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13th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Cerrolaza, Juan J. Porras, Antonio R. Mansoor, Awais Zhao, Qian Summar, Marshall Linguraru, Marius George Childrens Natl Med Ctr Sheikh Zayed Inst Pediat Surg Innovat Washington DC 20010 USA Childrens Natl Med Ctr Div Genet & Metab Washington DC 20010 USA

ISBN: (纸本)9781479923496;9781479923502

The early detection of genetic disorders in infants is crucial for the timely management of patients and disease. The particular facial characteristics of patients affected by dysmorphic syndromes, which account to about half of genetic disorders, allows to identify positive cases prior to cytogenetic results, and avoid the overuse of genetic blood tests. However, the diagnostic accuracy by pediatricians is moderate. In this work, we present a general framework for the detection of genetic disorders from facial pictures, combining geometrical and texture features. Based on the 2D extension of Linear Discriminant Analysis, we propose the extraction of optimal landmark-specific local binary Pattern-based features. In particular, the proposed framework computes optimal local image filters and soft neighborhood weighting matrices that enhance the discriminative ability of the system. This new framework was tested on a database of 145 cases, including 73 pathological patients with 15 different genetic syndromes, obtaining a detection accuracy of 0.95.

关键词： Dysmorphic syndromes Down syndrome local binary patterns Linear Discriminative Analysis

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Analyzing Surface Defects in Apples Using Gabor Features 12

Analyzing Surface Defects in Apples Using Gabor Features

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12th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Jolly, Puneet Raman, Sundaresan BITS Pilani Dept Comp Sci & Informat Syst Pilani Rajasthan India

ISBN: (纸本)9781509056989

This paper describes different approaches for detection and identification of diseases in apples using computer vision. Our proposed algorithms analyze surface appearance of apple for defects using image features, viz. color and texture. For segmentation of Region Of Interest (ROI), K-means clustering is performed over the image pixels based on their intensity values. For creation of feature vector, combinations of Gabor Wavelets with different feature descriptors were explored. Comparative study has been carried out between Haralick features, local binary patterns, and kernel PCA, to observe their performance over Gabor features. Classification is achieved via Support Vector Machines and K-Nearest Neighbors. For the task of disease detection, accuracy recorded was greater than 96.9% for Gabor+LBP approach and in range of 89.8% to 96.25% for Gabor+Haralick approach. Gabor+kernel PCA recorded lowest accuracy of 90%. For disease identification, combination of Gabor+LBP outperformed other combinations, recording highest accuracy ranging from 85.93% to 95.31%.

关键词： Apple Surface Defects Apples Gabor wavelets Feature Descriptors local binary patterns Kernel Principal Component Analysis Haralick features

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A local approach based on a local binary patterns variant texture descriptor for classifying pain states

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第12期37卷 7888-7894页

作者： Nanni, Loris Brahnam, Sheryl Lumini, Alessandra Univ Bologna IEIIT CNR DEIS I-40136 Bologna Italy Missouri State Univ Springfield MO 65804 USA

This paper focuses on the use of image-based techniques for classifying pain states, in particular we compare several texture descriptors based on local binary patterns (LBP), and we proposed some novel solutions based on the combination of new texture descriptors: the Elongated Ternary Pattern (ELTP) and the Elongated binary Pattern (ELBP). ELTP is the best performing descriptor in our experiments. The ELBP descriptor combines characteristics of the local Ternary Pattern (LTP) and ELTP. These two variants of the standard LBP are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. The resulting extracted features are used to train a support vector machine classifier. Extensive experiments are conducted using the Infant COPE database of neonatal facial images. Our results show that a local approach based on the ELTP feature extractor produces a reliable system for classifying pain states. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Image medical analysis Texture descriptors local binary patterns Neonatal pain detection Support vector machine

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Weighted Finite Automata based on local patterns for Image Authentication 9

Weighted Finite Automata based on Local Patterns for Image A...

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IEEE 9th International Conference on Semantic Computing

作者： Raju, U. S. N. George, Shibin Sandeep, Irlanki Kiran, Kothuri Sai Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal 506004 Telangana India

ISBN: (纸本)9781479979356

This paper presents an efficient image authentication system. The authentication signature is extracted from WFA encoding of the image. For noises that are more textural rather than color-based, we transform the image using a local-binary-Pattern filter, which is then converted to automata. We present a technique that incorporates the weights of the WFA, unlike previous works.

关键词： Weighted Finite Automata local binary patterns Signature Authentication

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An Improved Breast Tissue Density Classification Framework Using Bag of Features Model 4th

An Improved Breast Tissue Density Classification Framework U...

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4th IEEE International Colloquium on Information Science and Technology (CiSt)

作者： Chougrad, Hiba Zouaki, Hamid Alheyane, Omar CHOUAIB DOUKKALI Univ LIMA Lab Informat & Math & Leurs Applicat El Jadida Morocco CHOUAIB DOUKKALI Univ Lab Math Fondamentales El Jadida Morocco

ISBN: (纸本)9781509007516

we present a new approach to classify breast tissue density which is widely accepted to be an important risk indicator for the development of breast cancer. The computer aided diagnosis (CAD) framework developed, first segments the breast area then represents each image using the bag-of-features (BoF) approach. To represent the images, we first extract local binary patterns (LBP) features, which are then quantized to create a codebook. Second, we encode the features using the obtained codebook and a sparse coding algorithm to obtain a final image representation. Finally, we use support vector machines (SVM) classifier to carry out the classification task. In order to evaluate the efficiency of the proposed approach, we tested the framework using the digital database of screening mammograms (DDSM). The results showed that 91.25% of the samples were correctly classified. We also investigated the codebook size and selected the one that enhanced the results. Our proposal showed better performance compared to previous methods that classified the same dataset.

关键词： breast tissue density classification computer-aided diagnostic systems bag of features local binary patterns sparse coding LLC max-pooling

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Classification of Voiced and Non-voiced Speech Signals using Empirical Wavelet Transform and Multi-level local patterns

Classification of Voiced and Non-voiced Speech Signals using...

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IEEE International Conference on Digital Signal Processing (DSP)

作者： Kumar, T. Sunil Hussain, Md. Azahar Kanhangad, Vivek IIT Indore Elect Engn Indore Madhya Pradesh India

ISBN: (纸本)9781479980581

This paper presents a novel algorithm for classification of voiced and non-voiced speech segments in noisy environment. Empirical wavelet transform (EWT), an adaptive technique for analyzing non-stationary signals, is employed in the pre-processing stage for suppression of noise in speech signals. In this work, multi-level local patterns (MLP), modified version of 1D-local binary patterns (LBP) are used as features. Multi-level local patterns capture the local variations in non-stationary signal by performing comparisons in neighborhood of a sample. Finally, the comparative information thus generated is encoded into multiple states and histogram of MLPs corresponding to short segments of speech signal is computed. Nearest neighbor classifier utilizes the histogram features for classification of speech segments. Experimental evaluation of proposed approach is carried out on the publicly available CMU-Arctic database. The results of our experiments show improvement in classification accuracy with the use of EWT. Further, the MLP based approach clearly yields superior performance than the LBP based approach.

关键词： Empirical wavelet transform multi-level local patterns local binary patterns NN classifier voiced - non-voiced speech classification

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FRONTAL FACE DETECTION FOR SURVEILLANCE PURPOSES USING DUAL local binary patterns FEATURES

FRONTAL FACE DETECTION FOR SURVEILLANCE PURPOSES USING DUAL ...

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IEEE International Conference on Image Processing

作者： Louis, Wael Plataniotis, K. N. Univ Toronto Multimedia Lab Edward S Rogers Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781424479948

Face detection in video sequence is becoming popular in surveillance applications, but the usage of large number of features and the long training time are persistent problems. This paper integrates two types of local binary patterns (LBP) features in order to achieve a high detection rate with a high discriminative power face detector. First LBP feature is a novel way of using the Circular LBP, in which the pixels of the image are targeted;it is a non-computationally expensive feature extraction. The second LBP feature is the LBP Histogram, in which regions in the image are targeted;it is more computationally expensive than Circular LBP features but has higher discriminative power. The proposed detector is examined on real-life low-resolution surveillance sequence. Conducted experiments show that the proposed detector achieves 98% detection rate in comparison to 91% for the Lienhart detector. The proposed detector tolerates wide range of illumination changes.

关键词： Frontal face detection local binary patterns Cascaded classifiers Feature extraction

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OBJECT DETECTION USING NON-REDUNDANT local binary patterns

OBJECT DETECTION USING NON-REDUNDANT LOCAL BINARY PATTERNS

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IEEE International Conference on Image Processing

作者： Duc Thanh Nguyen Zong, Zhimin Ogunbona, Philip Li, Wanqing Univ Wollongong Sch Comp Sci & Software Engn ICT Res Inst Adv Multimedia Res Lab Wollongong NSW 2522 Australia

ISBN: (纸本)9781424479948

local binary Pattern (LBP) as a descriptor, has been successfully used in various object recognition tasks because of its discriminative property and computational simplicity. In this paper a variant of the LBP referred to as Non-Redundant local binary Pattern (NRLBP) is introduced and its application for object detection is demonstrated. Compared with the original LBP descriptor, the NRLBP has advantage of providing a more compact description of object's appearance. Furthermore, the NRLBP is more discriminative since it reflects the relative contrast between the background and foreground. The proposed descriptor is employed to encode human's appearance in a human detection task. Experimental results show that the NRLBP is robust and adaptive with changes of the background and foreground and also outperforms the original LBP in detection task.

关键词： Human detection local binary patterns

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Palmprint recognition based on discriminative local binary patterns statistic feature

Palmprint recognition based on discriminative local binary p...

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2nd International Conference on Signal Acquisition and Processing

作者： Mu, Meiru Ruan, Qiuqi Shen, Yongsheng Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

ISBN: (纸本)9780769539607

In on-line palmprint recognition tasks, texture based feature extraction methods are widely adopted for palmprint representation, owing to their high performance. Gabor filter bank is among the most promising texture information extraction tool because of its multi-scale and multi-orientational characteristics. However, it is both time and memory intensive to convolve palm images with a bank of filters to extract features. In this paper, a novel palmprint texture representation is proposed, discriminative local binary patterns statistic (DLBPS), which is extracted for palmprint recognition. In this approach, a palmprint is firstly divided into non-overlapping and equal-sized regions, which are then labeled into local binary patterns (LBP) independently. By calculating these patterns' distribution, the statistic features of the palmprint texture are attained. Subsequently, the Discriminative Common Vectors (DCV) algorithm is applied for dimensionality reduction of the feature space and solution of the optional discriminative common vectors. Finally, Euclidean distance and the nearest neighbor classifier are used for palmprint classification. Our experimental results demonstrate the effectiveness of the proposed DLBPS palmprint representation, which brings both high recognition accuracy rate and high speed benefits.

关键词： Palmprint recognition local binary patterns Discriminative Common Vector

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RESEARCH ON AEROENGINE RUB-IMPACT FAULT ANALYSIS BASED ON WAVELET TRANSFORM AND THE local binary patterns

RESEARCH ON AEROENGINE RUB-IMPACT FAULT ANALYSIS BASED ON WA...

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2010 International Conference on Wavelet Analysis and Pattern Recognition

作者： Wu, Ya-Hui Xue, Jing-Feng Zhang, Da-Zhi Li, Xin-Liang AVIC Changcheng Inst Metrol & Measurement Natl Key Lab Sci & Technol Metrol & Calibrat Beijing 100095 Peoples R China

ISBN: (纸本)9781424465316

The local binary patterns statistical characteristics of the continuous wavelet transform scalogram of the aeroengine vibration signal are explored in this paper. The method is based on recognizing that certain uniform local binary patterns which are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. Three patterns are studied on the rub-impact faulty signals from the aeroengine test. The analysis indicates that these features are suitable to reflect the local information of the scalogram and can reveal the characteristic of vibration signals well.

关键词： Aeroengine Rub-impact Continuous wavelet transform local binary patterns

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