An Efficacious Matching of Finger Knuckle Print Images Using Gabor Feature

2017 ◽  
pp. 153-162
Author(s):  
Nivedita Bhattacharya ◽  
Deepak Kumar Dewangan ◽  
Kranti Kumar Dewangan
Keyword(s):  
Gabor Feature

scholarly journals Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

2021 ◽  
Vol 13 (4) ◽  
pp. 721
Author(s):  
Zhongheng Li ◽  
Fang He ◽  
Haojie Hu ◽  
Fei Wang ◽  
Weizhong Yu
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Collaborative Representation ◽  
Detection Accuracy ◽  
Spatial Features ◽  
Collective Representation ◽  
Detection Approach ◽  
Adaptive Weight ◽  
Gabor Feature

Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD models only consider a single spectral or spatial feature of the hyperspectral image (HSI), which is unhelpful for improving detection accuracy. To solve these problems, in terms of speed and accuracy, we propose a novel anomaly detection approach, named Random Collective Representation-based Detector with Multiple Feature (RCRDMF). This method includes the following steps. This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the HSI image, which enables us to improve the accuracy of HAD by combining the multiple spectral and spatial features. The ensemble and random collaborative representation detector (ERCRD) method is then applied, which can improve the anomaly detection speed. Finally, an adaptive weight approach is proposed to calculate the weight for each feature. Experimental results on six hyperspectral datasets demonstrate that the proposed approach has the superiority over accuracy and speed.

Accelerating Multiresolution Gabor Feature Extraction for Real Time Vision Applications

2014 ◽  
Vol 76 (2) ◽  
pp. 149-168 ◽  
Author(s):  
Yong Cheol Peter Cho ◽  
Nandhini Chandramoorthy ◽  
Kevin M. Irick ◽  
Vijaykrishnan Narayanan
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Gabor Feature ◽  
Gabor Feature Extraction

scholarly journals Handwritten English Character Recognition and translate English to Devnagari Words

2019 ◽  
pp. 142-151
Author(s):  
Shivali Parkhedkar ◽  
Shaveri Vairagade ◽  
Vishakha Sakharkar ◽  
Bharti Khurpe ◽  
Arpita Pikalmunde ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Character Recognition ◽  
Computational Time ◽  
Feature Vectors ◽  
Form Processing ◽  
Knn Classifier ◽  
Gabor Feature ◽  
Handwritten Document ◽  
The Individual

In our proposed work we will accept the challenges of recognizing the words and we will work to win the challenge. The handwritten document is scanned using a scanner. The image of the scanned document is processed victimization the program. Each character in the word is isolated. Then the individual isolated character is subjected to “Feature Extraction” by the Gabor Feature. Extracted features are passed through KNN classifier. Finally we get the Recognized word. Character recognition is a process by which computer recognizes handwritten characters and turns them into a format which a user can understand. Computer primarily based pattern recognition may be a method that involves many sub process. In today’s surroundings character recognition has gained ton of concentration with in the field of pattern recognition. Handwritten character recognition is beneficial in cheque process in banks, form processing systems and many more. Character recognition is one in all the favored and difficult space in analysis. In future, character recognition creates paperless environment. The novelty of this approach is to achieve better accuracy, reduced computational time for recognition of handwritten characters. The proposed method extracts the geometric features of the character contour. These features are based on the basic line types that forms the character skeleton. The system offers a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked. The algorithm proposed concentrates on the same. It extracts totally different line varieties that forms a specific character. It conjointly also concentrates on the point options of constant. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the proposed method.

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