Abstract: The present disclosure relates to an image processing method, an imaging processing device and a storage medium. The method includes: acquiring a blurry image exposed in exposure time and event data sampled in the exposure time, wherein the event data is configured to reflect a luminance change of a pixel point in the blurry image; determining a global event feature in the exposure time according to the event data; and determining a sharp image corresponding to the blurry image according to the blurry image, the event data, and the global event feature.

Abstract: A method for training an image generation network, an electronic device and a storage medium are provided. The method includes: obtaining a sample image, where the sample image includes a first sample image and a second sample image corresponding to the first sample image; processing the first sample image based on an image generation network to obtain a predicted target image; determining a difference loss between the predicted target image and the second sample image; and training the image generation network based on the difference loss to obtain a trained image generation network.

Abstract: An object detection method and a neural network system for object detection are disclosed. The object detection method acquires a current frame of a sequence of frames representing an image sequence, and extracts a feature map of the current frame. The extracted feature map is pooled with information of a pooled feature map of a previous frame to thereby obtain a pooled feature map of the current frame. An object is detected from the pooled feature map of the current frame. A dynamic vision sensor (DVS) may be utilized to provide the sequence of frames. Improved object detection accuracy may be realized, particularly when object movement speed is slow.

Abstract: Disclosed is a co-segmentation method and apparatus for a three-dimensional model set, which includes: obtaining a super patch set for the three-dimensional model set which includes at least two three-dimensional models, each of the three-dimensional models including at least two super patches; obtaining a consistent affinity propagation model according to a first predefined condition and a conventional affinity propagation model, the consistent affinity propagation model being constraint by the first predefined condition which is position information for at least two super patches that are in the super patch set and belong to a common three-dimensional model set; converting the consistent affinity propagation model into a consistent convergence affinity propagation model; clustering the super patch set through the consistent convergence affinity propagation model to generate a co-segmentation outcome for the three-dimensional model set.

Abstract: The disclosure provides an object detection method and apparatus based on a Dynamic Vision Sensor (DVS). The method includes the following operations of: acquiring a plurality of image frames by a DVS; and, detecting the image frames by a recurrent coherent network to acquire a candidate box for objects to be detected, wherein the recurrent coherent network comprising a frame detection network model and a candidate graph model. By using a new recurrent coherent detection network, a bounding box for an object to be detected is fast detected from the data acquired by a DVS. The detection speed is improved greatly while ensuring the detection accuracy.

Abstract: A method and apparatus for processing a binocular disparity image are provided. A method of determining a disparity of a binocular disparity image that includes a left eye image and a right eye image includes acquiring features of a plurality of pixels of the binocular disparity image based on an event distribution of the binocular disparity image, calculating a cost matrix of matching respective pixels between the left eye image and the right eye image based on the features, and determining a disparity of each matched pair of pixels based on the cost matrix.

Abstract: Exemplary embodiments provide an image vision processing method, device and equipment and relate to: determining parallax and depth information of event pixel points in a dual-camera frame image acquired by Dynamic Vision Sensors; determining multiple neighboring event pixel points of each non-event pixel point in the dual-camera frame image; determining, according to location information of each neighboring event pixel point of each non-event pixel point, depth information of the non-event pixel point; and performing processing according to the depth information of each pixel point in the dual-camera frame image. Since non-event pixel points are not required to participate in the matching of pixel points, even if it is difficult to distinguish between the non-event pixel points or the non-event pixel points are occluded, depth information of the non-event pixel points can be accurately determined according to the location information of neighboring event pixel points.

Abstract: A method and apparatus for acquiring an image disparity are provided. The method may include acquiring, from dynamic vision sensors, a first image having a first view of an object and a second image having a second view of the object; calculating a cost within a preset disparity range of an event of first image and a corresponding event of the second image; calculating an intermediate disparity of the event of the first image and an intermediate disparity of the event of the second image based on the cost; determining whether the event of the first image is a matched event based on the intermediate disparity of the event of the first image and the intermediate disparity of the event of the second image; and predicting optimal disparities of all events of the first image based on an intermediate disparity of the matched event of the first imaged.

Abstract: The present invention provides a method for video matting via sparse and low-rank representation, which firstly selects frames which represent video characteristics in input video as keyframes, then trains a dictionary according to known pixels in the keyframes, next obtains a reconstruction coefficient satisfying the restriction of low-rank, sparse and non-negative according to the dictionary, and sets the non-local relationship matrix between each pixel in the input video according to the reconstruction coefficient, meanwhile sets the Laplace matrix between multiple frames, obtains a video alpha matte of the input video, according to ? values of the known pixels of the input video and ? values of sample points in the dictionary, the non-local relationship matrix and the Laplace matrix; and finally extracts a foreground object in the input video according to the video alpha matte, therefore improving quality of the extracted foreground object.

Abstract: An object detection method and a neural network system for object detection are disclosed. The object detection method acquires a current frame of a sequence of frames representing an image sequence, and extracts a feature map of the current frame. The extracted feature map is pooled with information of a pooled feature map of a previous frame to thereby obtain a pooled feature map of the current frame. An object is detected from the pooled feature map of the current frame. A dynamic vision sensor (DVS) may be utilized to provide the sequence of frames. Improved object detection accuracy may be realized, particularly when object movement speed is slow.

Abstract: Disclosed is a co-segmentation method and apparatus for a three-dimensional model set, which includes: obtaining a super patch set for the three-dimensional model set which includes at least two three-dimensional models, each of the three-dimensional models including at least two super patches; obtaining a consistent affinity propagation model according to a first predefined condition and a conventional affinity propagation model, the consistent affinity propagation model being constraint by the first predefined condition which is position information for at least two super patches that are in the super patch set and belong to a common three-dimensional model set; converting the consistent affinity propagation model into a consistent convergence affinity propagation model; clustering the super patch set through the consistent convergence affinity propagation model to generate a co-segmentation outcome for the three-dimensional model set.

Abstract: The invention discloses a deep learning based method for three dimensional (3D) model triangular facet feature learning and classifying and an apparatus. The method includes: constructing a deep convolutional neural network (CNN) feature learning model; training the deep CNN feature learning model; extracting a feature from, and constructing a feature vector for, a 3D model triangular facet having no class label, and reconstructing a feature in the constructed feature vector using a bag-of-words algorithm; determining an output feature corresponding to the 3D model triangular facet having no class label according to the trained deep CNN feature learning model and an initial feature corresponding to the 3D model triangular facet having no class label; and performing classification. The method enhances the capability to describe 3D model triangular facets, thereby ensuring the accuracy of 3D model triangular facet feature learning and classifying results.

Abstract: The invention provides a data driven method for transferring indoor scene layout and color style, including: preprocessing images in an indoor image data set, which includes manually labeling semantic information and layout information; obtaining indoor layout and color rules on the data set by learning algorithms; performing object-level semantic segmentation on input indoor reference image, or performing object-level and component-level segmentations using color segmentation methods, to extract layout constraints and color constraints of reference images, associating the reference images with indoor 3D scene via the semantic information; constructing a graph model for indoor reference image scene and indoor 3D scene to express indoor scene layout and color; performing similarity measurement on the indoor scene and searching for similar images in the data set to obtain an image sequence with gradient layouts from reference images to input 3D scene; performing image-sequence-guided layout and color transfer g

Abstract: The disclosure provides an object detection method and apparatus based on a Dynamic Vision Sensor (DVS). The method includes the following operations of: acquiring a plurality of image frames by a DVS; and, detecting the image frames by a recurrent coherent network to acquire a candidate box for objects to be detected, wherein the recurrent coherent network comprising a frame detection network model and a candidate graph model. By using a new recurrent coherent detection network, a bounding box for an object to be detected is fast detected from the data acquired by a DVS. The detection speed is improved greatly while ensuring the detection accuracy.

Abstract: Exemplary embodiments provide an image vision processing method, device and equipment and relate to: determining parallax and depth information of event pixel points in a dual-camera frame image acquired by Dynamic Vision Sensors; determining multiple neighboring event pixel points of each non-event pixel point in the dual-camera frame image; determining, according to location information of each neighboring event pixel point of each non-event pixel point, depth information of the non-event pixel point; and performing processing according to the depth information of each pixel point in the dual-camera frame image. Since non-event pixel points are not required to participate in the matching of pixel points, even if it is difficult to distinguish between the non-event pixel points or the non-event pixel points are occluded, depth information of the non-event pixel points can be accurately determined according to the location information of neighboring event pixel points.

Abstract: The invention discloses a deep learning based method for three dimensional (3D) model triangular facet feature learning and classifying and an apparatus. The method includes: constructing a deep convolutional neural network (CNN) feature learning model; training the deep CNN feature learning model; extracting a feature from, and constructing a feature vector for, a 3D model triangular facet having no class label, and reconstructing a feature in the constructed feature vector using a bag-of-words algorithm; determining an output feature corresponding to the 3D model triangular facet having no class label according to the trained deep CNN feature learning model and an initial feature corresponding to the 3D model triangular facet having no class label; and performing classification. The method enhances the capability to describe 3D model triangular facets, thereby ensuring the accuracy of 3D model triangular facet feature learning and classifying results.

Abstract: A method and apparatus for acquiring an image disparity are provided. The method may include acquiring, from dynamic vision sensors, a first image having a first view of an object and a second image having a second view of the object; calculating a cost within a preset disparity range of an event of first image and a corresponding event of the second image; calculating an intermediate disparity of the event of the first image and an intermediate disparity of the event of the second image based on the cost; determining whether the event of the first image is a matched event based on the intermediate disparity of the event of the first image and the intermediate disparity of the event of the second image; and predicting optimal disparities of all events of the first image based on an intermediate disparity of the matched event of the first imaged.

Abstract: A method and apparatus for processing a binocular disparity image are provided. A method of determining a disparity of a binocular disparity image that includes a left eye image and a right eye image includes acquiring features of a plurality of pixels of the binocular disparity image based on an event distribution of the binocular disparity image, calculating a cost matrix of matching respective pixels between the left eye image and the right eye image based on the features, and determining a disparity of each matched pair of pixels based on the cost matrix.

Abstract: The present invention provides a method for video matting via sparse and low-rank representation, which firstly selects frames which represent video characteristics in input video as keyframes, then trains a dictionary according to known pixels in the keyframes, next obtains a reconstruction coefficient satisfying the restriction of low-rank, sparse and non-negative according to the dictionary, and sets the non-local relationship matrix between each pixel in the input video according to the reconstruction coefficient, meanwhile sets the Laplace matrix between multiple frames, obtains a video alpha matte of the input video, according to ? values of the known pixels of the input video and ? values of sample points in the dictionary, the non-local relationship matrix and the Laplace matrix; and finally extracts a foreground object in the input video according to the video alpha matte, therefore improving quality of the extracted foreground object.

Abstract: A method of integrating binocular stereo video scenes with maintaining time consistency includes: propagating and extracting a contour of moving object of stereo video A; integrating and deformating of parallax between moving object and dynamic scene with time consistency; color blending of moving object and dynamic scene with time consistency where a method of median coordinate fusion is utilized. The method is simple and effective to utilize a small quantity of user interactions to successfully extract moving objects from stereo video which are same in time and as consistent as possible between left view and right view to develop multiple constraint conditions to guide the integration and deformation of parallax of moving object and dynamic scene and allow moving object to conform to the rules of perspective of dynamic scene. Moreover, the deformation result of moving object is smooth and consistent and can avoid effectively the occurrence of “dithering” phenomenon.