Gradient Local Binary Patterns Research Articles

Automatic road sign detection and recognition based on neural network

Road sign detection and recognition is an integral part of intelligent transportation systems. It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations, and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns feature to the RGB color space and the local self-similarity feature. ANN, AdaBoost, and support vector machine have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databases, well known in the traffic sign community (GTSRB, GTSDB, and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods.

SVM-based writer retrieval system in handwritten document images

Digital libraries include huge amount of information that are continuously increasing with the need of storing various kinds of handwritten documents such as administrative forms, and cultural heritage manuscripts. Therefore, new emerging techniques such as writer retrieval are used to facilitate information extraction from archived documents. Basically, a writer retrieval system is composed of two main steps that are feature generation and dissimilarity measure. To achieve a robust retrieval, we propose the use of an SVM classifier trained to automatically separate intra-writer features from inter-writer features. For feature generation, we investigate the effectiveness of the Histogram of Oriented Gradients, Gradient Local Binary Patterns, and Local Difference Features. Experiments are conducted on three benchmark datasets. The results obtained evince a satisfactory performance of SVM, which can give better results than the state of the art.

Multi-Scale Continuous Gradient Local Binary Pattern for Leaky Cable Fixture Detection in High-Speed Railway Tunnel

The feature of leaky cable fixture extracted by Local Binary Pattern (LBP) and its variants in high-speed railway tunnel has the defects of lacking description and high dimension. This paper proposes a new operator named Multi-scale Continuous Gradient Local Binary Pattern (MCG-LBP), which can realize the scale transformation of feature maps and ensure the low dimensionality of descriptors. For MCG-LBP, firstly a bi-directional triplet around the central pixel is presented to indicate the specific direction of gradient in circle neighborhood. Then, an effective dimensionality reduction strategy is introduced to perform successive down-sampling iterations. Finally, the multi-scale joint descriptors are encoded by continuous gradient sequences from different down-sampling maps, and Support Vector Machines is used to classify faulty cable fixtures. The proposed MCG-LBP can elicit a discriminative description through complementary gradient information generated by the combination of different single-scale features. While the low dimensionality of descriptor and no complex parameter to deal with both make it has higher computational efficiency. Experimental results show that the Recall and Precision of MCG-LBP reach 92.6% and 83.5% respectively on cable fixture data set, which is superior to the state-of-the-art methods.

Multi‐view hand gesture recognition via pareto optimal front

Computational cost-ineffectiveness and ambiguity due to self-occlusion are bottlenecks of vision-based hand gesture recognition. In this study, the authors address these issues by proposing a novel multi-view hand gesture recognition method based on Pareto optimal front. They first present an oriented gradient local binary pattern operator to generate a groupwise gesture feature data set for multi-view hand gesture images. Then they take hand gesture recognition over multi-view hand images as a multi-query image retrieval problem and Pareto optimal front is constructed based on the dissimilarities between the testing images and sample images. The gesture corresponding to the point with the shortest distance to the origin on the Pareto optimal front is the final recognised result. Extensive experiments verify the accuracy and efficiency of their Pareto optimal font-based method.

Extraction of multimodal features from depth and RGB images for recognising hand gestures

In this study, we propose a multimodal feature based framework for recognising hand gestures from RGB and depth images. In addition to the features from the RGB image, the depth image features are explored into constructing the discriminative feature labels of various gestures. Depth maps having powerful source of information, increases the performance level of various computer vision problems. A newly refined Gradient-Local Binary Pattern (G-LBP) is applied to extract the features from depth images and histogram of gradients (HOG) features are extracted from RGB images. The components from both RGB and depth channels, are concatenated to form a multimodal feature vector. In the final process, classification is performed using K-Nearest Neighbour and multi-class Support Vector Machines. The designed system is invariant to scale, rotation and illumination. The newly developed feature combination method is helpful to achieve superior recognition rates for future innovations.

基于改进梯度局部二值模式的人脸识别

基于改进梯度局部二值模式的人脸识别

Fuzzy integrals for combining multiple SVM and histogram features for writer's gender prediction

This study addresses automatic prediction of the writer's gender. We propose the use of fuzzy integral (FI) operators to combine support vector machines (SVMs) associated with different local features. Presently, we focus on local histogram-based features that describe different kinds of handwriting traits to ensure SVM complementarity. First, we introduce a new feature based on the histogram of templates that aims to highlight local orientations of the text strokes. As a second feature, we propose the rotation invariant uniform local binary patterns to enhance local textural information, whereas the third feature is the gradient local binary patterns. Various forms of the FI are used for combining these predictors. Experiments are conducted on four standard datasets of English, Arabic and French handwritten text. First, for each language, the prediction task is evaluated by considering text-independent and writer-independent design. Then, a more challenging prediction is tried by adding the language-independency constraint. The results obtained confirm the effectiveness of the proposed features. Also, they highlight the contribution of the combination step to achieve a robust prediction.

Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices

Robust and reliable detection of falls is crucial especially for elderly activity monitoring systems. In this letter, we present a fall detection system using wearable devices, e.g., smartphones, and tablets, equipped with cameras and accelerometers. Since the portable device is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may travel, as opposed to static sensors installed in certain rooms. Moreover, a camera provides an abundance of information, and the results presented here show that fusing camera and accelerometer data not only increases the detection rate, but also decreases the number of false alarms compared to only accelerometer-based or only camera-based systems. We employ histograms of edge orientations together with the gradient local binary patterns for the camera-based part of fall detection. We compared the performance of the proposed method with that of using original histograms of oriented gradients (HOG) as well as a modified version of HOG. Experimental results show that the proposed method outperforms using original HOG and modified HOG, and provides lower false positive rates for the camera-based detection. Moreover, we have employed an accelerometer-based fall detection method, and fused these two sensor modalities to have a robust fall detection system. Experimental results and trials with actual Samsung Galaxy phones show that the proposed method, combining two different sensor modalities, provides much higher sensitivity, and a significant decrease in the number of false positives during daily activities, compared to accelerometer-only and camera-only methods.

New off-line Handwritten Signature Verification method based on Artificial Immune Recognition System

Natural Immune System offers many interesting features that inspired the design of Artificial Immune Systems (AIS) used to solve various problems of engineering and artificial intelligence. AIS are particularly successful in fault detection and diagnosis applications where anomalies such as errors and failures are assimilated to viruses that should be detected. Thereby, AIS seem suitable to automatically detect forgeries in signature verification systems. This paper proposes a novel method for off-line signature verification that is based on the Artificial Immune Recognition System (AIRS). For feature generation, two different descriptors are proposed to generate signature traits. The first is the Gradient Local Binary Patterns that estimates gradient features based on the LBP neighborhood. The second descriptor is the Longest Run Feature, which describes the signature topology by considering longest suites of text pixels. Performance evaluation is carried out on CEDAR and GPDS-100 datasets. The results obtained showed that the proposed system has promising performance and often comfortably outperforms the state of the art.

Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure

Timely, precise, and reliable detection of fall events is very important for systems monitoring activities of elderly people, especially the ones living independently. In this paper, we propose an autonomous fall detection system by taking a completely different view compared with existing vision-based activity monitoring systems and applying a reverse approach. In our system, in contrast with static sensors installed at fixed locations, the camera is worn by the subject, and thus, monitoring is not limited only to areas where the sensors are located and extends to wherever the subject may travel. Moreover, the camera provides a richer set of data and helps lower the false positive rates compared with accelerometer-only systems. We employ a modified version of the histograms of oriented gradients (HOG) approach together with the gradient local binary patterns (GLBP). It is shown that, with the same training set, the GLBP feature is more descriptive and discriminative than HOG, histograms of template, and semantic local binary patterns. Moreover, we autonomously compute a threshold, for the detection of fall events, from the training data based on relative entropy, which is a member of Ali-Silvey distance measures. Experiments are performed with ten different people and a total of around 300 associated fall events indoors and outdoors. Experimental results show that, with the autonomously computed threshold, the proposed method provides 93.78% and 89.8% accuracy for detecting falls with indoor and outdoor experiments, respectively.

Robust soft-biometrics prediction from off-line handwriting analysis

Currently, writer's soft-biometrics prediction is gaining an important role in various domains related to forensics and anonymous writing identification. The purpose of this work is to develop a robust prediction of the writer's gender, age range and handedness. First, three prediction systems using SVM classifier and different features, that are pixel density, pixel distribution and gradient local binary patterns, are proposed. Since each system performs differently to the others, a combination method that aggregates a robust prediction from individual systems, is proposed. This combination uses Fuzzy MIN and MAX rules to combine membership degrees derived from predictor outputs according to their performances, which are modeled by Fuzzy measures. Experiments are conducted on two Arabic and English public handwriting datasets. The comparison of individual predictors with the state of the art highlights the relevance of proposed features. Besides, the proposed Fuzzy MIN-MAX combination comfortably outperforms individual systems and classical combination rules. Relatively to Sugeno's Fuzzy Integral, it has similar computational complexity while performing better in most cases.

A Novel Fast Pedestrian Detection Method

This paper presents novel pedestrian detection approach in video streaming, which could process frames rapidly. The method is based on cascades of HOG-LBP (Histograms of Oriented Gradients-Local Binary Pattern), but combines non-negative factorization to reduce the length of the feature, aiming at realizing a more efficient way of detection, remedying the slowness of the original method. Experiments show our method can process faster than HOG and HOG-LBP, and more accurate than HOG, which has better performance in pedestrian detection in video streaming.

Relative gradient local binary patterns method for face recognition under varying illuminations

Local binary patterns (LBPs) are effective facial texture feature descriptors in face recognition. However, the performance of original LBP-based face recognition methods rapidly deteriorates in the condition of nonmonotonic illumination variations. In order to overcome this drawback, we propose a LBP-based face recognition approach, namely relative gradient LBPs (RGLBPs), in which the relative gradient is first applied to the original face images to extract illumination invariant features. Then, an LBP describes textural and structural features for face recognition. Finally, the chi-square dissimilarity measure and the nearest neighbor classifier are used for classification. The experimental results validate that the proposed approach is efficient for the illumination problem in face recognition and also robust to expression and age variations.