Study on recognition of black insects on dark background by computer vision

Improved color channel comparison method (ICCCM) is an effective method to transformcolor images into gray-scale ones. Based on the ICCCM, black or white insects could be effectively extracted and recognized from the real color images with bright background. Howeverit is difficult to use the ICCCM to extract and recognize the black insects from the realcolorimage with dark background. In this paper, the ICCCM is modified to transformthe color images into the gray ones, extracting and recognizing the black insectson the dark background. The ICCCM is modified as follows: (1) A threshold of the gray image is an average brightness value ofred (R), green (G) and blue (B) in all the image pixels.(2) The bright pixels and the color pixels have the highest brightness value 255 in the gray image.(3) A pixel brightness value of the dark area in the gray image equals to a minimum of R, G and B in the pixel. (4) After deleted all the pixels with a brightness value of 255, a threshold of the binary image is determined by Otsus theory. The modified ICCCM more effectively extracts and recognizes the black insects from the realcolorimages with dark background compared with the ICCCM.

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Computer Vision for Visual Effects

10.1017/cbo9781139019682 ◽

2009 ◽

Cited By ~ 6

Author(s):

Richard J. Radke

Keyword(s):

Computer Vision ◽

Visual Effects

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Human Vision and Computer Vision

Contemporary Psychology ◽

10.1037/023481 ◽

1985 ◽

Vol 30 (1) ◽

pp. 47-47

Author(s):

Herman Bouma

Keyword(s):

Computer Vision ◽

Human Vision

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Computer vision for industrial applications

Software & Microsystems ◽

10.1049/sm.1983.0048 ◽

1983 ◽

Vol 2 (5) ◽

pp. 130

Author(s):

J.A. Losty ◽

P.R. Watkins

Keyword(s):

Computer Vision ◽

Industrial Applications

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The machine training in problems of satellite images’s processing

Metrologiya ◽

10.32446/0132-4713.2020-4-15-37 ◽

2020 ◽

pp. 15-37

Author(s):

L. P. Bass ◽

Yu. A. Plastinin ◽

I. Yu. Skryabysheva

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Computer Vision ◽

Satellite Images ◽

Vision Systems ◽

Earth Remote Sensing ◽

Practical Applications ◽

Convolution Neural Networks ◽

Computer Vision Systems ◽

Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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Modern Technology for Image processing and Computer vision -A Review

Journal of Advanced Sciences and Engineering Technologies ◽

10.32441/jaset.v1i2.178 ◽

2018 ◽

Vol 1 (2) ◽

pp. 17-23

Author(s):

Takialddin Al Smadi

Keyword(s):

Image Processing ◽

Computer Vision ◽

Augmented Reality ◽

Video Compression ◽

Video Processing ◽

Vision System ◽

Citrus Fruit ◽

Modern Technology ◽

Image And Video Processing ◽

Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality. © 2018 JASET, International Scholars and Researchers Association

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