scholarly journals Content-Sensitive Superpixel Generation with Boundary Adjustment

2020 ◽  
Vol 10 (9) ◽  
pp. 3150
Author(s):  
Dong Zhang ◽  
Gang Xie ◽  
Jinchang Ren ◽  
Zhe Zhang ◽  
Wenliang Bao ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computational Efficiency ◽  
Experimental Results ◽  
Evaluation Metrics ◽  
Local Entropy ◽  
Generation Algorithm ◽  
Amount Of Information ◽  
Adjustment Strategy ◽  
Computer Vision Applications

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics.

scholarly journals NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency

2020 ◽  
Vol 10 (12) ◽  
pp. 4415 ◽  
Author(s):  
Cheng Li ◽  
Baolong Guo ◽  
Geng Wang ◽  
Yan Zheng ◽  
Yang Liu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Computational Efficiency ◽  
Batch Processing ◽  
Experimental Results ◽  
Processing Strategy ◽  
Trade Off ◽  
Quantitative Metrics ◽  
Visual Tasks ◽  
Pixel Correlation ◽  
Region Description

Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important for advanced visual tasks. In this work, two strategies are presented on conventional simple non-iterative clustering (SNIC) framework, aiming to improve the computational efficiency as well as segmentation performance. Firstly, inter-pixel correlation is introduced to eliminate the redundant inspection of neighboring elements. In addition, it strengthens the color identity in complicated texture regions, thus providing a desirable trade-off between runtime and accuracy. As a result, superpixel centroids are evolved more efficiently and accurately. For further accelerating the framework, a recursive batch processing strategy is proposed to eliminate unnecessary sorting operations. Therefore, a large number of neighboring elements can be assigned directly. Finally, the two strategies result in a novel synergetic non-iterative clustering with efficiency (NICE) method based on SNIC. Experimental results verify that it works 40% faster than conventional framework, while generating comparable superpixels for several quantitative metrics—sometimes even better.

scholarly journals Product sub-vector quantization for feature indexing

2019 ◽  
Vol 35 (1) ◽  
pp. 69-83
Author(s):  
The-Anh Pham ◽  
Dinh-Nghiep Le ◽  
Thi-Lan-Phuong Nguyen
Keyword(s):  
Computer Vision ◽  
Vector Quantization ◽  
Experimental Results ◽  
Memory Allocation ◽  
Matching Process ◽  
Computer Vision Applications ◽  
Clustering Tree

This work addresses the problem of feature indexing to significantly accelerate the matching process which is commonly known as a cumbersome task in many computer vision applications. To this aim, we propose to perform product sub-vector quantization (PSVQ) to create finer representation of  underlying data while still maintaining reasonable memory allocation. In addition, the quantized data can be  jointly used with a clustering tree to perform approximate nearest search very efficiently. Experimental results demonstrate the superiority of the proposed method for different datasets in comparison with other methods.

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

2020 ◽  
pp. 104-117
Author(s):  
Osman Hürol Türkakın
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Current Literature ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

scholarly journals An Automated Approach to Mitigate Transcription Errors in Braille Texts for the Portuguese Language

2021 ◽  
Author(s):  
André Roberto Ortoncelli ◽  
Marlon Marcon ◽  
Franciele Beal
Keyword(s):  
Higher Education ◽  
Image Processing ◽  
Computer Vision ◽  
Experimental Results ◽  
Selection Function ◽  
Quota System ◽  
Synthetic Images ◽  
Lack Of Knowledge

The quota system in Brazil made it possible to include blind studentsin higher education. Teachers’ lack of knowledge about the braillesystem can represent a barrier between them and students who useit for writing and reading. Computer-vision-based transcriptionsolutions represent mechanisms for reducing understanding restrictionson this system. However, such tools face nuisances inherentto image processing systems, e.g., illumination, noise, and scale,harming the result. This paper presents an automated approachto mitigate transcription errors in braille texts for the Portugueselanguage. We propose a selection function, combined with dictionaries,that provides the best correspondence of words based ontheir braille representation. We validated our proposal on a datasetof synthetic images by submitting them to different noise levelsand testing the proposal’s robustness. Experimental results confirmthe effectiveness of the solution compared to a standard approach.As a contribution of this paper, we expect to provide a method tosupport robust and adaptable solutions to real use conditions.

scholarly journals Current research opportunities of image processing and computer vision

2019 ◽  
Vol 20 (4) ◽  
Author(s):  
Abhishek Gupta
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Literature Survey ◽  
Electromagnetic Spectrum ◽  
Research Needs ◽  
Research Opportunities ◽  
Current Trends ◽  
Challenges And Opportunities ◽  
Computer Vision Applications ◽  
Research Students

Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further. 

scholarly journals 3D Mathematical Modelling Technology in Visual Rehearsal System of Sports Dance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Chen Chen ◽  
Fahd S Alotaibi ◽  
Saeed Aldulaimi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Mathematical Modelling ◽  
Research Topic ◽  
Experimental Results ◽  
Dance Movement ◽  
Human Animation ◽  
Computer Vision Technology ◽  
Definition Of ◽  
Video Image Processing

Abstract Computer vision technology and video image processing technology in the visual rehearsal of sports dance is a hot research topic. Based on this research background, the thesis uses 3D mathematical modelling technology to interpolate and extract the captured sports and dance movement information to make the final synthesised human animation natural, smooth and lifelike. At the same time, the thesis realises the method of action cohesion through the definition of characteristic action unit attributes and association constraints. Then, it applies it to the visual rehearsal system of sports dance. Finally, the analysis of experimental results proves that the proposed method can improve the precision and recall of rehearsal.

scholarly journals Computer vision applications for coronagraphic optical alignment and image processing

2013 ◽  
Vol 52 (14) ◽  
pp. 3394 ◽  
Author(s):  
Dmitry Savransky ◽  
Sandrine J. Thomas ◽  
Lisa A. Poyneer ◽  
Bruce A. Macintosh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Optical Alignment ◽  
Computer Vision Applications
Sign in / Sign up

Export Citation Format

Share Document