, relates to the construction of a fusion image pyramid and a significant vision enhancement algorithm, and is an infrared and visible light fusion algorithm using multi-scale transform. The present invention uses the binocular cameras and NVIDIATX2 to construct a high-performance computing platform and to construct a high-performance solving algorithm to obtain a high-quality infrared and visible light fusion image. The present invention constructs an image pyramid by designing a filtering template according to different imaging principles of infrared and visible light cameras, obtains image information at different scales, performs image super-resolution and significant enhancement, and finally achieves real-time performance through GPU acceleration.

Infrared and visible light fusion method

The present invention discloses a large-field-angle image real-time stitching method based on calibration. First, a calibration algorithm is used to solve the positional relationship between cameras, and the prior information is used to solve a homography matrix between images. The system is easy to build, and the program is simple and easy to implement; an overlapping area ROI of images can be calculated by the homography matrix between images, and an energy model thereof can be built and solved with a graph cut algorithm; the graph cut algorithm has high time complexity and depends on the number of nodes in a graph; here, images are divided into layers, and solutions are obtained layer by layer and iterated; and finally, a stitched image is further optimized by simple linear fusion of stitching seams and histogram equalization of the stitched image.

Large-field-angle image real-time stitching method based on calibration

A multispectral camera dynamic stereo calibration algorithm is based on saliency features. The joint self-calibration method comprises the following steps: step 1: conducting de-distortion and binocular correction on an original image according to internal parameters and original external parameters of an infrared camera and a visible light camera. Step 2: Detecting the saliency of the infrared image and the visible light image respectively based on a histogram contrast method. Step 3: Extracting feature points on the infrared image and the visible light image. Step 4: Matching the feature points extracted in the previous step. Step 5: judging a feature point coverage area. Step 6: correcting the calibration result. The present invention solves the change of a positional relationship between an infrared camera and a visible light camera due to factors such as temperature, humidity and vibration.

Multispectral camera dynamic stereo calibration algorithm based on saliency features

The present invention discloses a multispectral stereo camera self-calibration algorithm based on track feature registration, and belongs to the field of image processing and computer vision. Optimal matching points are obtained by extracting and matching motion tracks of objects, and external parameters are corrected accordingly. Compared with an ordinary method, the present invention uses the tracks of moving objects as the features required for self-calibration. The advantage of using the tracks is good cross-modal robustness. In addition, direct matching of the tracks also saves the steps of extraction and matching the feature points, thereby achieving the advantages of simple operation and accurate results.

Multispectral stereo camera self-calibration algorithm based on track feature registration

The present invention discloses a multispectral camera external parameter self-calibration algorithm based on edge features, and belongs to the field of image processing and computer vision. Because a visible light camera and an infrared camera belong to different modes, fewer satisfactory point pairs are obtained by directly extracting and matching feature points. In order to solve the problem, the method starts from the edge features, and finds an optimal corresponding position of an infrared image on a visible light image through edge extraction and matching. In this way, a search range is reduced and the number of the satisfactory matched point pairs is increased, thereby more effectively conducting joint self-calibration on the infrared camera and the visible light camera. The operation is simple and results are accurate.

Multispectral camera external parameter self-calibration algorithm based on edge features

The present invention discloses a disparity image stitching and visualization method based on multiple pairs of binocular cameras. A calibration algorithm is used to solve the positional relationship between binocular cameras, and the prior information is used to solve a homography matrix between images; internal parameters and external parameters of the cameras are used to perform camera coordinate system transformation of depth images; the graph cut algorithm has high time complexity and depends on the number of nodes in a graph; the present invention divides the images into layers, and solutions are obtained layer by layer and iterated; then the homography matrix is used to perform image coordinate system transformation of the depth images, and a stitching seam is synthesized to realize seamless panoramic depth image stitching; and finally, depth information of a disparity image is superimposed on a visible light image.

Disparity image stitching and visualization method based on multiple pairs of binocular cameras

The present invention discloses a disparity estimation method for weakly supervised trusted cost propagation, which utilizes a deep learning method to optimize the initial cost obtained by the traditional method. By combining and making full use of respective advantages, the problems of false matching and difficult matching of untextured regions in the traditional method are solved, and the method for weakly supervised trusted cost propagation avoids the problem of data label dependency of the deep learning method.

Disparity estimation method for weakly supervised trusted cost propagation

The present invention discloses a method for constructing a grid map by using a binocular stereo camera. A high-performance computing platform is constructed by using a binocular camera and a GPU, and a high-performance solving algorithm is constructed to obtain a high-quality grid map containing three-dimensional information. The system in the present invention is easy to construct, so the input data may be collected by using the binocular stereo camera; the program is simple and easy to implement. According to the present invention, the grid height is calculated by using spatial prior information and statistical knowledge, so that a three-dimensional result is more robust; and according to the present invention, the adaptive threshold of grids is solved by using spatial geometry, filtering and screening of the grids are completed, and thus the generalization ability and robustness of the algorithm are improved.

Growing community of inventors

Liaoning, China

Wei Zhong