As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be ...As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background.To alleviate these issues,this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure,in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box,and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model,and reducing the running time by adding a total weights constraint.This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87%average Dice-Sørensen coefficient(DSC)accuracy,which is 0.94%higher than 86.93%,result of the state-of-the-art pancreas segmentation methods.Moreover,this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.展开更多
Humongous amounts of data bring various challenges to face image retrieval. This paper proposes an efficient method to solve those problems. Firstly,we use accurate facial landmark locations as shape features. Secondl...Humongous amounts of data bring various challenges to face image retrieval. This paper proposes an efficient method to solve those problems. Firstly,we use accurate facial landmark locations as shape features. Secondly, we utilise shape priors to provide discriminative texture features for convolutional neural networks. These shape and texture features are fused to make the learned representation more robust.Finally, in order to increase efficiency, a coarse-tofine search mechanism is exploited to efficiently find similar objects. Extensive experiments on the CASIAWeb Face, MSRA-CFW, and LFW datasets illustrate the superiority of our method.展开更多
A shot presents a contiguous action recorded by an uninterrupted camera operation and frames within a shot keep spatio-temporal coherence. Segmenting a serial video stream file into meaningful shots is the first pass ...A shot presents a contiguous action recorded by an uninterrupted camera operation and frames within a shot keep spatio-temporal coherence. Segmenting a serial video stream file into meaningful shots is the first pass for the task of video analysis, content-based video understanding. In this paper, a novel scheme based on improved two-dimensional entropy is proposed to complete the partition of video shots. Firstly, shot transition candidates are detected using a two-pass algorithm： a coarse searching pass and a fine searching pass. Secondly, with the character of two-dimensional entropy of the image, correctly detected transition candidates are further classified into different transition types whereas those falsely detected shot breaks are distinguished and removed. Finally, the boundary of gradual transition can be precisely located by merging the characters of two-dimensional entropy of the image into the gradual transition. A large number of video sequences are used to test our system performance and promising results are obtained.展开更多
基金supported by the National Natural Science Foundation of China[61772242,61976106,61572239]the China Postdoctoral Science Foundation[2017M611737]+3 种基金the Six Talent Peaks Project in Jiangsu Province[DZXX-122]the Jiangsu Province EmergencyManagement Science and Technology Project[YJGL-TG-2020-8]the Key Research and Development Plan of Zhenjiang City[SH2020011]Postgraduate Innovation Fund of Jiangsu Province[KYCX18_2257].
文摘As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background.To alleviate these issues,this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure,in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box,and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model,and reducing the running time by adding a total weights constraint.This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87%average Dice-Sørensen coefficient(DSC)accuracy,which is 0.94%higher than 86.93%,result of the state-of-the-art pancreas segmentation methods.Moreover,this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.
文摘Humongous amounts of data bring various challenges to face image retrieval. This paper proposes an efficient method to solve those problems. Firstly,we use accurate facial landmark locations as shape features. Secondly, we utilise shape priors to provide discriminative texture features for convolutional neural networks. These shape and texture features are fused to make the learned representation more robust.Finally, in order to increase efficiency, a coarse-tofine search mechanism is exploited to efficiently find similar objects. Extensive experiments on the CASIAWeb Face, MSRA-CFW, and LFW datasets illustrate the superiority of our method.
基金Supported by the National Natural Science Foundation of China (Grant No.60675017)National Basic Research Program of China (Grant No.2006CB303103)
文摘A shot presents a contiguous action recorded by an uninterrupted camera operation and frames within a shot keep spatio-temporal coherence. Segmenting a serial video stream file into meaningful shots is the first pass for the task of video analysis, content-based video understanding. In this paper, a novel scheme based on improved two-dimensional entropy is proposed to complete the partition of video shots. Firstly, shot transition candidates are detected using a two-pass algorithm： a coarse searching pass and a fine searching pass. Secondly, with the character of two-dimensional entropy of the image, correctly detected transition candidates are further classified into different transition types whereas those falsely detected shot breaks are distinguished and removed. Finally, the boundary of gradual transition can be precisely located by merging the characters of two-dimensional entropy of the image into the gradual transition. A large number of video sequences are used to test our system performance and promising results are obtained.
