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Conference on Applications of Digital Image Processing XXXVI

作者： Olveres, Jimena Nava, Rodrigo Escalante-Ramirez, Boris Cristobal, Gabriel Maria Garcia-Moreno, Carla Univ Nacl Autonoma Mexico Mexico City 04510 DF Mexico Univ Nacl Autonoma Mexico Fac Ingn Dept Procesamienti Senales Mexico City 04510 DF Mexico CSIC Inst Opt E-28006 Madrid Spain Hosp Angels Lomas Mexico City DF Mexico

ISBN: (纸本)9780819497062

In recent years, the use of Magnetic Resonance Imaging (MRI) to detect different brain structures such as midbrain, white matter, gray matter, corpus callosum, and cerebellum has increased. This fact together with the evidence that midbrain is associated with Parkinson's disease has led researchers to consider midbrain segmentation as an important issue. Nowadays, Active Shape Models (ASM) are widely used in literature for organ segmentation where the shape is an important discriminant feature. Nevertheless, this approach is based on the assumption that objects of interest are usually located on strong edges. Such a limitation may lead to a final shape far from the actual shape model. This paper proposes a novel method based on the combined use of ASM and local binary patterns for segmenting midbrain. Furthermore, we analyzed several LBP methods and evaluated their performance. The joint-model considers both global and local statistics to improve final adjustments. The results showed that our proposal performs substantially better than the ASM algorithm and provides better segmentation measurements.

关键词： Active Shape Models Dice's coefficient Hausdorff distance local binary patterns Midbrain segmentation

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Random Vector Functional-link Net Based Pedestrian Detection Using Multi-feature Combination

Random Vector Functional-link Net Based Pedestrian Detection...

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6th International Congress on Image and Signal Processing (CISP)

作者： Wang, Zhihui Yoon, Sook Xie, Shan Juan Lu, Yu Park, Dong Sun Chonbuk Natl Univ Div Elect & Informat Engn Jeonju South Korea Mokpo Natl Univ Dept Multimedia Mokpo South Korea Hangzhou Normal Univ Inst Remote Sensing & Earth Sci Hangzhou Peoples R China

ISBN: (纸本)9781479927630

Pedestrian detection is one of the key technologies in the field of computer vision. To improve the accuracy and efficiency of the recognition system, a variety of feature extraction and classification methods has been utilized on this challenging task. This paper proposes a novel multi-feature extraction and selection method to represent and distinguish different categories of samples. In addition, combined with the multi-feature combination, random vector functional-link net (RVFL) has been used to recognize these pedestrians from backgrounds. Experimental results show that multi-feature combination outperforms other widely used image features. Moreover, the performance of RVFL algorithm with multi-feature combination is even better than other state-of-the-art classification algorithms, such as SVM or AdaBoost based classifier.

关键词： pedestrian detection random vector functional-link net (RVFL) multi-feature combination local binary patterns histogram of orientated gradients

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Texture Classification using Multi-Dimensional LBP Variance

Texture Classification using Multi-Dimensional LBP Variance

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2nd IAPR Asian Conference on Pattern Recognition (ACPR)

作者： Doshi, Niraj P. Schaefer, Gerald Univ Loughborough Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781479921904

Texture classification is an important task for a variety of computer vision applications. A successful group of texture algorithms based on local neighbourhood descriptors and known as LBP (local binary patterns) has been shown to provide good and robust discriminative power, and is typically applied in a rotation invariant form and calculated at multiple resolutions. local contrast information can be integrated into the LBP histogram generation by using the variance as weights for LBP, leading to LBP variance (LBPV) texture features. Multi-scale LBPV histograms are obtained by concatenating the individual one-dimensional histograms derived from each scale. In this paper, we show that by calculating a multidimensional LBP variance (MD-LBPV) histogram improved texture classification can be achieved. We confirm this based on extensive experiments on several Outex benchmark datasets.

关键词： texture classification local binary patterns LBPV MD-LBP MD-LBPV

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Loop Closure Detection with a Holistic Image Feature

Loop Closure Detection with a Holistic Image Feature

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16th Portuguese Conference on Artificial Intelligence (EPIA)

作者： Campos, Francisco M. Correia, Luis Calado, Joao M. F. Inst Super Engn Lisboa LabMAg Lisbon Portugal Univ Lisbon Dept Comp Sci LabMAg Lisbon Portugal Inst Super Engn Lisboa IDMEC Lisbon Portugal Inst Super Engn Lisboa Dept Mech Engn Lisbon Portugal

ISBN: (纸本)9783642406690;9783642406683

In this paper we introduce a novel image descriptor, LBP-gist, suitable for real time loop closure detection. As the name suggests, the proposed method builds on two popular image analysis techniques: the gist feature, which has been used in holistic scene description and the LBP operator, originally designed for texture classification. The combination of the two methods gives rise to a very fast computing feature which is shown to be competitive to the state-of-the-art loop closure detection. Fast image search is achieved via Winner Take All Hashing, a simple method for image retrieval that exploits the descriptive power of rank-correlation measures. Two modifications of this method are proposed, to improve its selectivity. The performance of LBP-gist and the hashing strategy is demonstrated on two outdoor datasets.

关键词： loop closure detection visual features gist local binary patterns image retrieval

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On the Robustness of Color Texture Descriptors across Illuminants

On the Robustness of Color Texture Descriptors across Illumi...

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17th International Conference on Image Analysis and Processing (ICIAP)

作者： Bianco, Simone Cusano, Claudio Napoletano, Paolo Schettini, Raimondo Univ Milano Bicocca DISCo Dipartimento Informat Sistemist & Comunicaz Viale Sarca 336 I-20126 Milan Italy Univ Pavia I-27100 Pavia Italy

ISBN: (纸本)9783642411847;9783642411830

In this paper we evaluate several extensions of local binary patterns to color images. In particular, we investigate their robustness with respect to changes in the illuminant color temperature. To do so, we recovered the spectral reflectances of 1360 texture images from the Outex 13 data set. Then, we rendered the images as if they were taken under 33 different illuminants. For each combination of a training and test illuminant, we measured the classification performance of the texture features considered. The results of this extensive experimentation are reported and critically discussed.

关键词： Color texture classification illuminant invariance reflectance recovery local binary patterns Outex texture database

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An Extended local binary Pattern for Gender Classification

An Extended Local Binary Pattern for Gender Classification

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15th IEEE International Symposium on Multimedia (ISM)

作者： Ardakany, Abbas Roayaei Nicolescu, Mircea Nicolescu, Monica Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

ISBN: (纸本)9780769551401

This paper addresses the problem of gender recognition by proposing a new feature descriptor to be used in classification. The contribution of this work is an extension to the local binary patterns traditionally used as descriptors. local binary patterns include information about the relationship between a central pixel value and those of its neighboring pixels in a very compact manner. In the proposed method we incorporate into the descriptor more information from the neighborhood by using four predefined patterns, rather than just one, as in the classic model. We evaluate the performance of our method on the standard FERET database by comparing it to existing methods and show that we can extract more discriminative features and subsequently provide better gender recognition accuracy.

关键词： component local binary patterns Gender recognition Genetic algorithms

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Gabor Wavelet Transform Based Facial Expression Recognition Using PCA and LBP

Gabor Wavelet Transform Based Facial Expression Recognition ...

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Signal Processing and Communications Applications Conference

作者： Muzammil Abdulrahman Tajuddeen R. Gwadabe Fahad J. Abdu Alaa Eleyan Electrical & Electronics Engineering Department Mevlana University

ISBN: (纸本)9781479948734

This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis (PCA) and local binary pattern (LBP) algorithms. Experiments were carried out of using Japanese female facial expression (JAFFE) database. In all experiments conducted using JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with an average recognition rate of 90% under the same experimental setting.

关键词： Facial Expression Recognition Gabor Wavelet Transform Principal Component Analysis local binary patterns

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Color treatment in endoscopic image classification using multi-scale local color vector patterns

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MEDICAL IMAGE ANALYSIS 2012年第1期16卷 75-86页

作者： Haefner, M. Liedlgruber, M. Uhl, A. Vecsei, A. Wrba, F. Salzburg Univ Dept Comp Sci A-5020 Salzburg Austria St Elizabeth Hosp Dept Internal Med Vienna Austria St Anna Childrens Hosp Endoscopy Unit A-1090 Vienna Austria Med Univ Vienna Dept Clin Pathol Vienna Austria

In this work we propose a novel method to describe local texture properties within color images with the aim of automated classification of endoscopic images. In contrast to comparable local binary patterns operator approaches, where the respective texture operator is almost always applied to each color channel separately, we construct a color vector field from an image. Based on this field the proposed operator computes the similarity between neighboring pixels. The resulting image descriptor is a compact 1D-histogram which we use for a classification using the k-nearest neighbors classifier. To show the usability of this operator we use it to classify magnification-endoscopic images according to the pit pattern classification scheme. Apart from that, we also show that compared to previously proposed operators we are not only able to get competitive classification results in our application scenario, but that the proposed operator is also able to outperform the other methods either in terms of speed, feature compactness, or both. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Classification Colon cancer local binary patterns Multi-scale Color

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2013年第3期10卷

作者： Tavakoli, Hamed Rezazadegan Moin, M. Shahram Heikkila, Janne Univ Oulu Ctr Machine Vis Res Oulu Finland Islamic Azad Univ Qazvin Branch Qazvin Iran

In this paper, we present a tracking technique utilizing a simple saliency visual descriptor. Initially, we define a visual descriptor named local similarity pattern that mimics the famous texture operator local binary patterns. The key difference is that it assigns each pixel a code based on the similarity to the neighbouring pixels. Later, we simplify this descriptor to a local saliency operator which counts the number of similar pixels in a neighbourhood. We name this operator local similarity number (LSN). We apply the local similarity number operator to measure the amount of saliency in a target patch and model the target. The proposed tracking algorithm uses a joint saliency-colour histogram to represent the target in a mean-shift tracking framework. We will show that the proposed saliency-colour target representation outperforms texture-colour where texture modelled by local binary patterns and colour target representation techniques are used.

关键词： Mean-shift Tracking Saliency local binary patterns

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Fuzzy local binary patterns for ultrasound texture characterization

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5th International Conference on Image Analysis and Recognition

作者： Iakovidis, Dimitris K. Keramidas, Eystratios G. Maroulis, Dimitris Univ Athens Dept Informat & Telecommun GR-15784 Athens Greece

ISBN: (纸本)9783540698111

B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the local binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy local binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.

关键词： fuzzy local binary patterns ultrasound thyroid nodules support vector machines

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