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Defect detection of bare printed circuit boards based on gradient direction information entropy and uniform local binary patterns

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CIRCUIT WORLD 2017年第4期43卷 145-151页

作者： Li, Yunfeng Li, Shengyang Henan Univ Sci & Technol Sch Mechatron Engn Luoyang Peoples R China Collaborat Innovat Ctr Machinery Equipment Adv Mf Luoyang Peoples R China Henan Univ Sci & Technol Luoyang Peoples R China

Purpose - The purpose of this paper is to propose a defect detection method of bare printed circuit boards (PCB) with high accuracy. Design/methodology/approach - First, bilateral filtering of the PCB image was performed in the uniform color space, and the copper-clad areas were segmented according to the color difference among different areas. Then, according to the chaotic characteristics of the spatial distribution and the gradient direction of the edge pixels on the boundary of the defective areas, the feature vector, which evaluates quantitatively the significant degree of the defect characteristics by using the gradient direction information entropy and the uniform local binary patterns, was constructed. Finally, support vector machine classifier was used for the identification and localization of the PCB defects. Findings - Experimental results show that the proposed algorithm can accurately detect typical defects of the bare PCB, such as short circuit, open circuit, scratches and voids. Originality/value - Considering the limitations of describing all kinds of defects on bare PCB by using single kind of feature, the gradient direction information entropy and the local binary patterns were fused to build a feature vector, which evaluates quantitatively the significant degree of the defect features.

关键词： Printed circuit board Support vector machines Defect detection Information entropy uniform local binary patterns

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Enhancing Active Vision System Categorization Capability Through uniform local binary patterns 1st

Enhancing Active Vision System Categorization Capability Thr...

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1st International Symposium on Artificial Life and Intelligent Agents (ALIA)

作者： Lanihun, Olalekan Tiddeman, Bernie Tuci, Elio Shaw, Patricia Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

ISBN: (纸本)9783319180847;9783319180830

Previous research in Neuro-Evolution controlled Active Vision Systems has shown its potential to solve various shape categorization and discrimination problems. However, minimal investigation has been done in using this kind of evolved system in solving more complex vision problems. This is partly due to variability in lighting conditions, reflection, shadowing etc., which may be inherent to these kinds of problems. It could also be due to the fact that building an evolved system for these kinds of problems may be too computationally expensive. We present an Active Vision System controlled Neural Network trained by a Genetic Algorithm that can autonomously scan through an image pre-processed by uniform local binary patterns [8]. We demonstrate the ability of this system to categorize more complex images taken from the camera of a Humanoid (iCub) robot. Preliminary investigation results show that the proposed uniform local binary Pattern [8] method performed better than the gray-scale averaging method of [1] in the categorization tasks. This approach provides a framework that could be used for further research in using this kind of system for more complex image problems.

关键词： Categorization Active vision system Neural network Genetic algorithm uniform local binary patterns

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Rotation and scale invariant texture classification by compensating for distribution changes using covariate shift in uniform local binary patterns

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ELECTRONICS LETTERS 2014年第1期50卷 27-28页

作者： Hassan, A. Riaz, F. Rehman, S. NUST Dept Comp Engn Coll Elect & Mech Engn Rawalpindi Pakistan

A novel rotation and scale invariant texture classification methodology is proposed based on distribution matching in higher dimensional space. Feature extraction is performed by using uniform local binary patterns (uLBPs) in which the rotation and scale changes in an image cause shifts in the underlying uLBP histograms. To compensate for these shifts at the classification layer, the distributions of training and testing data using kernel methods are estimated and means of the two distributions in the transformed domain using importance weights are matched. These calculated importance weights are used in the standard support vector machines to compensate for the shift in the distributions. The proposed method is used for classifying the images in the Brodatz texture database demonstrating the effectiveness of the proposed methodology.

关键词： feature extraction image classification image texture support vector machines novel rotation and scale invariant texture classification methodology distribution matching feature extraction uniform local binary patterns uLBP histograms kernel methods importance weights standard support vector machines Brodatz texture database

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Dual CNN and texture-based face-iris multimodal biometric system via decision-level fusion

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第4期19卷 1-10页

作者： Serifi, Meryem Serifi, Umit Tarsus Univ Engn Fac Dept Comp Engn Mersin Turkiye

Multimodal biometric systems integrate multiple biometric traits to enhance recognition accuracy and robustness. This study introduces a novel face-iris multimodal biometric framework combining texture-based and deep learning methods. The system utilizes uniform local binary patterns applied to capture fine-grained texture features. Additionally, a dual convolutional neural network (CNN) model, incorporating AlexNet and an attention mechanism, extracts high-level discriminative features from entire face and iris images. The attention mechanism prioritizes critical regions in feature maps, improving focus on discriminative details while mitigating noise. The key innovation of the system lies in integrating texture-based and CNN-based features, which collectively enable robust feature extraction and classification. Furthermore, the decision-level fusion strategy using the majority voting technique ensures optimal combination of independent decisions from the methods, providing a resilient final classification decision. Experiments conducted on the CASIA-Iris-Distance database demonstrate a recognition performance of 99.53%, significantly outperforming unimodal and state-of-the-art multimodal systems.

关键词： Multimodal biometric System Information fusion Decision level fusion Convolutional neural networks Dual CNN uniform local binary patterns

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Machine Leaning Assessment of Border Irregularity of Thyroid Nodules from Ultrasound Images

Machine Leaning Assessment of Border Irregularity of Thyroid...

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Conference on Multimodal Image Exploitation and Learning

作者： Mohammad, Fakher Alzoubi, Alaa Du, Hongbo Jassim, Sabah Univ Buckingham Sch Comp Buckingham MK18 1EG England

ISBN: (纸本)9781510650770;9781510650763

The border irregularity of lesions or tumors is an important sign (or feature) contributing to the prediction of the tumor malignancy. This paper is concerned with developing automatic computer vision methods for assessing and recognizing thyroid nodule border irregularity from ultrasound images. Unlike many existing schemes, our methods rely on a small set of points on the nodule border marked manually by clinicians. To mitigate the absence of a fully segmented lesion boundary, we first apply the cubic-spline interpolation of the region of interest (ROI) points to approximate the lesion border and then select equal numbers of points from the approximated border using equal angular distances. We developed two complementary approaches to investigate the global (big indentations and protrusions) and local (small zigzag) irregularity features of the nodule. The first approach includes two Euclidian distances-based methods and a method inspired by Fractal Dimensions (FD). The distances-based methods facilitate the use of the interpolated border and their radial distance functions measured from ROI points to a reference point (centroid) or reference shape (Convex hull), while the FD inspired method uses interpolated border and a fitted ellipse perimeter ratio to calculate an irregularity index. The second approach facilitates the texture analysis within the constructed ribbons around the border line of different widths using feature vector of uniform local binary pattern (ULBP). We evaluate and compare the performance of our methods from the two approaches by using two datasets consisting of 395 and 100 ultrasound images of thyroid nodules collected from two hospitals and labelled by experienced radiologists respectively. The first is used as training and internal testing set, while the second is used for external testing. We shall show the viability of our methods attaining accuracy rates between 70% and 90%.

关键词： uniform local binary patterns Convex Hull Fractal Dimensions Lesions Border Irregularity Thyroid Ultrasound Images

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Evolutionary active vision system: from 2D to 3D

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ADAPTIVE BEHAVIOR 2021年第1期29卷 3-24页

作者： Lanihun, Olalekan Tiddernan, Bernie Shaw, Patricia Tuci, Elio Abeystwyth Univ Abeystwyth SY23 3DB Wales Abeystwyth Univ Dept Comp Sci Abeystwyth Wales Abeystwyth Univ Comp Sci Abeystwyth Wales Univ Namur Fac Comp Sci Namur Belgium

Biological vision incorporates intelligent cooperation between the sensory and the motor systems, which is facilitated by the development of motor skills that help to shape visual information that is relevant to a specific vision task. In this article, we seek to explore an approach to active vision inspired by biological systems, which uses limited constraints for motor strategies through progressive adaptation via an evolutionary method. This type of approach gives freedom to artificial systems in the discovery of eye-movement strategies that may be useful to solve a given vision task but are not known to us. In the experiment sections of this article, we use this type of evolutionary active vision system for more complex natural images in both two-dimensional (2D) and three-dimensional (3D) environments. To further improve the results, we experiment with the use of pre-processing the visual input with both the uniform local binary patterns (ULBP) and the histogram of oriented gradients (HOG) for classification tasks in the 2D and 3D environments. The 3D experiments include application of the active vision system to object categorisation and indoor versus outdoor environment classification. Our experiments are conducted on the iCub humanoid robot simulator platform.

关键词： Active vision system neural network evolutionary robotics uniform local binary patterns histogram of oriented gradients humanoid robot

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IRIS RECOGNITION METHOD BASED ON GABOR FILTERS AND uniform local binary patterns

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2012年第2期12卷 1250014-1250014页

作者： Feddaoui, Nadia Mahersia, Hela Hamrouni, Kamel Univ ELMAanar Natl Engn Sch Tunis LSTS Lab BP 37 Belvedere Tunis 1002 Tunisia

Iris recognition has been recently given greater attention in human identification and it is becoming increasingly an active topic in research. This paper presents a novel iris recognition method based on multi-channel Gabor filtering and uniform local binary patterns (ULBP). First, the eye image is processed in order to obtain a segmented and normalized eye image by applying Hough transform and polar transformation. Second, the iris image is analyzed by Gabor filters to extract the global features of texture details. Then, ULBP operators are applied in each transformed image to describe the local arrangement of iris texture patterns. Next, the obtained representation is partitioned in blocks. Finally, we have encoded the local relationships between statistical measures computed in blocks to form a template of 240 bytes. We estimate the similarity between irises by computing the modified Hamming distance between templates. Tests were carried out on CASIA v3 iris database. Experimental results illustrate the effectiveness and robustness of ULBP to extract rich local and global information of iris texture when combined with simultaneously multi-blocks and multi-channel method. The comparative evaluations illustrate the good discriminative properties of extracted features for iris recognition.

关键词： Biometry iris Gabor filters uniform local binary patterns

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Face Recognition by Weighted Multi-resolution uniform local Gabor binary patterns

Face Recognition by Weighted Multi-resolution Uniform Local ...

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12th International Conference on Control, Automation and Systems (ICCAS)

作者： Jeong, Kyung-Joong Kim, Dai-Jin POSTECH Dept Comp Sci Engn Pohang 790784 South Korea

ISBN: (纸本)9788993215045

In this paper, we present a method to face recognition which considers local shape information, weight of interesting region and texture information by Gabor filter, high-pass filter and local binary patterns, respectively. The face area can be largely divided into two dominant parts that one has high frequency domain and the other has low frequency domain. High frequency parts are interesting region which is edge of face shape, eye, nose, mouth and so on. Low frequency parts are forehead, cheek, and background beside face in the image. Then, the face image is divided into 6x5 small bins which represents a weight of the interesting region. Weight of interesting region is adopted for weighting of distance measure. Gabor filter can be easily extract shape features in face image. local binary patterns are robust to various illumination conditions while Gabor transform is not. Therefore, we combined two features for robust features on various illuminations. For evaluating the illumination robustness of our face recognition system, we used the publicly available database POSTECH face 2007. Finally, we got 91.15% recognition rate by sixteen orientation of Gabor parameter + LBP + average Gaussian high-pass filter.

关键词： Face Recognition uniform local binary patterns Gabor filter

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local generic representation for patch uLBP-based face recognition with single training sample per subject

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第18期77卷 24203-24222页

作者： Khadhraoui, Taher Borgi, Mohamed Anouar Benzarti, Faouzi Ben Amar, Chokri Amiri, Hamid Univ Tunis El Manar Natl Engn Sch Tunis ENIT SITI Lab Tunis Tunisia Univ Sfax Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia

In this paper, we propose a novel paradigm of Patch uniform local binary patterns (PuLBP) based local Generic Representation (LGR) for face recognition. Indeed, we introduce a new block in which an uLBP is used to approximate both reference and variation subsets. Thus, we concentrate on the challenging problem of a single sample per person in a gallery set. Particularly, the main problem is whether only one training subject per class is available. One of the novelties of our technique is to generate virtual samples of each subject. The new sample generic image in a gallery set is adopted to produce the intra-personal variations of different individuals. We illustrate the experimental results of our new algorithm on different benchmark databases, including the AR face database, the Extended Yale B face database, the FRGC database and the FEI database.

关键词： Face recognition uniform local binary patterns local generic representation Single training sample per subject

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A novel texture-based damage detection method for wire ropes

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MEASUREMENT 2019年 148卷 106954-000页

作者： Zhou, Ping Zhou, Gongbo He, Zhenzhi Tang, Chaoquan Zhu, Zhencai Li, Wei China Univ Min & Technol Sch Mechatron Engn Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Jiangsu Peoples R China Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

Visual method is a potential way to intuitively grasp the surface damage state of wire ropes to guarantee its reliability and safety. However, the various appearance characteristics of ropes are seriously affected by noise and effective inspection methods are lacking;therefore, efficient preprocessing and damage detection methods are urgently needed. Aiming at the influence of illumination, feature dimension, and the performance of different classification algorithms, this paper presents a novel method for the damage detection of wire ropes based on texture features, called WR-LBPML. To uniform illuminate and highlight textures of the rope images, different homomorphic filtering algorithms were compared and applied. A total of 59 texture features were extracted from rope surface images using uniform local binary patterns (u-LBP) operators. Due to the redundancy of the dataset, the features were processed using principal component analysis. Finally, we conducted a comparative study of three machine learning algorithms. Extensive experiments and comparisons of rope damage detection indicated that the proposed method can achieve optimal performance (accuracy: 93.3%, efficiency: 0.04 ms per sample) using a support vector machine with 10 features extracted by u-LBP operator, in which the images were filtered by a Gaussian low-pass filter with a block size of 1. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Damage detection Wire rope Texture feature Image filtering uniform local binary patterns Machine learning

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