We characterize the algorithmic dimensions (i.e., the lower and upper asymptotic densities of information) of infinite binary sequences in terms of the inability of learning functions having an algorithmic constraint ...
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local binary pattern (LBP) is very useful in various applications of machine vision problems. It is an effective multi-resolution texture descriptor. Various types of local binary patterns have been proposed till now....
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local binary pattern (LBP) is very useful in various applications of machine vision problems. It is an effective multi-resolution texture descriptor. Various types of local binary patterns have been proposed till now. Even though deep learning framework has gained more attention in generic object recognition nowadays, in this article, we studied the effectiveness of LBP features in the transform domain for the task of object recognition. The comparison was done based on the performance measure such as recall, precision, equal error rate and F-measure. Images have been pre-processed using Log Gabor filter, and then, different types of LBPs are applied on the transformed images, and the statistical features are extracted and classified with support vector machine (SVM). The evaluation is done on the Graz databases. It is observed that rotation invariant LBP and direction coded LBP perform better with lesser error rate and higher recall, precision and F-measure.
Image inpainting has made great progress with the help of deep learning. However, existing methods show performance degradation when restoring corrupted images with complex scenes. In this paper, we propose a novel im...
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ISBN:
(纸本)9781728198354
Image inpainting has made great progress with the help of deep learning. However, existing methods show performance degradation when restoring corrupted images with complex scenes. In this paper, we propose a novel image inpainting method by reducing intermediate layer information loss and fusing texture-structure features. To be specific, we first compute a local binary pattern (LBP) map of the corrupted image as the input of structure feature extraction, considering that LBP contains richer structure information than edges and contours. Then, we introduce a Wide Identical Residual Weighting (WIRW) module to utilize the intermediate layer features in the structure encoder. Furthermore, we introduce a Spatial-Transformer (ST) module consisting of Convolutional Neural Network (CNN) and Transformer branches to fuse the structure and texture features, where the CNN and Transformer branches are responsible for capturing the local and global information, respectively. Various experiments on public datasets including CelebA, Paris StreetView, and Places2 demonstrate the effectiveness of the proposed method. Especially, our ablation study separately verifies the contribution of each module to the whole framework. The code is publicly available at https://***/GZHU-DVL/LFang.
Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering...
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Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering. To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP). The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement. The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images. The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information. The detail information is protected by real-time superposition with feature information. Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics.
In the era of cybersecurity, Biometric recognition systems are used in a variety of applications, including access control and forensic investigation. This paper presents a novel biometric authentication technique in ...
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Determining gender using offline handwriting is an applied research problem in forensics, psychology and security applications. This task becomes challenging and tedious due to interpersonal and intrapersonal differen...
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Face is a widely used biometric modality because of ease with which it can be captured by digital cameras. However, because of its wider accessibility and popularity, it becomes the most vulnerable biometric modality....
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Fraud is misappropriating a profit organization's system that does not always result in immediate legal penalties. Credit card firms must be able to spot fraudulent credit card transactions so that customers aren&...
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One of the most significant and extensively studied local texture descriptors is called local binary patterns (LBP). In the studies, a comprehensive analysis of the most renowned LBP related procedures is required in ...
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Researchers have shown that 55% of concepts are conveyed through facial emotion and only 7% are conveyed by words and sentences, so facial expression plays an important role in conveying concepts in human communicatio...
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ISBN:
(纸本)9798350314557
Researchers have shown that 55% of concepts are conveyed through facial emotion and only 7% are conveyed by words and sentences, so facial expression plays an important role in conveying concepts in human communications. In recent years, due to the improvement of artificial neural networks, many studies have been conducted related to facial expression recognition. This paper presents a method based on ensemble classification using convolutional neural networks to recognize facial emotions. The concatenation of spatial features with global features is used as a feature map for the classification stage in the committee network. Two committee networks are fed separately with LBP and raw images. After training the two committee networks, to classify the emotion, the maximum probability between the two networks is considered as the final output. The proposed method was applied and tested on the FER2013 dataset. Our proposed method is more accurate than many leading methods, and in competition with the successful model that has a more complex architecture and higher computational cost, it has been able to achieve acceptable results with a simple architecture.
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