The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform
Jiaya Jia - One of the best experts on this subject based on the ideXlab platform.
-
semantic segmentation with object Clique Potential
International Conference on Computer Vision, 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
-
ICCV - Semantic Segmentation with Object Clique Potential
2015 IEEE International Conference on Computer Vision (ICCV), 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
Jianping Shi - One of the best experts on this subject based on the ideXlab platform.
-
semantic segmentation with object Clique Potential
International Conference on Computer Vision, 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
-
ICCV - Semantic Segmentation with Object Clique Potential
2015 IEEE International Conference on Computer Vision (ICCV), 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
Renjie Liao - One of the best experts on this subject based on the ideXlab platform.
-
semantic segmentation with object Clique Potential
International Conference on Computer Vision, 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
-
ICCV - Semantic Segmentation with Object Clique Potential
2015 IEEE International Conference on Computer Vision (ICCV), 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
Shu Liu - One of the best experts on this subject based on the ideXlab platform.
-
semantic segmentation with object Clique Potential
International Conference on Computer Vision, 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
-
ICCV - Semantic Segmentation with Object Clique Potential
2015 IEEE International Conference on Computer Vision (ICCV), 2015Co-Authors: Jianping Shi, Shu Liu, Renjie Liao, Jiaya JiaAbstract:We propose an object Clique Potential for semantic segmentation. Our object Clique Potential addresses the misclassified object-part issues arising in solutions based on fully-convolutional networks. Our object Clique set, compared to that yielded from segment-proposal-based approaches, is with a significantly smaller size, making our method consume notably less computation. Regarding system design and model formation, our object Clique Potential can be regarded as a functional complement to local-appearance-based CRF models and works in synergy with these effective approaches for further performance improvement. Extensive experiments verify our method.
Thanh Minh Nguyen - One of the best experts on this subject based on the ideXlab platform.
-
A fuzzy logic model based Markov random field for medical image segmentation
Evolving Systems, 2013Co-Authors: Thanh Minh NguyenAbstract:Fuzzy logic incorporates human knowledge into the system via facts and rules and hence widely used in image segmentation. Another successful approach in image segmentation is the use of Markov random field (MRF) to incorporate local spatial information between neighboring pixels of an image. This paper studies the benefits of these two approaches and combines fuzzy logic with MRF to develop a new adaptive fuzzy inference system. The premise part of each fuzzy if–then rule in our approach adopts MRF to utilize the spatial constraint in an image, while the consequent part specifies the pixel distance map. Unlike other fuzzy logic models that require training data that is not always available to train the system before segmenting an image, we propose an unsupervised learning algorithm for automatic image segmentation. To impose the spatial information on the fuzzy if–then rule base, a new Clique Potential MRF function is proposed in this paper. Our approach is used to segment many medical images from simulated brain images to real brain ones with excellent results.