Computer Vision for the Ballet Industry: A Comparative Study of Methods for Pose Recognition

2020 ◽  
pp. 118-129
Author(s):  
Margaux Fourie ◽  
Dustin van der Haar
Keyword(s):  
Computer Vision ◽  
Comparative Study ◽  
Pose Recognition

Two-Dimensional Canonical Correlation Analysis of the Logically Concatenated Cross Binary Pattern for Cross Pose Face Recognition

2018 ◽  
Vol 8 (3) ◽  
pp. 66-79
Author(s):  
Kumud Arora ◽  
Poonam Garg
Keyword(s):  
Computer Vision ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Two Dimensional ◽  
Computation Complexity ◽  
Pose Recognition ◽  
Local Texture ◽  
Face Images ◽  
Informative Content

Face pose recognition is one of the challenging areas in computer vision. Cross-pose change causes the change in the information of face appearance. The maximization of intrasubject correlation helps to widen the intersubject differences which helps further in achieving pose invariance. In this paper, for cross pose recognition, the authors propose to maximize the cross pose correlation by using the logically concatenated cross binary pattern (LC-CBP) descriptor and two dimensional canonical correlation analysis (2DCCA). The LC-CBP descriptor extracts the local texture details of face images with low computation complexity and the 2DCCA explicitly searches for the maximization of the correlated features to retain most informative content. Joint feature consideration via 2DCCA helps in setting up a better correspondence between a discrete set of nonfrontal pose and the frontal pose of the same subject. Experimental results demonstrate the two dimensional canonical correlation LC-CBP descriptor along with intensity values improve the correlation.

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.

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