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APPLIED OPTICS 2014年第5期53卷 923-930页

作者： Butel, Guillaume P. Smith, Greg A. Burge, James H. Univ Arizona Coll Opt Sci Tucson AZ 85721 USA

Deflectometry is widely used to accurately calculate the slopes of any specular reflective surface, ranging from car bodies to nanometer-level mirrors. This paper presents a new deflectometry technique using binary patterns of increasing frequency to retrieve the surface slopes. binary pattern Deflectometry allows almost instant, simple, and accurate slope retrieval, which is required for applications using mobile devices. The paper details the theory of this deflectometry method and the challenges of its implementation. Furthermore, the binary pattern method can also be combined with a classic phase-shifting method to eliminate the need of a complex unwrapping algorithm and retrieve the absolute phase, especially in cases like segmented optics, where spatial algorithms have difficulties. Finally, whether it is used as a stand-alone or combined with phase-shifting, the binary patterns can, within seconds, calculate the slopes of any specular reflective surface. (C) 2014 Optical Society of America

关键词： stand-alone data recorder binary pattern Vehicle body Phase shift Phase reflecting surface

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Multi-Scale binary pattern Encoding Network for Cancer Classification in Pathology Images

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第3期26卷 1152-1163页

作者： Vuong, Trinh T. L. Song, Boram Kim, Kyungeun Cho, Yong M. Kwak, Jin T. Korea Univ Sch Elect Engn Seoul 02841 South Korea Sungkyunkwan Univ Sch Med Kangbuk Samsung Hosp Dept Pathol Seoul 05505 South Korea Univ Ulsan Coll Med Asan Med Ctr Dept Pathol Seoul 03181 South Korea

Multi-scale approaches have been widely studied in pathology image analysis. These offer an ability to characterize tissues in an image at various scales, in which the tissues may appear differently. Many of such methods have focused on extracting multi-scale hand-crafted features and applied them to various tasks in pathology image analysis. Even, several deep learning methods explicitly adopt the multi-scale approaches. However, most of these methods simply merge the multi-scale features together or adopt the coarse-to-fine/fine-to-coarse strategy, which uses the features one at a time in a sequential manner. Utilizing the multi-scale features in a cooperative and discriminative fashion, the learning capabilities could be further improved. Herein, we propose a multi-scale approach that can identify and leverage the patterns of the multiple scales within a deep neural network and provide the superior capability of cancer classification. The patterns of the features across multiple scales are encoded as a binary pattern code and further converted to a decimal number, which can be easily embedded in the current framework of the deep neural networks. To evaluate the proposed method, multiple sets of pathology images are employed. Under the various experimental settings, the proposed method is systematically assessed and shows an improved classification performance in comparison to other competing methods.

关键词： Feature extraction Pathology Deep learning Cancer detection Prostate cancer Breast cancer Image segmentation Cancer classification convolutional neural network multi-scale binary pattern digital pathology

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Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion

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IET IMAGE PROCESSING 2015年第1期9卷 12-21页

作者： Nikan, Soodeh Ahmadi, Majid Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

A local-based illumination insensitive face recognition algorithm is proposed which is the combination of image normalisation and illumination invariant descriptors. Illumination insensitive representation of image is obtained based on the ratio of gradient amplitude to the original image intensity and partitioned into smaller sub-blocks. Local phase quantisation and multi-scale local binary pattern, extract the sub-regions characteristics. Distance measurements of local nearest neighbour classifiers are fused at the score level to find the best match and decision-level fusion combines the results of two matching techniques. Entropy, class posterior probability and mutual information are utilised as the weights of fusion components. Simulation results on the YaleB, Extended YaleB, AR, Multi-PIE and FRGC databases show the improved performance of the proposed algorithm under severe illumination with low computational complexity and no reconstruction or training requirement.

关键词： image normalization FUSION Quantization Nearest neighbor IMAGE INTENSITY Optimal matching complexity classes binary pattern face recognition algorithm

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High-quality fringe pattern generation based on binary pattern optimization with projector defocusing

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JOURNAL OF OPTICAL TECHNOLOGY 2017年第1期84卷 22-28页

作者： Li, X. -X. Zhang, Z. -J. Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai 200072 Peoples R China

Real-time three-dimensional reconstruction is increasingly important in many fields. However, it is a challenge for the conventional digital fringe projection technique. The binary defocusing technique applied to the digital fringe projection technique not only significantly improves the real-time performance but also fundamentally eliminates the nonlinearity of the projector. In the existing techniques, the dithering techniques based on optimization are superior to the others. However, those optimization methods have two obvious drawbacks: the objective function just qualifies the global intensity similarity while ignoring the local similarity, and the optimization framework is inefficient or time consuming. This paper first presents a novel objective function consisting of a global intensity term and a local structure term to comprehensively evaluate similarity. Second, a model optimization framework, which includes a hybrid optimization algorithm and a half period optimization idea, is employed. Both simulations and experimental results show the advantages of the proposed objective function and the optimization framework, as well as the improvement of quality and speed of 3D reconstruction. (C) 2017 Optical Society of America

关键词： interference pattern Projectors FRINGE PROJECTION defocusing Real time pattern generation Technology Objective function Optimization methods binary pattern Optimization algorithms

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Robust method for interest region description based on local intensity binary pattern

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JOURNAL OF ELECTRONIC IMAGING 2017年第4期26卷 43025.1-43025.1页

作者： Yang, Yi Duan, Fajie Jiang, Jiajia Ma, Ling Zheng, Hao Tianjin Univ State Key Lab Precis Measurement Technol & Instru Tianjin Peoples R China

This paper proposes a robust method based on a local intensity binary pattern for interest feature description. To avoid estimating reference orientation, local features were calculated on a rotation invariant system. Different from the local binary patterns (LBP) and center-symmetric-LBP operator, our proposed local circular contrast pattern (LCCP) operator calculates a local binary feature by comparing the relative intensity order information of each two adjacent elements in the sequence consisting of the sampling point and its neighboring points. To evaluate the performance of our proposed descriptor LCCP and other existing descriptors (e.g., scale-invariant feature transform, DAISY, HRI-CSLTP, multisupport region order-based gradient histogram-single, local intensity order pattern), image matching experiments were first conducted on the Oxford dataset, additional image pairs with complex light changes, image sequences with different noise, and three-dimensional objects dataset. To further evaluate the discriminative ability of local descriptors, a simple object recognition experiment was carried out on three public datasets. The experimental results show that our descriptor LCCP exhibits a better performance and robustness than other evaluated descriptors. (C) 2017 SPIE and IS&T

关键词： binary pattern reference orientation local binary patterns image matching scale invariant feature transform

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A local binary pattern classification approach for cloud types derived from all-sky imagers

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2019年第7期40卷 2667-2682页

作者： Oikonomou, Spiros Kazantzidis, Andreas Economou, George Fotopoulos, Spiros Univ Patras Dept Phys Elect Lab Patras Greece Univ Patras Dept Phys Lab Atmospher Phys Patras Greece

Cloud classification from ground-based observations is a challenging task that attracts increasing attention, favoured by the development of all-sky imaging equipment. In this work, we propose a new method for cloud type classification from all-sky images. Appropriate versions of two descriptors, Regional Local binary pattern (R-LBP) and Four Patch-Local binary pattern (FP-LBP), are employed on the images in order to extract not only global but also local textural information from the observed cloud type patterns. In the classification stage, a linear Support Vector Machine (SVM) scheme and Linear Discriminant Analysis (LDA) classifiers are adopted for the assignment of the corresponding cloud type label. Our method is evaluated against two state-of-the-art methods and their datasets consisting of 5000 and 2500 images, respectively. According to the results, the proposed method outperforms the previous ones. Due to its promising results and the novelty of local pattern information of clouds, the proposed methodology could be considered as the basis for future studies aiming to overcome the basic disadvantage of all-sky imaging algorithms: to provide regional cloud type information instead of one cloud type for the whole sky.

关键词： Novelty Taxonomy images binary pattern imaging instrument Cloud Types pattern recognition

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Spatially enhanced local binary pattern

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ELECTRONICS LETTERS 2012年第25期48卷 1590-1591页

作者： Yu, Kaimin Wang, Zhiyong Yue, Lei Feng, Dagan Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Beijing Inst Technol Dept Elect Engn Beijing Peoples R China

The local binary pattern (LBP) is one of the state-of-the-art texture descriptors for texture classification. However, the spatial distribution of the LBPs is not taken into account. Proposed is a novel texture descriptor, namely the spatially enhanced LBP, to encapsulate the spatial distribution of the LBPs with a shape context based method. Also, the shape context based method is adapted to be rotation invariant. Experimental results on three widely used benchmark datasets show that the proposed descriptor outperforms the original LBP descriptor.

关键词： image classification image texture LBP descriptor LBP spatial distribution context based method image texture classification spatially enhanced LBP spatially enhanced local binary pattern texture descriptors texture descriptor Image classification image texture binary pattern

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Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP)

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pattern ANALYSIS AND APPLICATIONS 2023年第3期26卷 847-859页

作者： Koley, Subhadeep Roy, Hiranmoy Dhar, Soumyadip Bhattacharjee, Debotosh RCC Inst Informat Technol Dept ECE Kolkata 700015 W Bengal India RCC Inst Informat Technol Dept IT Kolkata 700015 W Bengal India Jadavpur Univ Dept CSE Kolkata 700032 W Bengal India

With the ever-increasing security threats in recent years, biometric authentication has become omnipresent. Among all biometric characteristics, face recognition research has gained traction lately. This paper proposes a new face image descriptor named Local Discrete Cosine Transform binary pattern (LDCTBP) for illumination- and modality-invariant face recognition. Utilizing the frequency segregation behavior of Discrete Cosine Transform (DCT), an effective cross-modal illuminationagnostic local feature descriptor has been formulated. Eventually, by encoding the illumination-normalized DCT coefficients into a binary pattern, Local Discrete Cosine Transform binary pattern has been generated. Qualitative and quantitative analysis performed on the Extended Yale-B, CUFSF, and TUFTS dataset depict the supremacy of the proposed framework over other state-of-the-arts. Moreover, the proposed LDCTBP has been integrated with a light-weight Convolutional Neural Network (CNN) to prove the importance of handcrafted features in CNN training.

关键词： Discrete cosine transform binary pattern Illumination-invariance Face descriptor Deep learning

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Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2014年第1期4卷 1页

作者： Kim, Hyunduk Lee, Sang-Heon Sohn, Myoung-Kyu Kim, Dong-Ju DGIST Dept Convergence 50-1 Sang Ri Daegu 711873 South Korea

In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, two techniques were employed. In order to deal with the large set of training data, the method of Random Forests was employed;this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a binary pattern Run Length matrix was employed;this matrix is combination of binary pattern and a Run Length matrix. The binary pattern was calculated by randomly selected operator. In order to extract feature of training patch, we calculate statistical texture features from the binary pattern Run Length matrix. Moreover we perform some techniques to real-time operation, such as control the number of binary test. Experimental results show that our algorithm is efficient and robust against illumination change.

关键词： Head pose estimation Random forests binary pattern Run Length matrix Illumination-invariant

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Fast Traffic Sign Recognition with a Rotation Invariant binary pattern Based Feature

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SENSORS 2015年第1期15卷 2161-2180页

作者： Yin, Shouyi Ouyang, Peng Liu, Leibo Guo, Yike Wei, Shaojun Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant binary pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

关键词： traffic sign recognition binary pattern SIFT artificial neutral network

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