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 published on a yearly basis
Papers
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TL;DR: This paper presents first of its kind, a full scale industrial system which can read tyre codes when installed along driveways such as at gas stations or parking lots with vehicles driving under 10mph, and shows promise for the intended application.
Abstract: This paper addresses the challenge of reading low contrast text on tyre sidewall images of vehicles in motion. It presents first of its kind, a full scale industrial system which can read tyre codes when installed along driveways such as at gas stations or parking lots with vehicles driving under 10 mph. Tyre circularity is first detected using a circular Hough transform with dynamic radius detection. The detected tyre arches are then unwarped into rectangular patches. A cascade of convolutional neural network (CNN) classifiers is then applied for text recognition. Firstly, a novel proposal generator for the code localization is introduced by integrating convolutional layers producing HOG-like (Histogram of Oriented Gradients) features into a CNN. The proposals are then filtered using a deep network. After the code is localized, character detection and recognition are carried out using two separate deep CNNs. The results (accuracy, repeatability and efficiency) are impressive and show promise for the intended application.
18 citations
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28 Jan 2014TL;DR: A feature-fusion method based on Canonical Correlation Analysis (CCA) for facial-expression recognition where features from the eye and the mouth windows are extracted separately, which are correlated with each other in representing a facial expression.
Abstract: In this paper, we propose a feature-fusion method based on Canonical Correlation Analysis (CCA) for facial-expression recognition. In our proposed method, features from the eye and the mouth windows are extracted separately, which are correlated with each other in representing a facial expression. For each of the windows, two effective features, namely the Local Phase Quantization (LPQ) and the Pyramid of Histogram of Oriented Gradients (PHOG) descriptors, are employed to form low-level representations of the corresponding windows. The features are then represented in a coherent subspace by using CCA in order to maximize the correlation. In our experiments, the Extended Cohn-Kanade dataset is used; its face images span seven different emotions, namely anger, contempt, disgust, fear, happiness, sadness, and surprise. Experiment results show that our method can achieve excellent accuracy for facial-expression recognition.
18 citations
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TL;DR: The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.
Abstract: Breast cancer detection is the most challenging aspect in the field of health monitoring system. In this paper, breast cancer detection was assessed by employing Mammographic Image Analysis Society (MIAS) dataset. The proposed approach contains four major steps, namely, image‐preprocessing, segmentation, feature extraction, and classification. Initially, Laplacian filtering was utilized to identify the area of edges in mammogram images and, also, it was very sensitive to noise. Then, segmentation was carried‐out using modified‐Adaptively Regularized Kernel‐based Fuzzy‐C‐Means (ARKFCM); it was a flexible high level machine learning technique to localize the object in complex template. In conventional ARKFCM, it was hard to segment the ill‐defined masses in mammogram images. To address this concern, the Euclidean distance in ARKFCM was replaced by correlation function in order to improve the segmentation efficiency. The hybrid feature extraction (Histogram of Oriented Gradients (HOG), homogeneity, and energy) was performed on the segmented cancer region to extract feature subsets. The respective feature values were given as the input for a multi‐objective classifier: Deep Neural Network (DNN) for classifying the normal and abnormal regions in mammogram images. The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.
18 citations
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01 Jan 2017TL;DR: In this work, an offline signature identification system based on Histogram of Oriented Gradients vector features is designed and a 98.33 percent test accuracy is obtained by using the proposed method along with two-folded cross correlation.
Abstract: In this work, an offline signature identification system based on Histogram of Oriented Gradients (HOG) vector features is designed. Handwritten signature images are collected at Yildiz Technical University, from 15 people, 40 samples from each. Before the HOG feature extraction, size fixing and noise reduction processes are applied to all signature images. HOG features are extracted from the noiseless same sized images. In order to prevent the waste of processing time and to eliminate the redundant features, PCA is applied to the dataset. Obtained dataset is used to train the GRNN. As a result, a 98.33 percent test accuracy is obtained by using the proposed method along with two-folded cross correlation.
18 citations
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01 Oct 2014TL;DR: This paper proposes a new feature based on HOG to be used with the discriminatively trained part framework for pedestrian detection, based on computing the image mixed partial derivatives to is used to redefine the gradients of some pixels and to reweigh the vote at all pixels with respect to the original HOG.
Abstract: Recently, several approaches for pedestrian detection have been investigated using discriminatively trained part based models with which Histogram of Oriented Gradients (HOG) showed to be a robust feature. In this paper, we propose a new feature based on HOG to be used with the discriminatively trained part framework for pedestrian detection. Our method is based on computing the image mixed partial derivatives to be used to redefine the gradients of some pixels and to reweigh the vote at all pixels with respect to the original HOG. Our approach was tested on the PASCAL2007 and INRIA person dataset and showed to have an outstanding performance.
18 citations