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A Deep Normalization and Convolutional Neural Network for Image Smoke Detection
It is a challenging task to recognize smoke from images due to large variance of smoke color, texture, and shapes. There are smoke detection methods that have been proposed, but most of them areExpand
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High-order local ternary patterns with locality preserving projection for smoke detection and image classification
It is a challenging task to recognize smoke from visual scenes due to large variations in the color, texture, shapes of smoke. To improve detection accuracy, we propose a novel feature extractionExpand
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Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns
Abstract Local binary patterns (LBP) have powerful discriminative capabilities. However, traditional methods with LBP histograms cannot capture spatial structures of LBP codes. To extract the spatialExpand
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Encoding pairwise Hamming distances of Local Binary Patterns for visual smoke recognition
Abstract To achieve scale invariance, existing methods based on multi-scale local binary patterns (LBP) usually concatenate histograms of LBP codes from different scales. Direct concatenation ofExpand
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Non-Linear Dimensionality Reduction and Gaussian Process Based Classification Method for Smoke Detection
To improve smoke detection accuracy, we combine local binary pattern (LBP) like features, kernel principal component analysis (KPCA), and Gaussian process regression (GPR) to propose a novel dataExpand
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Sub Oriented Histograms of Local Binary Patterns for Smoke Detection and Texture Classification
Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms ofExpand
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Co-occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition
It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based onExpand
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Learning multi-scale and multi-order features from 3D local differences for visual smoke recognition
Abstract To overcome shortages of conventional hand-crafted features, we propose a learning based feature extraction method for visual smoke recognition. We first slide a 3D sampling window in theExpand
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Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition
Traditional smoke recognition methods are mainly based on handcrafted features. However, it is difficult to design handcrafted features that are robust and discriminative for smoke recognitionExpand
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Fusing texture, edge and line features for smoke recognition
To improve recognition accuracy, the authors fuse texture, edge and line information to propose a feature extraction method for smoke recognition. The Canny operator is proposed to generate an edgeExpand
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