The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective s...The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.展开更多
In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based...In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based on the prior knowledge of work- pieces is presented, in which the contour of a work-piece is chosen as the major feature and the corresponding template of the edges is created. Secondly, a bottom-up salient region estimation algorithm is proposed, where the image boundaries are labelled as background queries, and the salient region can be detected by computing contrast against image boundary. Finally, the calibration method for vision system with telecentric lens is discussed, and the dimensions of the work-pieces are measured. In addition, strategies such as image pyramids and a stopping criterion are adopted to speed-up the algorithm. An automatic system embedded with the proposed detection and measurement algorithm combining top-down and bottom-up saliency （DM-TBS） is designed to pick out defective work-pieces without any manual auxiliary. Experiments and results demonstrate the effectiveness of the proposed method.展开更多
It is of great significance to rapidly detect targets in large-field remote sensing images,with limited computation resources.Employing relative achievements of visual attention in perception psychology,this paper pro...It is of great significance to rapidly detect targets in large-field remote sensing images,with limited computation resources.Employing relative achievements of visual attention in perception psychology,this paper proposes a hierarchical attention based model for target detection.Specifically,at the preattention stage,before getting salient regions,a fast computational approach is applied to build a saliency map.After that,the focus of attention（FOA） can be quickly obtained to indicate the salient objects.Then,at the attention stage,under the FOA guidance,the high-level visual features of the region of interest are extracted in parallel.Finally,at the post-attention stage,by integrating these parallel and independent visual attributes,a decision-template based classifier fusion strategy is proposed to discriminate the task-related targets from the other extracted salient objects.For comparison,experiments on ship detection are done for validating the effectiveness and feasibility of the proposed model.展开更多
基金supported by the National Natural Science Foundation of China(No.52174021)Key Research and Develop-ment Project of Hainan Province(No.ZDYF2022GXJS 003).
文摘The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.
基金supported by National Natural Science Foundation of China(Nos.61379097,91748131,61771471,U1613213 and 61627808)National Key Research and Development Plan of China(No.2017YFB1300202)Youth Innovation Promotion Association Chinese Academy of Sciences(CAS)(No.2015112)
文摘In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based on the prior knowledge of work- pieces is presented, in which the contour of a work-piece is chosen as the major feature and the corresponding template of the edges is created. Secondly, a bottom-up salient region estimation algorithm is proposed, where the image boundaries are labelled as background queries, and the salient region can be detected by computing contrast against image boundary. Finally, the calibration method for vision system with telecentric lens is discussed, and the dimensions of the work-pieces are measured. In addition, strategies such as image pyramids and a stopping criterion are adopted to speed-up the algorithm. An automatic system embedded with the proposed detection and measurement algorithm combining top-down and bottom-up saliency （DM-TBS） is designed to pick out defective work-pieces without any manual auxiliary. Experiments and results demonstrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China (40871157)
文摘It is of great significance to rapidly detect targets in large-field remote sensing images,with limited computation resources.Employing relative achievements of visual attention in perception psychology,this paper proposes a hierarchical attention based model for target detection.Specifically,at the preattention stage,before getting salient regions,a fast computational approach is applied to build a saliency map.After that,the focus of attention（FOA） can be quickly obtained to indicate the salient objects.Then,at the attention stage,under the FOA guidance,the high-level visual features of the region of interest are extracted in parallel.Finally,at the post-attention stage,by integrating these parallel and independent visual attributes,a decision-template based classifier fusion strategy is proposed to discriminate the task-related targets from the other extracted salient objects.For comparison,experiments on ship detection are done for validating the effectiveness and feasibility of the proposed model.
