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Magnetic Resonance Imaging, texture analysis and regression techniques to non-destructively predict the quality characteristics of meat pieces

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 82卷 110-125页

作者： Avila, M. M. Duran, M. L. Caballero, D. Antequera, T. Palacios-Perez, T. Cernadas, E. Fernandez-Delgado, M. Univ Extremadura Sch Technol Badajoz Spain Univ Extremadura Meat & Meat Prod Res Inst Avda Ciencias S-N Caceres 10003 Spain Univ Santiago de Compostela Ctr Singular Invest Tecnol Informac USC CiTIUS Rua Xenaro de la Fuente Dominguez Santiago De Compostela 15782 Spain

The quality of meat products is traditionally assessed by chemical or sensorial analysis, which are time consuming, need specialized technicians and destroy the products. The development of new technologies to monitor meat pieces using non-destructive methods in order to establish their quality is earning importance in the last years. An increasing number of studies have been carried out on meat pieces combining Magnetic Resonance Imaging (MRI), texture descriptors and regression techniques to predict several physico-chemical or sensorial attributes of the meat, mainly different types of pig ham and loins. In spite of the importance of the problem, the conclusions of these works are still preliminary because they only use the most classical texture descriptors and regressors instead of stronger methods, and because the methodology used to measure the performance is optimistic. In this work, we test a wide range of texture analysis techniques and regression methods using a realistic methodology to predict several physico-chemical and sensorial attributes of different meat pieces of Iberian pigs. The texture descriptors include statistical techniques, like Haralick descriptors, local binary patterns, fractal features and frequential descriptors, like Gabor or wavelet features. The regression techniques include linear regressors, neural networks, deep learning, support vector machines, regression trees, ensembles, boosting machines and random forests, among others. We developed experiments using 15 texture feature vectors, 28 regressors over 4 datasets of Iberian pig meat pieces to predict 39 physico-chemical and sensorial attributes, summarizing 16,380 experiments. There is not any combination of texture vector and regressor which provides the best result for all attributes tested. Nevertheless, all these experiments provided the following conclusions: (1) the regressor performance, measured using the squared correlation (R-2), is from good to excellent (above 0.5625)

关键词： Meat products Magnetic resonance imaging Regression methods Texture classification Wavelet transform Gabor filters local binary patterns Fractal descriptors

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Video-Based Face Verification with local binary patterns and SVM Using GMM Supervectors 1

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12th International Conference on Computational Science and Its Applications (ICCSA)

作者： Pereira, Tiago F. Angeloni, Marcus A. Simoes, Flavio O. Silva, Jose Eduardo C. CPqD Res & Dev Ctr Telecommun BR-13026123 Campinas SP Brazil

ISBN: (数字)9783642311253

ISBN: (纸本)9783642311246;9783642311253

The classification task has a relevant importance in face verification systems and there are many approaches proposed to solve it. This paper shows a new approach for the classification task in video-based face verification systems using Support Vector Machines (SVM) as classifier and Gaussian Mixture Models (GMM) working as its kernel. The use of local binary patterns (LBP) for face description, in conjunction with the generation of Gaussian supervectors as input points for the classifier, describes the temporal information contained in a video by a unique feature point, which seems to be a very compact and powerful form of representation. Our experimental results, performed on MOBIO database and protocol, shows the advantages of the proposed technique.

关键词： Face Verification local binary patterns Support Vector Machines Gaussian Mixture Models

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Fast Object Detection Based on Color Histograms and local binary patterns

Fast Object Detection Based on Color Histograms and Local Bi...

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IEEE Region 10 Conference (TENCON) - Sustainable Development through Humanitarian Technology

作者： Lee, Kwon Lee, Chulhee Kim, Seon-Ae Kim, Young-Hoon Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea Elect & Telecommun Res Inst Wireless Personal Network Team Taejon South Korea

ISBN: (纸本)9781467348232;9781467348225

In this paper, we present a fast object detection algorithm based on color histograms and local binary patterns (LBP). The proposed method consists of two steps: coarse target object detection and precise target object detection. During the coarse target object detection step, a small number of target candidates were generated using integral color histogram matching. To reduce computational complexity, we only used the dominant color of the integral color histogram matching. During the precise target object detection step, the target object was determined by comparing LBP feature distributions with those of the target object candidates. Experimental results show that the proposed method is efficient and robust.

关键词： object detection integral color histogram local binary patterns

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A comprehensive study of texture analysis based on local binary patterns

A comprehensive study of texture analysis based on local bin...

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Conference on Optics, Photonics and Digital Technologies for Multimedia Applications II

作者： Nava, Rodrigo Cristobal, Gabriel Escalante-Ramirez, Boris Univ Nacl Autonoma Mexico Posgrado Ciencia &Ingn Computac Mexico City 04510 DF Mexico CSIC Inst Opt Madrid 28006 Spain Univ Nacl Autonoma Mexico Fac Ingn Dept Procesamiento Senales Mexico City DF Mexico

ISBN: (纸本)9780819491282

One of the main goals of texture analysis is to provide a robust mathematical description of the spatial behavior of intensity values in any given neighborhood. These local distributions called textures characterize object surfaces and are used for pattern identification and recognition of images. However, some spatial patterns may vary from quite simple stripes to randomness, where textures look like unstructured noise. Since textures can exhibit a large number of properties such as surface materials and geometry of the lighting sources, many different approaches have been proposed. A featured method is the modification of Wang's algorithm made by Ojala et al, the so-called local binary patterns (LBP). The LBP algorithm uses a 3 x 3 square mask named "texture spectrum" which represents a neighborhood around a central pixel. The values in the square mask are compared with the central pixel and then multiplied by a weighting function according with their positions. This technique has become popular due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. Specially, it has gained increased importance in image classification, where the success not only depends on a robust classifier but also relies in a good selection of the feature descriptors. However, Ojala's algorithm presents some limitations such as noise sensitivity and lack of invariance to rotational changes. This fact has fostered many extensions of the original LBP approach that in many cases are based on minor changes in order to attain e.g. illumination and rotational invariance or improving the robustness to noise. In this paper we present a detailed overview of the LBP algorithm and other recently modifications. In addition, we perform a texture classification study with seven algorithms in presence of rotational changes, noise degradation, contrast information, and different sizes of LBP masks using the USC-SIPI database. The LBP histograms have

关键词： local binary patterns texture analysis texture classification distance measure USI-SIPI Brodatz

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Developing Learning-Based Preprocessing Methods for Detecting Complicated Vehicle Licence Plates

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IEEE ACCESS 2020年 8卷 170951-170966页

作者： Al-Shemarry, Meeras Salman Li, Yan Univ Southern Queensland Sch Sci Fac Hlth Engn & Sci Toowoomba Qld 4350 Australia Karbala Univ Dept Comp Coll Sci Karbala 56001 Iraq

A licence plate detection (LPD) system is an important tool in several roadway traffic applications. This study aims to develop an advanced detection system that works well in complicated scenarios. It proposes a robust preprocessing enhancement method for accurately detecting the licence plates from difficult vehicle images. The proposed method includes the combination of a Gaussian filter, an enhancement cumulative histogram equalization method, and a contrast-limited adaptive histogram equalization technique. The local binary pattern and median filter with histogram of oriented gradient descriptors are used as powerful tools to extract key features from three types of licence plate resolutions. The extracted features are used as input to support vector machine classifier. Processing methods, such as a position-based method are used with the detector to reduce unwanted bounding boxes, as well as false positive values. Four databases consisting of 2050 vehicle images under different conditions are used. Various detection metrics, object localization, and the receiver operating characteristic (ROC) curve are used to evaluate the performance of the proposed method. The experimental results on vehicles databases in several languages, including English, Chinese, and Arabic number plates, show that the proposed method has achieved significant performance improvements. It outperforms the state-of-the-art approaches in terms of both the detection rate and the processing time. The detection rate when trained with 1520 LP images is 99.62% with a false positive rate of 1.675% for complicated images. The average detection time per vehicle image is 0.2408 milliseconds.

关键词： Feature extraction Support vector machines Histograms Lighting Training Databases Robustness Histogram of oriented gradient licence plate detection local binary patterns support vector machine

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Towards more discriminative features for texture recognition

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PATTERN RECOGNITION 2020年 107卷 107473-107473页

作者： Cerkezi, Llukman Topal, Cihan Eskisehir Tech Univ Dept Elect & Elect Engn Iki Eylul Campus TR-26555 Eskisehir Turkey

local binary patterns (LBP) are considered to be one of the most computationally efficient descriptor that can also be combined jointly among different variants to increase accuracy. In this study, we propose a method to obtain more discriminative 2D LBP features by optimizing projections of a joint LBP distribution onto the marginal histograms. To find a more efficient representation of the feature vector, we seek the least redundant marginal histograms of a joint LBP distribution via optimizing several constraints. In this way, we aim to have a more compact yet accurate feature vector in contrast to the methods that flatten the joint distribution. Experiments we perform on five popular texture datasets show that the feature vectors optimized with the proposed method provide higher recognition rates with the same size vectors and comparable results even with lower dimensional vectors. We also compare the proposed algorithm to more recent texture recognition methods based on convolutional neural networks and show that it can still provide comparable results even though the resulting feature vectors are smaller by orders of magnitude. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： local binary patterns Texture recognition Feature representations Feature optimization Deep texture features Mutual information

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Automatic Defect Detection of Yarn-dyed Fabrics Based on Energy Fusion and local binary patterns

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Li, Wenyu Cheng, Longdi Xue, Wenliang Donghua Univ Coll Text Shanghai 201620 Peoples R China

ISBN: (纸本)9783037853702

For the purpose of realizing fast and effective detection of defects of yarn-dyed fabric, and in consideration of the inherent characteristics of texture, i.e., color and structure, an approach for automatic defect detection is proposed in this paper. The image of yarn-dyed fabric to be enhanced is first converted from RGB true color space to L*a*b* color space. Then Log-gabor filters filter chromatic and brightness channels, and energy feature images are acquired after energy is fused between chromatic and brightness. Finally defects of yarn-dyed fabrics can be detected on the energy feature images using local binary patterns. The proposed method can detect colored and structural flaws. Experimental results for the defect detection from six kinds of yarn-dyed fabrics indicate that a high detection rate is achieved for the proposed method. It is fast enough to be possible for real-time application.

关键词： Yarn-dyed Fabric Defect detection Energy Fusion Log-gabor Filter local binary patterns

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Peachy Parallel Assignments (EduPar 2019) 33

Peachy Parallel Assignments (EduPar 2019)

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33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Ozturk, Ozcan Glick, Ben Mache, Jens Bunde, David P. Bilkent Univ Ankara Turkey Lewis & Clark Coll Portland OR 97219 USA Knox Coll Galesburg IL USA

ISBN: (纸本)9781538655559

Peachy Parallel Assignments are a resource for instructors teaching parallel and distributed programming. These are high-quality assignments, previously tested in class, that are readily adoptable. This collection of assignments includes face recognition, finding the electrical potential of a square wire, and heat diffusion. All of these come with sample assignment sheets and the necessary starter code.

关键词： Parallel computing education High-Performance Computing education Parallel programming OpenMP Java Executor framework local binary patterns Poisson's equation Numerical differential equations

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REGISTRATION OF RF ULTRASOUND DATA USING HYBRID local binary patterns

REGISTRATION OF RF ULTRASOUND DATA USING HYBRID LOCAL BINARY...

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9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Klein, T. Hansson, M. Karamalis, A. Navab, N. Tech Univ Munich D-8000 Munich Germany Malmo Univ Sch Technol Malmo Sweden

ISBN: (纸本)9781457718588

Registration of ultrasound images is often complicated due to inherent noise. Robust similarity metrics and optimization procedures are required to facilitate medical applicability. In this paper a novel hybrid procedure, incorporating global statistics and local textural features, is proposed for the registration of envelope detected radio frequency ultrasound data. On the global scale this is achieved by Hellinger distance between distribution in images, and on the local scale by a statistics-based extension of Fuzzy local binary patterns (FLBP). The proposed procedure is shown to outperform standard measures such as SSD and NCC, as well as Hellinger distance and histogram matching of standard FLBPs, in rigid registration experiments of envelope detected radio frequency data samples of the human neck.

关键词： Ultrasound Radio Frequency (RF) Similarity Hellinger local binary patterns Nakagami

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A Novel Texture Classification Scheme based on Completed Multiple Adaptive Threshold patterns

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Procedia Computer Science 2020年 171卷 331-340页

作者： Nishant Shrivastava Department of Computer Science and Engineering Jaypee University Anupshahr India-203390

local binary patterns and their extensions are heavily used in image texture classification. However traditional LBP codes are sensitive to noise and have limited capability of discriminating various texture patterns. This study presents a novel Completed multiple adaptive threshold patterns (CMATP) descriptor for texture classification to ensure higher classification rates. For each patch of pixels statistical parameters like mean, standard deviation and value of centre pixel are computed to determine structural properties. Based on these observations pixel patches are categorized in to uniform, moderate and non-uniform structures. A suitable adaptive threshold is then obtained for each class of pixel groups, which can generate binary pattern that are more uniform and can produce distinct binary code for different texture patterns. Further to extract centre pixel and contrast information from each patch, multiple threshold based complementary sign and magnitude patterns are generated in a similar way. The sign and magnitude pattern are then combined form CMATP descriptor. We have compared the proposed CMATP pattern with some state of the art LBP based descriptors on some benchmark datasets like Outex, Brodatz and UMD. The experimental results shows superior performance of the CMATP as compared to others.

关键词： local binary patterns Texture classification Adaptive threshold Rotation invariant

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