137 results about "Salient object detection" patented technology

The invention discloses an RGB-D salient object detection method based on foreground and background optimization. The method comprises the following steps: initial foreground modeling is performed based on low-level feature contrast, and a superpixel-level initial salient figure is obtained; a middle-level aggregation processing is performed on the superpixel-level initial salient figure, and a middle-level salient figure is obtained; a high-level prior is introduced in the middle-level salient figure to improve the detection effect, and a foreground probability is generated; edge connectivity mixing depth information is calculated, and the edge connectivity is converted into a background probability; the foreground probability and the background probability are optimized, and a objective function is obtained; the objective function is solved, a optimal salient figure is obtained, and the detection of a salient object is realized. According to the invention, a optimization framework based on foreground and background measurement and the depth information of a scene is fully utilized by the invention, a high recall rate can be obtained, and the accuracy is high; the method can accurately position the salient object in different scenes and different sizes of objects and can also obtain nearly equal salience values in the target object.

Systems, methods, and computer-readable media are provided for generating an image processing effect via disparity-guided salient object detection. In some examples, a system can detect a set of superpixels in an image; identify, based on a disparity map generated for the image, an image region containing at least a portion of a foreground of the image; calculate foreground queries identifying superpixels in the image region having higher saliency values than other superpixels in the image region; rank a relevance between each superpixel and one or more foreground queries; generate a saliency map for the image based on the ranking of the relevance between each superpixel and the one or more foreground queries; and generate, based on the saliency map, an output image having an effect applied to a portion of the output image.

An attention mechanism based salient object detection method relates to the fields of computer vision and digital image processing. The method comprises that a top down attention network is designed to purify different layers of convolution characteristics, a fusion network with characteristics of a residual error of second order term design is used to maintain characteristics of the residual error better, a saliency map in which any static color image serves as input and the size of output is consistent with that of the input image, and in the saliency map, white represents areas of salient objects, and black represents background areas. Thus, the saliency map of high resolution can be obtained, and small salient objects can be detected more effectively.

The invention relates to a video saliency object detection method and system based on a cross attention mechanism. The method comprises the following steps: A, inputting an input adjacent frame imageinto a similar network structure sharing parameters, and extracting high-level and low-level features; b, performing feature re-registration and alignment on the saliency features in the single-frameimage by using a self-attention module; c, utilizing an inter-frame cross attention mechanism to obtain the relationship dependence on the position of the salient object on the inter-frame space-timerelationship, acting on the advanced feature as a weight, and capturing the consistency of salient object detection on the space-time relationship; d, fusing the extracted intra-frame advanced features and low-level features of the adjacent frames and the space-time features with the inter-frame dependency relationship; e, performing feature dimension reduction on the input features, and outputting a pixel-level classification result by using a classifier; and F, establishing a depth video saliency object detection model based on a cross attention mechanism, and accelerating the training of the model by using GPU parallel computing.

A salient object detection method based on histogram power function fitting comprises the four steps of the histogram power function fitting, a superpixel classification, a salient region problem and salient object detection. The salient object detection method based on the histogram power function fitting has the advantages that gray threshold is obtained through using an FT saliency map, a figure manifold sorting method, an SLIC method superpixel classification and the histogram power function fitting, under the situations of multiple images, multiple targets and complex scenes, the detection efficiency is high, the performance is good, the precision is good, the problem in salient object detection fields is solved, by means of the salient object detection method based on the histogram power function fitting, the execution speed is high, and the complexity of algorithm is low; meanwhile, higher detection precision can be guaranteed.

The invention relates to an image saliency target detection method. Region segmentation of image analysis is involved. The method is an image saliency detection method based on multi-graph model priorand short connection network optimization. The method comprises the following steps of: utilizing color and position information for each input image; calculating a KNN graph model and a K regular graph model; obtaining a saliency map S1 under the KNN graph model and a saliency map S2 under the K regular graph model; carrying out pixel-level fusion on the KNN graph model and the K regular graph model; S3, obtaining an initial saliency map of the original image; and S3, optimizing the initial saliency map by using a short connection network to obtain a final saliency map Sfinal of the originalimage, and completing image saliency target detection, thereby overcoming the defects of incomplete saliency target detection and inaccurate algorithm detection when foreground and background colorsare similar in the prior art of image saliency target detection.

The invention provides an automatic detection method of a salient object based on salience density and edge response, and relates to a method for automatically detecting the salient object, solving the problems of the convectional salient object detection method that only one attribute that is the salience is utilized, but the edge attribute of the salient object is not taken into account, therefore, the detection accuracy of the salient object is relatively low. The automatic detection method of the salient object based on the salience density and the edge response comprises the following steps of: calculating and generating a salient map S of an input map according to the regional salience calculation method in combination of the global color comparison and the color space distribution; generating an edge response map E on the salient map S by utilizing a group of Gabor filters; efficiently searching a global optimal sub-window containing the salient object in the input map by utilizing the maximized branch-and-bound algorithm of the salience density and the edge response; adopting the obtained optimal sub-window as the input; initializing the GrabCut graphic cutting method; carrying out the GrabCut graphic cutting method; and automatically extracting the salient object with a good edge. The automatic detection method is applicable to the image processing field.

The invention discloses a salient object detection method based on multi-level context information fusion. The object of the method is to construct and utilize multi-level context features to performimage saliency detection. According to the method, a new convolutional neural network architecture is designed, and the new convolutional neural network architecture is optimized in a manner of convolution from a high layer to a bottom layer, so that the context information on different scales is extracted for an image, and the context information is fused to obtain a high-quality image saliency map. The salient region detected by using the method can be used for assisting other visual tasks.

The invention discloses an optical field salient target detection method based on a depth convolution network. The method comprises the following steps: 1. Converting a sub-aperture image of all viewing angles from optical field data obtained by using an optical field acquisition device; 2. Reconstructing the sub-aperture images into microlens images from different angles of view; 3, perform dataenhancement on that microlens image; With Deeplab- Based on the pre-training weights of V2 network, a salient object detection model combined with microlens image is constructed and trained by data set. 5. Using the trained salient object detection model to detect the salient object. The method of the invention can effectively improve the accuracy of the detection of the salient object of the complex scene image.

The invention discloses an amplitude spectrum analysis based salient object detection method, which comprises the steps of extracting a brightness feature I, an antagonistic feature RG and an antagonistic feature BY from an acquired image; the extracted features are transformed into a frequency domain through quaternion Fourier transform so as to acquire an amplitude spectrum, a phase spectrum and an intrinsic axis spectrum of the image; detecting the size of each salient object and a central position of each salient object in the image by using an image signature operator; acquiring an optimal filtering scale corresponding to each of the different salient object through utilizing a specific relation between the optimal filtering scale of the amplitude spectrum and the size of the salient objects, and respectively carrying out different scales of Gaussian filtering on the amplitude spectrum of the image; determining a weight value of an optimal saliency map corresponding to each salient object according to central bias Gaussian distribution and salient object locations, carrying out adaptive Gaussian weight fusion on the acquired different saliency maps, and calculating a fused saliency map; carrying out Gaussian filtering on the fused saliency map; and performing normalization on saliency values to acquire a final saliency map. The method disclosed by the invention can suppress the background quickly and effectively, the salient objects are highlighted uniformly, and more salient information of the image is reserved.

The invention discloses an SAR and optical image fusion method for landslide detection. The method provided by the invention comprises the following steps: step 1, preprocessing an SAR and an opticalimage; step 2, performing HIS conversion on the optical image to obtain three components I, H and S; step 3, performing stationary wavelet transform and high-frequency component energy maximization fusion on I components of the SAR image and the optical image; step 4, performing saliency detection on the low-frequency and high-frequency components of the SAR image and the gray scale information ofthe image, establishing an SAR salient target detection area guidance function, and partitioning the SAR image; step 5, establishing a salient region fusion rule, and realizing image fusion accordingto a regional fusion strategy; and step 6, identifying and extracting landslide disaster information based on the fused image. The method has good adaptability to SAR and optical image fusion for landslide detection, adopts related processing measures in structure maintenance, noise removal and spectrum reservation, and obtains an excellent effect.