فهرست مقالات Histogram of oriented gradient

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  1 - Centroid Distance Shape Recognition for Real Time Low Complexity Traffic Sign Recognition
  Hamidreza Emami Ramin Shaghaghi Kandowan Seyyed Abolfazl Hosseini
  This paper represents advantages of using Centroid distance function for shape detection in real time traffic sign recognition compared with extracting histogram of oriented gradients (HOG) features and using support vector machine (SVM) classifier. Simulation results o چکیده کامل

  This paper represents advantages of using Centroid distance function for shape detection in real time traffic sign recognition compared with extracting histogram of oriented gradients (HOG) features and using support vector machine (SVM) classifier. Simulation results of using centroid distance show similar accuracy in compare with HOG SVM while have very low complexity and cost and running with higher speed. پرونده مقاله
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  2 - Improving Face Recognition Rate Based on Histogram of Oriented Gradients and Difference of Gaussian
  Sahar Iranpour Mobarakeh Mehran Emadi
  Face recognition is a widely used identification method in the machine learning field because face biometrics are distinctive enough for detection and have more accessibility compared to other biometrics. Despite their merits, face biometrics have various challenges. Ma چکیده کامل

  Face recognition is a widely used identification method in the machine learning field because face biometrics are distinctive enough for detection and have more accessibility compared to other biometrics. Despite their merits, face biometrics have various challenges. Mainly, these challenges are divided into local and global categories. Local challenges can be addressed using sustainable methods against change while global challenges such as illumination challenges require powerful pre-processing methods. Therefore, in this study, a sustainable method against light changes has been proposed. In this method, two stages of the Difference of Gaussian have been utilized for the illumination normalization. Then, the features of the normalized image are extracted using Histogram of Oriented Gradient (HOG) and the feature vectors are classified using 3 k-nearest neighbor classifiers and the support vector machine with linear kernel, and the support vector machine with Radial Basis Function (RBF) kernel. Testing the proposed method on Computer Vision and Biometric Laboratory (CVBL) data indicated that the recognition rate, at best for the illumination challenge in the whole face and a part of the face is 98.6 % and 97.9% respectively. پرونده مقاله
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  3 - Visual Tracking using Learning Histogram of Oriented Gradients by SVM on Mobile Robot
  Iman Zabbah Shima Foolad Ali Maroosi Alireza Pourreza
  The intelligence of a mobile robot is highly dependent on its vision. The main objective of an intelligent mobile robot is in its ability to the online image processing, object detection, and especially visual tracking which is a complex task in stochastic environments. چکیده کامل

  The intelligence of a mobile robot is highly dependent on its vision. The main objective of an intelligent mobile robot is in its ability to the online image processing, object detection, and especially visual tracking which is a complex task in stochastic environments. Tracking algorithms suffer from sequence challenges such as illumination variation, occlusion, and background clutter, so an accurate tracker should employ the appropriate visual features to identify target. In this paper, we propose using the histogram of oriented gradient (HOG), as an important descriptor. The descriptor simulates the performance of the complex cells in the primary visual cortex (V1) and it has low sensitivity to the illumination changes. In the proposed method, firstly, an object model is generated by training the HOG of multi first frames via an SVM classifier. Then, in order to track a new frame, the HOG descriptors are extracted from the surrounding areas of the target in the previous frame and convolved with the object model. Finally, the location with the highest score is defined as the target. The experimental results demonstrate the proposed method has significant performance compare to the state-of-the-art methods. Furthermore, we apply our algorithm to the mobile robot built by the robotics team to ensure its performance in a real environment. پرونده مقاله