scispace - formally typeset
Search or ask a question
Topic

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
More filters
Proceedings ArticleDOI
20 Jan 2021
TL;DR: In this paper, an approach to detect a human face using texture analysis which includes computing a Histogram of Gradients (HOG) over a region of the face and then uses Support Vector Machines (SVMs) to recognize a face.
Abstract: Computer vision has become a highly evolving field in recent years, dealing with methods for obtaining, processing, examining, and understanding digital images. Human face recognition in computer vision has a vital role to play in security and surveillance, and the mechanisms for increasing the security levels are strengthening day by day. The existing human face recognition system has been enhanced by introducing an anti-spoofing mechanism which will help to stop a nefarious person to intentionally get around with the system. This article focuses on an approach to detect a human face using texture analysis which includes computing a Histogram of Gradients (HOG) over a region of the face and then uses Support Vector Machines (SVMs) to recognize a face. A blink detection mechanism used in this article ensures the liveliness of the person, making the system more reliable. A Raspberry Pi module is used in implementing the work involved in this paper and the programming is done in Python using libraries like OpenCV and NumPy. This model can achieve a maximum accuracy of 92.68% and achieves optimal results during the afternoon, taking a total of 9.89 seconds for face recognition and blink detection.

11 citations

Book ChapterDOI
27 Mar 2018
TL;DR: The survey provides a ready-reference for preferred vehicle detection technique under different applications and three main detection algorithms; Gaussian Mixture Model, Histogram of Gradients, and Adaptive motion Histograms based vehicle detection are implemented and evaluated.
Abstract: Attention towards Intelligent Transportation System (ITS) has increased manifold especially due to prevailing security situation in the past decade. An integral part of ITS is video-based surveillance systems extracting real-time traffic parameters such as vehicle counting, vehicle classification, vehicle velocity etc. using stationary cameras installed on road sides. In all these systems, robust and reliable detection of vehicles is significantly a critical step. Since, several vehicle detection techniques exist, evaluating these techniques with respect to different environment conditions and application scenarios will give a better choice for actual deployment. The paper presents a concise survey of vehicle detection techniques used in diverse applications of video-based surveillance systems. Moreover, three main detection algorithms; Gaussian Mixture Model (GMM), Histogram of Gradients (HoG), and Adaptive motion Histograms based vehicle detection are implemented and evaluated for performance under varying illumination, traffic density and occlusion conditions. The survey provides a ready-reference for preferred vehicle detection technique under different applications.

11 citations

Proceedings ArticleDOI
22 Nov 2016
TL;DR: An enhanced approach for writer identification from offline Arabic handwriting samples in text-independent mode is presented where the handwriting is divided into small fragments and each fragment is represented by the histogram of oriented gradients (HOG).
Abstract: This paper 1 presents an enhanced approach for writer identification from offline Arabic handwriting samples in text-independent mode. Based on the hypothesis that graphical fragments in handwriting are individual, we propose a technique based on texture analysis where the handwriting is divided into small fragments and each fragment is represented by the histogram of oriented gradients (HOG). The set of HOG descriptors for all the fragments in the writing is used to characterize its writer. The proposed system is evaluated using writing samples of the IFN/ENIT database realizing an identification rate of 86.62% on 411 writers.

11 citations

Proceedings ArticleDOI
01 Apr 2019
TL;DR: The performance analysis of Haar-like features and Histogram of Oriented Gradients suited for detecting human presence in an image is presented here.
Abstract: The performance analysis of Haar-like features and Histogram of Oriented Gradients suited for detecting human presence in an image is presented here. The algorithms are implemented using OpenCV on an embedded platform. The algorithms were evaluated for a dataset comprising of 2850 images. The implementation details, comparison of algorithms and results obtained are discussed in detail.

11 citations

Journal ArticleDOI
TL;DR: A method for automatic localization of fetal brain in 3 T MRI when the images are acquired as a stack of 2D slices that are misaligned due to fetal motion, which is robust and does not rely on any prior knowledge of fetal head development.
Abstract: Fetal brain magnetic resonance imaging (MRI) is a rapidly emerging diagnostic imaging tool. However, automated fetal brain localization is one of the biggest obstacles in expediting and fully automating large-scale fetal MRI processing. We propose a method for automatic localization of fetal brain in 3 T MRI when the images are acquired as a stack of 2D slices that are misaligned due to fetal motion. First, the Histogram of Oriented Gradients (HOG) feature descriptor is extended from 2D to 3D images. Then, a sliding window is used to assign a score to all possible windows in an image, depending on the likelihood of it containing a brain, and the window with the highest score is selected. In our evaluation experiments using a leave-one-out cross-validation strategy, we achieved 96% of complete brain localization using a database of 104 MRI scans at gestational ages between 34 and 38 weeks. We carried out comparisons against template matching and random forest based regression methods and the proposed method showed superior performance. We also showed the application of the proposed method in the optimization of fetal motion correction and how it is essential for the reconstruction process. The method is robust and does not rely on any prior knowledge of fetal brain development.

11 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
89% related
Convolutional neural network
74.7K papers, 2M citations
87% related
Deep learning
79.8K papers, 2.1M citations
87% related
Image segmentation
79.6K papers, 1.8M citations
87% related
Feature (computer vision)
128.2K papers, 1.7M citations
86% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022181
2021116
2020189
2019179
2018240