Local binary pattern based texture analysis for visual fire recognition

Huang, Jing; Zhao, Jianhui; Gao, Weiwei; Long, Chengjiang; Xiong, Lu; Yuan, Zhiyong; Han, Shizhong

doi:10.1109/cisp.2010.5647609

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…In addition, authors in [7] analyzed texture properties of fire in terms of spatial variations, and spatial variations of flame are much larger than those of other objects. Many researchers also proposed texture analysis methods to explore texture characteristics of fire, such as discrete wavelet transformation (DWT) [4,7], GLCM [8,9] and local binary pattern (LBP) [10]. Fig.…”

Section: Fire Characteristicsmentioning

confidence: 99%

Copyright Protection for Fire Video Images using an Effective Watermarking Method

Nguyen¹,

Kim²

2013

KIPS Transactions on Software and Data Engineering

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This paper proposes an effective watermarking approach for copyright protection of fire video images. The proposed watermarking approach efficiently utilizes the inherent characteristics of fire data with respect to color and texture by using a gray level co-occurrence matrix (GLCM) and fuzzy c-means (FCM) clustering. GLCM is used to generate a texture feature dataset by computing energy and homogeneity properties for each candidate fire image block. FCM is used to segment color of the fire image and to select fire texture blocks for embedding watermarks. Each selected block is then decomposed into a one-level wavelet structure with four subbands [LL, LH, HL, HH] using a discrete wavelet transform (DWT), and LH subband coefficients with a gain factor are selected for embedding watermark, where the visibility of the image does not affect. Experimental results show that the proposed watermarking approach achieves about 48 dB of high peak-signal-to-noise ratio (PSNR) and 1.6 to 2.0 of low M-singular value decomposition (M-SVD) values. In addition, the proposed approach outperforms conventional image watermarking approach in terms of normalized correlation (NC) values against several image processing attacks including noise addition, filtering, cropping, and JPEG compression.

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Section: Fire Characteristicsmentioning

confidence: 99%

Copyright Protection for Fire Video Images using an Effective Watermarking Method

Nguyen¹,

Kim²

2013

KIPS Transactions on Software and Data Engineering

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“…Studies related to dynamic textures include three main aspects, segmentation, synthesis and classification. DT analysis has various applications of visual processing, such as fire detection [2] and facial analysis [3]. In this paper, we focus on the recognition of DTs.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Texture Recognition Using 3D Random Features

Zhao

Lin

Liu

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we present a novel, simple but effective approach for dynamic texture recognition using 3D random features. Compared with the existing dynamic texture recognition approaches using carefully designed features for high performance, our method use only a few 3D random filters to extract spatio-temporal features from local dynamic texture blocks, which are further encoded into a low-dimensional feature vector. To explore the representative power of the 3D random features, we use two different encoding schemes, the learning-based Fisher vector encoding and the learning-free binary encoding. The proposed method is tested on the UCLA and DynTex databases with various evaluation protocols. Experimental results demonstrate the high performance of our method for dynamic texture recognition.

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“…In the last years, the LBP descriptor [15] has been successful in several applications, such as texture recognition [16], face detection/recognition [17], and facial expression recognition [18], due to its powerful characteristics. However, the fact that the LBP descriptor does not consider any global spatial information is a disadvantage in the case of hand gestures.…”

Section: Volumetric Spatiograms Of Local Binary Patterns (Vs-lbp)mentioning

confidence: 99%

Human–computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

Maqueda

del-Blanco

Jaureguizar

et al. 2015

Computer Vision and Image Understanding

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A more natural, intuitive, user-friendly, and less intrusive Human-Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.

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Local binary pattern based texture analysis for visual fire recognition

Cited by 16 publications

References 11 publications

Copyright Protection for Fire Video Images using an Effective Watermarking Method

Copyright Protection for Fire Video Images using an Effective Watermarking Method

Dynamic Texture Recognition Using 3D Random Features

Human–computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

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