Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern Images

2020 ◽  
Vol 67 (5) ◽  
pp. 946-950 ◽  
Author(s):  
Wei Zhou ◽  
Shengyu Gao ◽  
Ling Zhang ◽  
Xin Lou
Keyword(s):  
Feature Extraction ◽  
Histogram Of Oriented Gradients ◽  
Bayer Pattern

Palmprint Recognition Using Fusion of Local Binary Pattern and Histogram of Oriented Gradients

2020 ◽  
pp. 22-31
Author(s):  
Hardik Agarwal ◽  
◽  
Kanika Somani ◽  
Shivangi Sharma ◽  
Prerna Arora ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Palmprint Recognition ◽  
Histogram Of Oriented Gradients ◽  
Extraction Techniques ◽  
Classification Techniques ◽  
Segmented Image ◽  
Average Accuracy

In this paper, unique features of the segmented image samples are extracted by using two major feature extraction techniques: Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). After this, these features are fused to get more precise and productive outcomes. The average accuracy of the three distinct datasets that were generated using the LBP and HOG features are determined. To calculate the accuracy of the three distinct models, classification techniques like KNN and SVM, are adopted.

scholarly journals Object Detection and Tracking using Multiple Features Extraction

2021 ◽  
Vol 10 (10) ◽  
pp. 75-79
Author(s):  
Bhavya Rudraiah* ◽  
◽  
Dr. Geetha K. S. ◽  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Extraction Methods ◽  
Vital Role ◽  
Histogram Of Oriented Gradients ◽  
Multiple Objects ◽  
Multiple Features ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
And Performance

In most of the video analysis applications, object detection and tracking play vital role. Most of detection and tracking algorithms fail to predict multiple objects with varying orientation. In this paper, the goal is to identify and track multiple objects using different feature extraction methods like Locality Sensitive Histogram, Histogram of Oriented Gradients and Edges. These features are subjected to train classifier that can detect the object of different orientations. Experimental results and performance evaluation depicts the proposed method which uses LSH performs well with an increased accuracy of 98%. This method can precisely track the object and can be utilized to track under different scale and pose variations.

scholarly journals Sketch-Based Image Retrieval with Histogram of Oriented Gradients and Hierarchical Centroid Methods

2020 ◽  
Vol 188 ◽  
pp. 00026
Author(s):  
Viny Christanti Mawardi ◽  
Yoferen Yoferen ◽  
Stéphane Bressan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Euclidean Distance ◽  
High Threshold ◽  
Histogram Of Oriented Gradients ◽  
Test Results ◽  
Average Precision ◽  
Canny Edge Detection ◽  
Image Dataset ◽  
Canny Edge

Searching images from digital image dataset can be done using sketch-based image retrieval that performs retrieval based on the similarity between dataset images and sketch image input. Preprocessing is done by using Canny Edge Detection to detect edges of dataset images. Feature extraction will be done using Histogram of Oriented Gradients and Hierarchical Centroid on the sketch image and all the preprocessed dataset images. The features distance between sketch image and all dataset images is calculated by Euclidean Distance. Dataset images used in the test consist of 10 classes. The test results show Histogram of Oriented Gradients, Hierarchical Centroid, and combination of both methods with low and high threshold of 0.05 and 0.5 have average precision and recall values of 90.8 % and 13.45 %, 70 % and 10.64 %, 91.4 % and 13.58 %. The average precision and recall values with low and high threshold of 0.01 and 0.1, 0.3 and 0.7 are 87.2 % and 13.19 %, 86.7 % and 12.57 %. Combination of the Histogram of Oriented Gradients and Hierarchical Centroid methods with low and high threshold of 0.05 and 0.5 produce better retrieval results than using the method individually or using other low and high threshold.

scholarly journals RIGID TRACKING FOR SCALE AND ROTATION VARYING TARGETS FROM MOVING CAMERA

2021 ◽  
Vol 9 (2) ◽  
pp. 175-180
Author(s):  
Karthika Pragadeeswari C., Yamuna G.
Keyword(s):  
Feature Extraction ◽  
Optical Flow ◽  
Random Sample ◽  
Computation Time ◽  
Rotation Invariance ◽  
Histogram Of Oriented Gradients ◽  
True Positive ◽  
Random Sample Consensus ◽  
Time Accuracy ◽  
Ransac Algorithm

Targets when move rapidly needed to be tracked in many significant fields such as in combat applications. Objects undergoes many scale changes and also undergoes rotation variance. The target when viewed from static position, the size becomes smaller as the target moves farther and farther. Tracking the targets needs more attention and this can be done by Improved optical flow to which feature extraction through Histogram of Oriented Gradients and Random Sample Consensus (RANSAC) algorithm for scale and rotation invariance is added. The performance of the method is measured by its computation time, accuracy and high true positive values and other related parameters simulated in MAT LAB.

scholarly journals Classification of handwritten Javanese script using random forest algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 1308-1315
Author(s):  
Mohammad Arif Rasyidi ◽  
Taufiqotul Bariyah ◽  
Yohanes Indra Riskajaya ◽  
Ayunda Dwita Septyani
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Predictive Model ◽  
Histogram Of Oriented Gradients ◽  
Random Forest Algorithm ◽  
Forest Model ◽  
Black And White ◽  
Image Conversion ◽  
Image Dataset

The ability to read and write Javanese scripts is one of the most important competencies for students to have in order to preserve the Javanese language as one of the Indonesian cultures. In this study, we developed a predictive model for 20 Javanese characters using the random forest algorithm as the basis for developing Javanese script learning media for students. In building the model, we used an extensive handwritten image dataset and experimented with several different preprocessing methods, including image conversion to black-and-white, cropping, resizing, thinning, and feature extraction using histogram of oriented gradients. From the experiment, it can be seen that the resulting random forest model is able to classify Javanese characters very accurately with accuracy, precision, and recall of 97.7%.

Sign in / Sign up

Export Citation Format

Share Document