scholarly journals Enhancing the Human with Computers: Ongoing research at the Computer Graphics, Image Processing and Interaction Group

2011 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Luciana Nedel ◽  
Anderson Maciel ◽  
Carla Dal Sasso Freitas ◽  
Claudio Jung ◽  
Manuel Oliveira ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Graphics ◽  
Data Visualization ◽  
Human Perception ◽  
Natural World ◽  
Massive Data ◽  
3D Interaction ◽  
Ongoing Research ◽  
Surgery Planning

The Computer Graphics, Image Processing and Interaction (CGIP) group at UFRGS concentrates expertise from many different and complementary graphics related domains. In this paper we introduce the group and present our re- search lines and some ongoing projects. We selected mainly the projects related to 3D interaction and navigation, which includes applications as massive data visualization, surgery planning and simulation, tracking and computer vision algorithms, and modeling approaches for human perception and natural world.

Automatic Splicing Technology in Image Splicing Processing

2014 ◽  
Vol 1014 ◽  
pp. 367-370
Author(s):  
Xiao Bo Yu ◽  
Yun Feng Zhang ◽  
Yue Gang Fu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Computer Graphics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Research Field ◽  
Important Research ◽  
Image Splicing ◽  
Manual Intervention

Automatic splicing technology is all important research field of image processing, and has become a research focusing on the computer vision and computer graphics,and has important practical value in the fields of image splicing processing, medical image analysis and so on.On the basis of a linear transition method, this paper presents an algorithm which realizes to diminish the seams in overlap region according to the content of scenes.This algorithm avoids manual intervention during the mosaic process.With the help of automatic splicing technology based on the overlapping areas linear transition, the requirement of seamless image splicing can be met. 1.Introduction

scholarly journals AN OPTIMIZED ALGORITHM FOR COMPUTING THE VORONOI SKELETON

2020 ◽  
pp. 542-554
Author(s):  
Dmytro Kotsur ◽  
Vasyl Tereshchenko
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Data Structure ◽  
Computer Graphics ◽  
Voronoi Diagram ◽  
Execution Time ◽  
Efficient Algorithms ◽  
Computational Experiments

The skeleton-based representation is widely used in such fields as computer graphics, computer vision and image processing. Therefore, efficient algorithms for computing planar skeletons are of high relevance. In this paper, we propose an optimized algorithm for computing the Voronoi skeleton of a planar object with holes, which is represented by a set of polygons. Such skeleton allows us to use efficiently the properties of the underlying Voronoi diagram data structure. It was shown that the complexity of the proposed Voronoi-based skeletonization algorithm is O(N log N), where N is the number of polygon’s vertices. We have also proposed theoretically justified optimization heuristic based on polygon/polyline simplification algorithms. In order to evaluate and prove the efficiency of the heuristic, a series of computational experiments were conducted involving the polygons obtained from MPEG 7 CE-Shape-1 dataset. We have measured the execution time of the skeletonization algorithm, computational overheads related to the introduced heuristics and also the influence of the heuristic onto accuracy of the resulting skeleton. As a result, we have established the criteria, which allow us to choose the optimal heuristics for our skeletonization algorithm depending on the system’s requirements.

scholarly journals Computer Vision and Image Processing: A Paper Review

2018 ◽  
Vol 2 (1) ◽  
pp. 22 ◽  
Author(s):  
Victor Wiley ◽  
Thomas Lucas
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Theoretical Concept ◽  
Field Application ◽  
Massive Data ◽  
Application Domain ◽  
Data Recording ◽  
Paper Review ◽  
Wide Usage

Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information,    understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.

TEACHING IMAGE COMPUTATION: FROM COMPUTER GRAPHICS TO COMPUTER VISION

2001 ◽  
Vol 15 (05) ◽  
pp. 823-831 ◽  
Author(s):  
BRUCE A. DRAPER ◽  
J. ROSS BEVERIDGE
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Graphics ◽  
Feature Matching ◽  
Perspective Projection ◽  
Image Generation ◽  
Coordinate Systems ◽  
Basic Image ◽  
Components Analysis ◽  
Image Computation

This paper describes a course in image computation that is designed to follow and build up an established course in computer graphics. The course is centered on images: how they are generated, manipulated, matched and symbolically described. It builds on the student's knowledge of coordinate systems and the perspective projection pipeline. It covers image generation techniques not covered by the computer graphics course, most notably ray tracing. It introduces students to basic image processing concepts such as Fourier analysis and then to basic computer vision topics such as principal components analysis, edge detection and symbolic feature matching. The goal is to prepare students for advanced work in either computer vision or computer graphics.

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