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.

Devanagari Ancient Character Recognition using HOG and DCT Features

Abstract: In the present work, a system for recognition of ancient documents in Devanagari script is presented. Two feature extraction techniques, namely, DCT(Discrete Cosine Transformation) zigzag features and Histogram of oriented gradients are considered for extracting features of Devanagari ancient manuscripts. For recognition, three classification techniques, namely, SVM (Support Vector Machine), decision tree, and Naive Bayes are used. A database for the experiments is collected from various libraries and museums. Using SVM classifier with RBF kernel, a recognition accuracy of 90.70% with DCT zigzag feature vector of length 100 has been reported. A recognition accuracy of 90.70% with a partitioning strategy of dataset (80% data as training data and the remaining 20% data as testing data) has been achieved.

People detection in heavy machines applications

Abstract: In this paper we focus on improving the performance of people detection algorithm on fish-eye images in a safety system for heavy machines. Fish-eye images give the advantage of a very wide angle-of-view, which is important in the context of heavy machines. However, the distortions in fish-eye images present many difficulties for image processing. The underlying framework of the proposed detection system uses Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). By analyzing the effect of distortions in different regions in the field-of-view and by adding artificial distortions in the training process of the binary classifier, we can obtain better detection results on fish-eye images.

Can thermal and visible image fusion improves ear recognition

Abstract: This paper intends to study whether thermal and visible image fusion improves ear recognition given illumination variations. Five fusion methods, namely, simple average, mean average, average discrete wavelet transform (DWT), optimized DWT and weighted DWT were tested. The experiments were done using DIAST Variability Illuminated Thermal and Visible Ear Image Datasets. There were two experiments conducted. The first experiment is done using images with three different illumination conditions (i.e. dark, average and bright illuminations) while the second experiment only considers average and brightly illuminated image. The evaluation was done based on ear recognition accuracy where features were extracted based on the histogram of oriented gradients (HOG) while support vector machine (SVM) was used as the classifier. In Experiment 1, thermal images performed best with 96.36% recognition rate while visible images was the lowest with 62.73% accuracy. In Experiment 2, all three DWT fusions scored 95.45% accuracy, surpassing thermal image (94.55%) while visible image still is the lowest with 90.00% recognition rate.

Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

Abstract: Computer vision applications face various challenges while detection and classification of objects in real world like large variation in appearances, cluttered back ground, noise, occlusion, low illumination etc.. In this paper a Wavelet based Histogram of Oriented Gradients (WHOG) feature descriptors are proposed to represent shape information by storing local gradients in image. This results in enhanced representation of shape information. The performance of the feature descriptors are tested on multiclass image data set having partial occlusion, different scales and rotated object images. The performance of WHOG feature based object classification is compared with HOG feature based classification. The matching of test image with its learned class is performed using Back Propagation Neural Network (BPNN) algorithm. Proposed features not only performed superior than HOG but also beat wavelet, moment invariant and Curvelet.

Handwritten Kannada Numerals Recognition Using Histogram of Oriented Gradient Descriptors and Support Vector Machines

Abstract: The role of a good feature extractor is to represent an object using numerical measurements. A good feature extractor should generate the features, which must support the classifier to classify similar objects into one category and the distinct objects into separate category. In this paper, we present a method based on HOG (Histogram of Oriented Gradients) for the recognition of handwritten kannada numerals. HOG descriptors are proved to be invariant to geometric transformation and hence they are one among the best descriptors for character recognition. We have used Multi-class Support Vector Machines (SVM) for the classification. The proposed algorithm is experimented on 4,000 images of isolated handwritten Kannada numerals and an average accuracy of 95% is achieved.