scholarly journals EMAN: The Human Visual Feature Based No-Reference Subjective Quality Metric

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 46152-46164
Author(s):  
Pallab Kanti Podder ◽  
Manoranjan Paul ◽  
Manzur Murshed
Keyword(s):  
Subjective Quality ◽  
Visual Feature ◽  
Quality Metric ◽  
Feature Based

An Efficient Algorithm for Improved Shape Context Feature's Cost Matrix Computation

2012 ◽  
Vol 532-533 ◽  
pp. 1631-1635
Author(s):  
Shan Shan Li ◽  
Ying Hai Zhao ◽  
Jiang An Wang
Keyword(s):  
Efficient Method ◽  
Fourier Transformation ◽  
Time Complexity ◽  
Visual Feature ◽  
Shape Context ◽  
Rotation Invariant ◽  
Matrix Computation ◽  
Cost Matrix ◽  
Feature Based ◽  
The Cost

Shape context is not rotation invariant as a local visual feature. To solve this problem, 2-D and 1-D Fourier Transformation has been performed on the feature. Based on the property of Fourier Transformation, a fast and efficient method is presented in the cost matrix computation of these improved shape context feature. The analysis shows the time complexity is much lower and the experiments show effective and efficiency of this new algorithm.

scholarly journals A Visual Object Tracking Algorithm Based on Dynamics Pattern and Convolutional Feature

2019 ◽  
Vol 37 (6) ◽  
pp. 1310-1319
Author(s):  
Boyan Zhang ◽  
Yong Zhong ◽  
Zhendong Li
Keyword(s):  
Short Term Memory ◽  
Motion Blur ◽  
Feature Representation ◽  
Visual Object ◽  
Visual Feature ◽  
Illumination Variation ◽  
Time Step ◽  
Bounding Box ◽  
Feature Based ◽  
Lstm Network

Deep visual feature-based method has demonstrated impressive performance in visual tracking attributing to its powerful capability of visual feature representation. However, in some complex environments such as dramatic change of appearance, illumination variation and rotation, the extracted deep visual feature is insufficient for accurately characterizing the target. To solve this problem, we present an integrated tracking framework which combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). Firstly, the LSTM extracted dynamics feature of target on time sequence, resulting the state of target at present time step. With that state, the accurate preprocessed bounding box was obtained. Then, deep convolutional feature of the target was extracted using a CNN, based on the processed bounding box. Finally, the position of the target was determined based on the score of the feature. During tracking stage, in order to improve the adaptation of the network, the parameters of the network were updated using samples of the target captured while successful tracking. The experiment shows that the proposed method achieves outstanding tracking performance and robustness in cases of partial occlusion, out-of-view, motion blur and fast motion.

Keypoints-Based Image Passive Forensics Method for Copy-Move Attacks

2016 ◽  
Vol 30 (03) ◽  
pp. 1655008 ◽  
Author(s):  
Xiaofeng Wang ◽  
Guanghui He ◽  
Chao Tang ◽  
Yali Han ◽  
Shangping Wang
Keyword(s):  
Block Matching ◽  
Duplicate Region ◽  
Detection Accuracy ◽  
Visual Feature ◽  
Region Detection ◽  
Computation Efficiency ◽  
Passive Forensics ◽  
Harris Detector ◽  
Feature Based ◽  
Copy Move Forgery Detection

A novel image passive forensics method for copy-move forgery detection is proposed. The proposed method combines block matching technology and feature point matching technology, and breaks away from the general framework of the visual feature-based approach that used local visual feature such as SIFT and followed by a clustering procedure to group feature points that are spatially close. In our work, image keypoints are extracted using Harris detector, and the statistical features of keypoint neighborhoods are used to generate forensics features. Then we proposed a new forensics features matching approach, in which, a region growth technology and a mismatch checking approach are developed to reduce mismatched keypoints and improve detected accuracy. We also develop a duplicate region detection method based on the distance frequency of corresponding keypoint pairs. The proposed method can detect duplicate regions for high resolution images. It has higher detection accuracy and computation efficiency. Experimental results show that the proposed method is robust for content-preserving manipulations such as JPEG compression, gamma adjustment, filtering, luminance enhancement, blurring, etc.

scholarly journals Visual Feature Based Image Forgery Detection

2018 ◽  
Vol 7 (4.6) ◽  
pp. 86
Author(s):  
D. Vaishnavi ◽  
D. Mahalakshmi ◽  
Venkata Siva Rao Alapati
Keyword(s):  
False Positive Rate ◽  
True Positive Rate ◽  
True Positive ◽  
Visual Feature ◽  
Forgery Detection ◽  
Digital Form ◽  
Essential Information ◽  
Positive Rate ◽  
Speed Up ◽  
Feature Based

In present days, the images are building up in digital form and which may hold essential information. Such images can be voluntarily forged or manipulated using the image processing tools to abuse it. It is very complicated to notice the forgery by naked eyes. In particular, the copy move forgery is enormously demanding one to expose. Hence, this paper put forwards a method to determine the copy move forgery by extracting the visual feature called speed up robust features (SURF). In the direction to quantitatively analyze the performance, the metrics namely false positive rate and true positive rate are estimated and also comparative study is carried out by previous existing methods.  

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