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.

Elevator button and floor number recognition through hybrid image classification approach for navigation of service robot in buildings

Abstract: To successfully move a robot into the building, the elevator button and elevator floor number detection and recognition can play an important role. It can help a robot move in the building, just as it also can help a visually impaired person who wants to move another floor in the building. Due to vision-based approach, the difference in lighting condition and the complex background are the main obstacles in this research. A hybrid image classification model is presented in this research to overcome all these difficulties. This hybrid model is the combination of histogram of oriented gradients and bag of words models, which later reduces the dimension of image features by using the feature selection algorithm. An artificial neural network has been implemented to get the experimental result by training and testing. In order to get training performance, 1000 training image samples have been used and additional 1000 image samples also been used to get the testing performance. The experimental results of this research indicate that this proposed framework is important for real-time implementation to implement the elevator button and elevator floor number recognition framework.

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Abstract: In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG) algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs), by considerably reducing the area (thus increasing the level of parallelism), while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.

HOG feature based SVM classification of glaucomatous fundus image with extraction of blood vessels

Abstract: Glaucoma at the later stages causes eye blindness, it is essential for early detection to minimize the risks and warn patients who might eventually lose their vision. The study gives an assessment on Glaucoma detection using the Histogram of Oriented Gradients (HOG) Feature extraction along with SVM classification of retinal fundus image with the extraction of blood vessels using Gabor filter. Retinal fundus images are reduced in their dimension. Then the blood vessels are extracted by using Gabor filter, morphological operations and thresholding. This improves the accuracy. Textural features within the images are used for accurate and efficient glaucoma classification. The image is then given to a HOG feature descriptor. The feature sets (statistical properties) are then extracted from the HOG Features that are used for Support Vector Machine classification of glaucomatous images.

Football player detection in video broadcast

Abstract: The paper describes a novel segmentation system based on the combination of Histogram of Oriented Gradients (HOG) descriptors and linear Support Vector Machine (SVM) classification for football video. Recently, HOG methods were widely used for pedestrian detection. However, presented experimental results show that combination of HOG and SVM is very promising for locating and segmenting players. In proposed system a dominant color based segmentation for football playfield detection and a 3D playfield modeling based on Hough transform is introduced. Experimental evaluation of the system is done for SD (720×576) and HD (1280×720) test sequences. Additionally, we test proposed system performance for different lighting conditions (non-uniform pith lightning, multiple player shadows) as well as for various positions of the cameras used for acquisition.

A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules

Abstract: In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network (DCNN) method is used for feature extraction and hybridize as combination of Convolutional Neural Network (CNN), Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradients (ExHOG) and Local Binary Pattern (LBP). A combination of shape, texture, scaling, rotation, translation features extracted using HOG, LBP and CNN. The Homogeneous descriptors used to extract the feature of lung images from Lung Image Database Consortium (LIDC) are given to classifiers Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree and Random Forest to classify nodules and non-nodules. Experimental results demonstrate the effectiveness of the proposed method in terms of accuracy which gives best result than the competing methods.