The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors ...The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.展开更多
为自动识别视频中表情类别,提出基于面部块表情特征编码的视频表情识别方法框架。检测并精确定位视频中人脸关键点位置,以检测到的关键点为中心,提取面部显著特征块。沿着时间轴方向,对面部各特征块提取LBP-TOP(local binary pattern fr...为自动识别视频中表情类别,提出基于面部块表情特征编码的视频表情识别方法框架。检测并精确定位视频中人脸关键点位置,以检测到的关键点为中心,提取面部显著特征块。沿着时间轴方向,对面部各特征块提取LBP-TOP(local binary pattern from three orthogonal planes)动态特征描述子,将这些描述子作为表情特征并输入Adaboost分类器进行训练和识别,预测视频表情类型。在国际通用表情数据库BU-4DFE的纹理图像上进行测试,取得了81.2%的平均识别率,验证了所提算法的有效性,与同领域其它主流算法相比,其具有很强的竞争性。展开更多
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number 7906。
文摘The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.
文摘为自动识别视频中表情类别,提出基于面部块表情特征编码的视频表情识别方法框架。检测并精确定位视频中人脸关键点位置,以检测到的关键点为中心,提取面部显著特征块。沿着时间轴方向,对面部各特征块提取LBP-TOP(local binary pattern from three orthogonal planes)动态特征描述子,将这些描述子作为表情特征并输入Adaboost分类器进行训练和识别,预测视频表情类型。在国际通用表情数据库BU-4DFE的纹理图像上进行测试,取得了81.2%的平均识别率,验证了所提算法的有效性,与同领域其它主流算法相比,其具有很强的竞争性。
