A method based on local HSV image and the shape of object to recognize object is proposed for robot tracking. After the color segment, the knowledge of the shape of objects is used to recognize objects. The robot trac...A method based on local HSV image and the shape of object to recognize object is proposed for robot tracking. After the color segment, the knowledge of the shape of objects is used to recognize objects. The robot tracking result testifies the avail-ability of the method.展开更多
Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital image processing, image segmentation is an im...Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital image processing, image segmentation is an important section for computers to "understand" images and edge detection is always one of the most important methods in the field of image segmentation. Edges in color images are considered as local discontinuities both in color and spatial domains. Despite the intensive study based on integration of single-channel edge detection results, and on vector space analysis, edge detection in color images remains as a challenging issue.展开更多
It is difficult to detect dissolve accurately in video segmentation. Two new parameters AEI and IDM are computed to describe dissolve. An improved method based on the change curves of AEI and IDM is proposed to detect...It is difficult to detect dissolve accurately in video segmentation. Two new parameters AEI and IDM are computed to describe dissolve. An improved method based on the change curves of AEI and IDM is proposed to detect dissolve accurately. The experiments show that this method can detect dissolve accurately.展开更多
Somatic cell counts （SCCs） levels indicate the occurrence of infections in goat udders and are related to the productivity of goat milk, cheese and yoghurt. This work presents a segmentation method for counting soma...Somatic cell counts （SCCs） levels indicate the occurrence of infections in goat udders and are related to the productivity of goat milk, cheese and yoghurt. This work presents a segmentation method for counting somatic cells in goat milk images, intending to detect an infection known as mastiffs, which is the major cause of loss in dairy farming. The image segmentation procedure is devised by using the lab color space and the watershed transform. A large number of samples under variable preparation conditions are treated with the proposed method. A comparison between manual and the proposed technique is presented. Promising results indicates that video-microscopy systems may be employed to develop automated SCC for goat milk.展开更多
Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neur...Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neural network(CNN) features and the region-based features as the feature representation. The hierarchical features contain more global information, while the region-based features contain more local information. The combination of these two kinds of features significantly enhances the feature representation. Then the fused features are used to train a softmax classifier to produce per-pixel label assignment probability. And a fully connected conditional random field(CRF) is used as a post-processing method to improve the labeling consistency. We conduct experiments on SIFT flow dataset. The pixel accuracy and class accuracy are 84.4% and 34.86%, respectively.展开更多
Existing water hazard detection methods usually fail when the features of water surfaces are greatly changed by the surroundings, e.g., by a change in illumination. This paper proposes a novel algorithm to robustly de...Existing water hazard detection methods usually fail when the features of water surfaces are greatly changed by the surroundings, e.g., by a change in illumination. This paper proposes a novel algorithm to robustly detect different kinds of water hazards for autonomous navigation. Our algorithm combines traditional machine learning and image segmentation and uses only digital cameras, which are usually affordable, as the visual sensors. Active learning is used for automatically dealing with problems caused by the selection, labeling and classification of large numbers of training sets. Mean-shift based image segmentation is used to refine the final classification. Our experimental results show that our new algorithm can accurately detect not only ‘common’ water hazards, which usually have the features of both high brightness and low texture, but also ‘special’ water hazards that may have lots of ripples or low brightness.展开更多
基金Supported by the National 863 Program of China(No.2002AA421170).
文摘A method based on local HSV image and the shape of object to recognize object is proposed for robot tracking. After the color segment, the knowledge of the shape of objects is used to recognize objects. The robot tracking result testifies the avail-ability of the method.
基金National Natural Science Foundation of China (No.60374071)
文摘Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital image processing, image segmentation is an important section for computers to "understand" images and edge detection is always one of the most important methods in the field of image segmentation. Edges in color images are considered as local discontinuities both in color and spatial domains. Despite the intensive study based on integration of single-channel edge detection results, and on vector space analysis, edge detection in color images remains as a challenging issue.
文摘It is difficult to detect dissolve accurately in video segmentation. Two new parameters AEI and IDM are computed to describe dissolve. An improved method based on the change curves of AEI and IDM is proposed to detect dissolve accurately. The experiments show that this method can detect dissolve accurately.
文摘Somatic cell counts （SCCs） levels indicate the occurrence of infections in goat udders and are related to the productivity of goat milk, cheese and yoghurt. This work presents a segmentation method for counting somatic cells in goat milk images, intending to detect an infection known as mastiffs, which is the major cause of loss in dairy farming. The image segmentation procedure is devised by using the lab color space and the watershed transform. A large number of samples under variable preparation conditions are treated with the proposed method. A comparison between manual and the proposed technique is presented. Promising results indicates that video-microscopy systems may be employed to develop automated SCC for goat milk.
基金supported by the National Natural Science Foundation of China(Nos.U1509207,61325019,61472278,61403281 and 61572357)the Key Project of Natural Science Foundation of Tianjin(No.14JCZDJC31700)
文摘Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neural network(CNN) features and the region-based features as the feature representation. The hierarchical features contain more global information, while the region-based features contain more local information. The combination of these two kinds of features significantly enhances the feature representation. Then the fused features are used to train a softmax classifier to produce per-pixel label assignment probability. And a fully connected conditional random field(CRF) is used as a post-processing method to improve the labeling consistency. We conduct experiments on SIFT flow dataset. The pixel accuracy and class accuracy are 84.4% and 34.86%, respectively.
基金Project supported by the National Natural Science Foundation of China (Nos. 60505017 and 60534070)the Natural Science Foundation of Zhejiang Province, China (No. 2005C14008)
文摘Existing water hazard detection methods usually fail when the features of water surfaces are greatly changed by the surroundings, e.g., by a change in illumination. This paper proposes a novel algorithm to robustly detect different kinds of water hazards for autonomous navigation. Our algorithm combines traditional machine learning and image segmentation and uses only digital cameras, which are usually affordable, as the visual sensors. Active learning is used for automatically dealing with problems caused by the selection, labeling and classification of large numbers of training sets. Mean-shift based image segmentation is used to refine the final classification. Our experimental results show that our new algorithm can accurately detect not only ‘common’ water hazards, which usually have the features of both high brightness and low texture, but also ‘special’ water hazards that may have lots of ripples or low brightness.
