Pyramid mappings onto hypercubes for computer vision: Connection machine comparative study

1993 ◽  
Vol 5 (6) ◽  
pp. 471-489 ◽  
Author(s):  
Sotirios G. Ziavras ◽  
Muhammad A. Siddiqui
Keyword(s):  
Computer Vision ◽  
Comparative Study ◽  
Connection Machine

Comparative Study of Computer Vision Models for Insect Pest Identification in Complex Backgrounds

2019 ◽  
Author(s):  
Gabriel L. Tenorio ◽  
Felipe F. Martins ◽  
Thiago M. Carvalho ◽  
Antonio C. Leite ◽  
Karla Figueiredo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Comparative Study ◽  
Insect Pest

scholarly journals A comparative study of grayscale conversion techniques applied to SIFT descriptors

2015 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Samuel Macêdo ◽  
Givânio Melo ◽  
Judith Kelner
Keyword(s):  
Computer Vision ◽  
Augmented Reality ◽  
Comparative Study ◽  
Calculation Results ◽  
Gradient Based ◽  
Overall Performance ◽  
The Impact ◽  
Research Studies ◽  
Color Channels

In computer vision, gradient-based tracking is usually performed from monochromatic inputs. However, a few research studies consider the influence of the chosen color-tograyscale conversion technique. This paper evaluates the impact of these conversion algorithms on tracking and homography calculation results, both being fundamental steps of augmented reality applications. Eighteen color-to-greyscale algorithms were investigated. These observations allowed the authors to conclude that the methods can cause significant discrepancies in the overall performance. As a related finding, experiments also showed that pure color channels (R, G, B) yielded more stability and precision when compared to other approaches.

A Comparative Study of Popular CNN Topologies Used for Imagenet Classification

2020 ◽  
pp. 89-103
Author(s):  
Hmidi Alaeddine ◽  
Malek Jihene
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Comparative Study ◽  
Exponential Growth ◽  
Multiple Paths ◽  
Deep Cnn ◽  
Different Characteristics

Deep Learning is a relatively modern area that is a very important key in various fields such as computer vision with a trend of rapid exponential growth so that data are increasing. Since the introduction of AlexNet, the evolution of image analysis, recognition, and classification have become increasingly rapid and capable of replacing conventional algorithms used in vision tasks. This study focuses on the evolution (depth, width, multiple paths) presented in deep CNN architectures that are trained on the ImageNET database. In addition, an analysis of different characteristics of existing topologies is detailed in order to extract the various strategies used to obtain better performance.

Applications of the Connection Machine to Computer Vision

1990 ◽  
Author(s):  
William J. Wolfe ◽  
Gita Alaghband ◽  
Donald Mathis ◽  
Alan Baxter
Keyword(s):  
Computer Vision ◽  
Connection Machine
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