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Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.


Papers
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Journal ArticleDOI
TL;DR: An efficient method based on histogram of oriented gradients (HOG) and motion energy image (MEI) that can detect all inter-frame forgeries and achieve higher accuracy with lower execution time is proposed.
Abstract: Inter-frame forgery is a common type of video forgery to destroy the video evidence. It occurs in the temporal domain such as frame deletion, frame insertion, frame duplication, and frame shuffling. These forms of forgery are more frequently produced in a surveillance video because the camera position and the scene are relatively stable, where the tampering process is easy to operate and imperceptible. In this paper, we propose an efficient method for inter-frame forgery detection based on histogram of oriented gradients (HOG) and motion energy image (MEI). HOG is obtained from each image as a discriminative feature. In order to detect frame deletion and insertion, the correlation coefficients are used and abnormal points are detected via Grabb’s test. In addition, MEI is applied to edge images of each shot to detect frame duplication and shuffling. Experimental results prove that the proposed method can detect all inter-frame forgeries and achieve higher accuracy with lower execution time.

22 citations

Journal ArticleDOI
TL;DR: Experiments conducted on three public kinship databases show that the proposed descriptor can outperform many state-of-the-art kinship verification algorithms and descriptors including those that are based on deep Convolutional Neural Nets.
Abstract: Texture descriptors such as Local Binary Pattern (LBP), Local Phase Quantization (LPQ), and Histogram of Oriented Gradients (HOG) have been widely used for face image analysis. This work introduces a novel framework for image-based kinship verification able to efficiently combine local and global facial information extracted from diverse descriptors. The proposed scheme relies on two main points: (1) we model the face images using a Pyramid Multi-level (PML) representation where local descriptors are extracted from several blocks at different resolution scales; (2) we compute the covariance (second-order statistics) between diverse local features characterizing each individual block in the PML representation. This gives rise to a face descriptor with two interesting properties: (i) thanks to the PML representation, scales and face parts are explicitly encoded in the final descriptor without having to detect the facial landmarks; (ii) the covariance descriptor encodes spatial features of any type allowing the integration of several state-of-the-art texture and color features. Experiments conducted on three public kinship databases show that the proposed descriptor can outperform many state-of-the-art kinship verification algorithms and descriptors including those that are based on deep Convolutional Neural Nets.

22 citations

Journal ArticleDOI
TL;DR: This framework has achieved good results in speed and accuracy and has been verified that the accuracy of the verification set is improved compared to the individual features using the fused features.
Abstract: With the continuous development of the electronics industry, the number of printed circuit board (PCB) has grown at a rapid rate, and the requirements for the detection systems of PCB have also continuously increased. In the traditional PCB detection, the main reference is the comparison method. However, in a real scene, there are a series of problems such as non-uniform illumination, tilting of the camera angle, and the like, resulting in a less satisfactory effect of the reference comparison method. So, the authors proposed a non-reference comparison framework of PCB defects detection. This framework has achieved good results in speed and accuracy. The authors extract the histogram of oriented gradients and local binary pattern features for each PCB image, respectively, put into the support vector machine to get two independent models. Then, according to Bayes fusion theory, the authors fuse two models for defects classification. The authors have established a PCB data set that includes both defective and defect-free. It has been verified that the accuracy of the verification set is improved compared to the individual features using the fused features. The authors also illustrate the effectiveness of Bayes feature fusion in terms of speed.

22 citations

Proceedings ArticleDOI
01 Mar 2017
TL;DR: This paper proposes to presents a hand gesture recognition as an assistive tool for patients care that uses Haar-like feature, Adaboost algorithm and Cascade Classification for hand detection and Principal Component Analysis work together with Histogram of Oriented Gradients to achieve preferable results.
Abstract: This paper proposes to presents a hand gesture recognition as an assistive tool for patients care. The system uses Haar-like feature, Adaboost algorithm and Cascade Classification for hand detection. Principal Component Analysis work together with Histogram of Oriented Gradients and hand gesture decision to achieve preferable results of hand gesture recognition. Hand detection accuracy achieves 91.88 percent and sensitivity succeeds 95.75 percent.

22 citations

Proceedings ArticleDOI
15 Apr 2019
TL;DR: This paper proposes a warning notification diffusion solution related to real-time pedestrian presence detection, through an inter-vehicle communication system, using Histogram of Oriented Gradients descriptor with the linear Support Vector Machine classifier, and Haar feature-based cascade classifier to reach vehicle detection.
Abstract: The ability to perceive and understand surrounding road-users behaviors is crucial for self-driving vehicles to correctly plan reliable reactions. Computer vision that relies mostly on machine learning techniques enables autonomous vehicles to perform several required tasks such as pedestrian detection. Furthermore, within a fully autonomous driving environment, driverless vehicle has to communicate and share perceived data with its neighboring vehicles for more safe navigation. In this context, our paper proposes a warning notification diffusion solution related to real-time pedestrian presence detection, through an inter-vehicle communication system. To achieve this purpose, pedestrian and vehicle recognition is required. Thus, we implemented intended detectors. We used Histogram of Oriented Gradients (HOG) descriptor with the linear Support Vector Machine (SVM) classifier for the pedestrian detector, and Haar feature-based cascade classifier to reach vehicle detection. The performance evaluation of our solution leads to fairly good detection accuracy around 90% for pedestrian and 88% for vehicle.

22 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022181
2021116
2020189
2019179
2018240