Segmentation-Based Historical Handwritten Word Spotting Using Document-Specific Local Features

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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A Novel Blood Vessel Extraction Using Multiscale Matched Filters with Local Features and Adaptive Thresholding

Bioscience Biotechnology Research Communications ◽

10.21786/bbrc/13.3/18 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1104-1113

Author(s):

Duaa AlSaeed

Keyword(s):

Blood Vessel ◽

Local Features ◽

Adaptive Thresholding ◽

Matched Filters ◽

Vessel Extraction

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Robust Hashing for Image Authentication Using Zernike Moments and Local Features

IEEE Transactions on Information Forensics and Security ◽

10.1109/tifs.2012.2223680 ◽

2013 ◽

Vol 8 (1) ◽

pp. 55-63 ◽

Cited By ~ 118

Author(s):

Yan Zhao ◽

Shuozhong Wang ◽

Xinpeng Zhang ◽

Heng Yao

Keyword(s):

Image Authentication ◽

Local Features ◽

Zernike Moments

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The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

Sensors ◽

10.3390/s21103327 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3327

Author(s):

Vicente Román ◽

Luis Payá ◽

Adrián Peidró ◽

Mónica Ballesta ◽

Oscar Reinoso

Keyword(s):

Mobile Robotics ◽

A Priori ◽

Computational Cost ◽

Relevant Information ◽

Local Features ◽

New Family ◽

Lighting Conditions ◽

Omnidirectional Images ◽

Great Development

Over the last few years, mobile robotics has experienced a great development thanks to the wide variety of problems that can be solved with this technology. An autonomous mobile robot must be able to operate in a priori unknown environments, planning its trajectory and navigating to the required target points. With this aim, it is crucial solving the mapping and localization problems with accuracy and acceptable computational cost. The use of omnidirectional vision systems has emerged as a robust choice thanks to the big quantity of information they can extract from the environment. The images must be processed to obtain relevant information that permits solving robustly the mapping and localization problems. The classical frameworks to address this problem are based on the extraction, description and tracking of local features or landmarks. However, more recently, a new family of methods has emerged as a robust alternative in mobile robotics. It consists of describing each image as a whole, what leads to conceptually simpler algorithms. While methods based on local features have been extensively studied and compared in the literature, those based on global appearance still merit a deep study to uncover their performance. In this work, a comparative evaluation of six global-appearance description techniques in localization tasks is carried out, both in terms of accuracy and computational cost. Some sets of images captured in a real environment are used with this aim, including some typical phenomena such as changes in lighting conditions, visual aliasing, partial occlusions and noise.

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A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

Applied Sciences ◽

10.3390/app11052174 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2174

Author(s):

Xiaoguang Li ◽

Feifan Yang ◽

Jianglu Huang ◽

Li Zhuo

Keyword(s):

Local Features ◽

Attention Mechanism ◽

Experimental Results ◽

Dynamic Scene ◽

Feature Maps ◽

Training Scheme ◽

Real Scene ◽

Global And Local

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.

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