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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
17 Jun 2016
TL;DR: It is noted that edge detection is one of the most fundamental processes within the low level vision and provides the basis for the higher level visual intelligence in primates, and its implementation needs a cross-disciplinary approach in neuroscience, computing and pattern recognition.
Abstract: This review provides an overview of the literature on the edge detection methods for pattern recognition that inspire from the understanding of human vision. We note that edge detection is one of the most fundamental processes within the low level vision and provides the basis for the higher level visual intelligence in primates. The recognition of the patterns within the images relates closely to the spatiotemporal processes of edge formations, and its implementation needs a cross-disciplinary approach in neuroscience, computing and pattern recognition. In this review, the edge detectors are grouped in as edge features, gradients and sketch models, and some example applications are provided for reference. We note a significant increase in the amount of published research in the last decade that utilises edge features in a wide range of problems in computer vision and image understanding having a direct implication to pattern recognition with images.

9 citations

Patent
20 Aug 2014
TL;DR: In this paper, a distributed pedestrian detection system and method based on a mobile robot platform is presented, which includes the steps that various current mature detection algorithms related to pedestrians and a robot operating system (ROS) are fused, various characteristics of the pedestrians are calculated through distributed calculation nodes of the ROS, various calculated characteristic data are comprehensively fused, pedestrian targets can be stably tracked in the robot moving process, and then detection accuracy is improved.
Abstract: The invention discloses a distributed pedestrian detection system and method based on a mobile robot platform. The system comprises the mobile robot platform and a Microsoft Kinect camera, a desktop computer and a network communication device, wherein the Microsoft Kinect camera, the desktop computer and the network communication device are carried on the mobile robot platform. The method includes the steps that various current mature detection algorithms related to pedestrians and a robot operating system (ROS) are fused, various characteristics of the pedestrians are calculated through distributed calculation nodes of the robot operating system (ROS), various calculated characteristic data are comprehensively fused, pedestrian targets can be stably tracked in the robot moving process, and then detection accuracy is improved, wherein the distributed calculation nodes mainly include the histogram of gradients ( HOG) node, the face detection node, the upper body histogram of gradients (HOG) node, the skin color detection node, the point cloud detection node and the gesture detection node. The system and method are mainly used for detection calculation of the pedestrians in images of a mobile robot, and especially for mobile robot pedestrian detection calculation so that human-machine interaction can be achieved.

9 citations

Book ChapterDOI
10 Jun 2013
TL;DR: A set of tests is conducted to find the classifiers which optimize recall in the detection of persons in visible video sequences, and the same classifiers are used to detect people in infrared video sequences obtaining excellent results.
Abstract: This paper introduces a solution for detecting humans in smart spaces through computer vision. The approach is valid both for images in visible and infrared spectra. Histogram of oriented gradients (HOG) is used for feature extraction in the human detection process, whilst linear support vector machines (SVM) are used for human classification. A set of tests is conducted to find the classifiers which optimize recall in the detection of persons in visible video sequences. Then, the same classifiers are used to detect people in infrared video sequences obtaining excellent results.

9 citations

Proceedings ArticleDOI
01 Nov 2018
TL;DR: This study classifies the stage of Diabetic Retinopathy into three classes, namely normal, mild Non-Proliferative Diabetic retinopathy (NPDR), and moderate/severe NPDR class, using Histogram of Oriented Gradients (HOG) to extract feature and uses factor analysis as a feature selection method.
Abstract: This study classifies the stage of Diabetic Retinopathy (DR) into three classes, namely normal, mild Non-Proliferative Diabetic Retinopathy (NPDR), and moderate/severe NPDR class. In general, this research is done to solve that problem arises as a result of similarity of image per stages that cannot be assessed invisible. So, it requires a handling where the image of the retina can be categorized into appropriate categories. Based on the problem, two experimental mechanisms were conducted for each hierarchy, i.e approach computer vision that only focus to process the whole image and the approach taken by the medical using texture feature as marker feature to detect DR. The data are obtained from DiaretDB0 public database. In this research, we present Histogram of Oriented Gradients (HOG) to extract feature. To select the best feature from HOG, we used factor analysis as a feature selection method. This step was done to get good performance in classification step. In our experimental design, we implented shallow learning such as Support Vector Machines learning and Random Forest learning to classify moderate/sever NPDR vs. mild NPDR, mild NPDR vs. Normal, and moderate/severe NPDR vs. Normal. The experimental result shows that our proposed method is able to provide good enough performance in terms of time and accuracy. Our proposed method achieved around 85% accuracy for the binary class classification.

9 citations

Journal ArticleDOI
TL;DR: An improved version of a recent state-of-the-art texture descriptor called Gaussians of Local Descriptors (GOLD), which is based on a multivariate Gaussian that models the local feature distribution that describes the original image, is presented.
Abstract: improved version of the Gaussians of Local Descriptorswe describe the covariance matrix using a set of visual featuresthe original approach and the new one are combined by sum rule This paper presents an improved version of a recent state-of-the-art texture descriptor called Gaussians of Local Descriptors (GOLD), which is based on a multivariate Gaussian that models the local feature distribution that describes the original image. The full rank covariance matrix, which lies on a Riemannian manifold, is projected on the tangent Euclidean space and concatenated to the mean vector for representing a given image. In this paper, we test the following features for describing the original image: scale-invariant feature transform (SIFT), histogram of gradients (HOG), and weber's law descriptor (WLD). To improve the baseline version of GOLD, we describe the covariance matrix using a set of visual features that are fed into a set of Support Vector Machines (SVMs). The SVMs are combined by sum rule. The scores obtained by an SVM trained using the original GOLD approach and the SVMs trained with visual features are then combined by sum rule. Experiments show that our proposed variant outperforms the original GOLD approach. The superior performance of the proposed system is validated across a large set of datasets. Particularly interesting is the performance obtained in two widely used person re-identification datasets, CAVIAR4REID and IAS, where the proposed GOLD variant is coupled with a state-of-the-art ensemble to obtain an improvement of performance on these two datasets. Moreover, we performed further tests that combine GOLD with non-binary features (local ternary/quinary patterns) and deep transfer learning. The fusion among SVMs trained with deep features and the SVMs trained using the ternary/quinary coding ensemble is demonstrated to obtain a very high performance across datasets. The MATLAB code for the ensemble of classifiers and for the extraction of the features will be publicly available11https://www.dei.unipd.it/node/2357 (+Pattern Recognition and Ensemble Classifiers) to other researchers for future comparisons.

9 citations


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