Indexing SURF Features by SVD Based Basis on GPU with Multi-Query Support

2014 ◽  
pp. 422-433 ◽  
Author(s):  
Vibha Patel ◽  
Bhavin Patel
Keyword(s):  
Surf Features

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

Complex Printed Uyghur Document Image Retrieval Based on Modified SURF Features

2018 ◽  
pp. 99-111
Author(s):  
Aliya Batur ◽  
Patigul Mamat ◽  
Wenjie Zhou ◽  
Yali Zhu ◽  
Kurban Ubul
Keyword(s):  
Image Retrieval ◽  
Document Image ◽  
Surf Features

scholarly journals An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 706 ◽  
Author(s):  
Chengyou Wang ◽  
Zhi Zhang ◽  
Xiao Zhou
Keyword(s):  
Feature Detection ◽  
Joint Photographic Expert Group ◽  
Social Stability ◽  
Forgery Detection ◽  
Detection Scheme ◽  
Speeded Up Robust Features ◽  
Image Blurring ◽  
Surf Features ◽  
Copy Move Forgery Detection ◽  
Processing Techniques

The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.

Detecting Eyes and Lips Using Neural Networks and SURF Features

2012 ◽  
pp. 338-354 ◽  
Author(s):  
Artem A. Lenskiy ◽  
Jong-Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Success Rate ◽  
Detection Rate ◽  
Facial Feature ◽  
Speed Up ◽  
Surf Features ◽  
Left And Right ◽  
The Right

In this chapter, the authors elaborate on the facial image segmentation and the detection of eyes and lips using two neural networks. The first neural network is applied to segment skin-colors and the second to detect facial features. As for input vectors, for the second network the authors apply speed-up robust features (SURF) that are not subject to scale and brightness variations. The authors carried out the detection of eyes and lips on two well-known facial feature databases, Caltech. and PICS. Caltech gave a success rate of 92.4% and 92.2% for left and right eyes and 85% for lips, whereas the PCIS database gave 96.9% and 95.3% for left and right eyes and 97.3% for lips. Using videos captured in real environment, among all videos, the authors achieved an average detection rate of 94.7% for the right eye and 95.5% for the left eye with a 86.9% rate for the lips

Shot segmentation technology based on SURF features and SIFT features

2019 ◽  
Vol 34 (5) ◽  
pp. 521-529
Author(s):  
张昊骕 ZHANG Hao-su ◽  
朱晓龙 ZHU Xiao-long ◽  
胡新洲 HU Xin-zhou ◽  
任洪娥 REN Hong-e
Keyword(s):  
Shot Segmentation ◽  
Surf Features ◽  
Sift Features

A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations

2019 ◽  
Vol 78 (24) ◽  
pp. 34437-34457 ◽  
Author(s):  
Abdel-Aziz I. M. Hassanin ◽  
Fathi E. Abd El-Samie ◽  
Ghada M. El Banby
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Morphological Operations ◽  
Surf Features

Advanced SURF Features Based Flexible Object Detection

2019 ◽  
Author(s):  
Di Lv ◽  
Yunfu Deng ◽  
Zhihao Li ◽  
Qujiang Lei ◽  
Bo Liang ◽  
...  
Keyword(s):  
Object Detection ◽  
Surf Features
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