The paper assesses the use of two of the most common texture information extraction techniques for the classification of polarimetric Synthetic Aperture Radar (PolSAR) images and proposes a traditional machine learnin...
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With the widespread application of vision-assisted analysis models, how to enhance images and optimize details in low-light conditions is an important research direction. In view of the limitations of existing algorit...
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One of the harder problems in facial recognition is the Single Sample per Person (SSPP) problem, where only one training image is available for a facial recognition model. Such a problem exists in practical applicatio...
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ISBN:
(纸本)9781450329231
One of the harder problems in facial recognition is the Single Sample per Person (SSPP) problem, where only one training image is available for a facial recognition model. Such a problem exists in practical applications such as the OSCARS which is a face recognition for classroom attendance checking. This study focuses on benchmarking diffierent local binary pattern based algorithms for face recognition, with the goal of finding the best suited to the purposes of such an application. The study also proposes and shows that the Sparse local binary pattern is consistently robust to variations in both changes in lighting and rotation when recognizing faces. Copyright 2014 ACM.
Microscopic image sharpness metric is very sensitive to illumination changes, so the research of sharpness metric methods which have better robustness to illumination is required. A new method which utilized the speci...
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local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images and has been very successful in image retrieval applications. In this paper, we propose a texture i...
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Automatic facial expression recognition (FER) played more and more important role in recent years for its wide range of potential applications. So, as one of the challenging tasks in intelligent system, it still has m...
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ISBN:
(纸本)9783662450482
Automatic facial expression recognition (FER) played more and more important role in recent years for its wide range of potential applications. So, as one of the challenging tasks in intelligent system, it still has many questions need to be deeply researched. Taking into account the importance of eyes and mouth for FER and the outstanding performance of local binary pattern (LBP) to extract local textures, a representation model for facial expressions based on feature blocks and LBP descriptor is proposed. The strategies of feature blocks obtaining and LBP feature extracting are analyzed in details and the recognition experiment is conducted. Experimental result shows that this algorithm has good performance.
This paper presents a simple, novel, yet highly effective approach for robust face recognition. Given LBP-like descriptors based on local accumulated pixel differences, Angular Differences (AD) and Radial Differences ...
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local co-location pattern (LCP) mining is an important branch of spatial co-location pattern mining, which aims to discover co-location patterns that prevalently co-occur in local regions. The LCPs can reveal the impl...
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Automatic context recognition enables mobile devices to adapt their configuration to different environments and situations. This paper investigates the use of acoustic cues as a means of recognising context. The major...
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ISBN:
(纸本)9781479974511
Automatic context recognition enables mobile devices to adapt their configuration to different environments and situations. This paper investigates the use of acoustic cues as a means of recognising context. The majority of existing approaches exploit Mel-scaled cepstral coefficients (MFCCs) developed for the analysis of speech signals. The hypothesis in this paper is that new features are needed in order to capture complex acoustic structure. The paper introduces the use of local binary pattern (LBP) analysis which is used to complement MFCCs with acoustic texture information. The second contribution relates to a bag-of-features extension which clusters LBPs into a small number of codewords. Both approaches outperform the current state of the art and the latter is particularly appealing for embedded applications in which computational efficiency is paramount.
By analyzing the detection accuracy and the testing speed of the local binary pattern. we propose an improved LBP algorithm and apply it in human detection. Through the signs of the comparisons among neighboring pixel...
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