Study on Recognition of Black Insects on Dark Background by Computer Vision

2013 ◽  
Vol 756-759 ◽  
pp. 4685-4689
Author(s):  
Sa Liu ◽  
Yan Yang ◽  
Xiao Dong Zhu ◽  
Huai Wei Wang ◽  
Shi Bin Lian
Keyword(s):  
Computer Vision ◽  
Comparison Method ◽  
Color Images ◽  
Binary Image ◽  
Color Channel ◽  
Gray Image ◽  
Dark Background ◽  
Average Brightness ◽  
Image Pixels ◽  
Pixel Brightness

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.

New Set of Invariant Quaternion Krawtchouk Moments for Color Image Representation and Recognition

2021 ◽  
pp. 2250037
Author(s):  
Gaber Hassan ◽  
Khalid M. Hosny ◽  
R. M. Farouk ◽  
Ahmed M. Alzohairy
Keyword(s):  
Quaternion Algebra ◽  
Color Image ◽  
Image Representation ◽  
Color Images ◽  
Color Channel ◽  
Krawtchouk Polynomials ◽  
Krawtchouk Moments ◽  
Similarity Transformations ◽  
The Stability ◽  
Discrete Moments

One of the most often used techniques to represent color images is quaternion algebra. This study introduces the quaternion Krawtchouk moments, QKrMs, as a new set of moments to represent color images. Krawtchouk moments (KrMs) represent one type of discrete moments. QKrMs use traditional Krawtchouk moments of each color channel to describe color images. This new set of moments is defined by using orthogonal polynomials called the Krawtchouk polynomials. The stability against the translation, rotation, and scaling transformations for QKrMs is discussed. The performance of the proposed QKrMs is evaluated against other discrete quaternion moments for image reconstruction capability, toughness against various types of noise, invariance to similarity transformations, color face image recognition, and CPU elapsed times.

Real-Time Enhancement Method of Low Gray Image Under Dark Background

2011 ◽  
Vol 26 (3) ◽  
pp. 374-378
Author(s):  
黄梅 HUANG Mei ◽  
吴志勇 WU Zhi-yong ◽  
梁敏华 LIANG Min-hua ◽  
于建军 YU Jian-jun ◽  
管目强 GUAN Mu-qiang
Keyword(s):  
Real Time ◽  
Gray Image ◽  
Dark Background ◽  
Enhancement Method

Research on the Paper Splicing Recovery

2014 ◽  
Vol 602-605 ◽  
pp. 3443-3446
Author(s):  
Yu Xuan Jiang
Keyword(s):  
Neural Network ◽  
Euclidean Distance ◽  
Comparison Method ◽  
Binary Image ◽  
Network Data ◽  
Grey Level ◽  
Binary Matrix ◽  
Som Neural Network ◽  
Restoration Problem

This paper focuses on scraps of paper splicing recovery issues. To deal with complex scraps restoration problem, first the image should be changed as pixel grey level binary image, generating binary matrix, and then classify the scraps using SOM neural network data and feature comparison method. Combining with the Euclidean distance discriminant method, we match the scraps of paper with same edges and obtain a complete stitching schematic finally.

scholarly journals N-D Linear Time Exact Signed Euclidean Distance Transform

2003 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Marcelo Siqueira ◽  
James Gee
Keyword(s):  
Computer Vision ◽  
Euclidean Distance ◽  
Linear Time ◽  
Small Time ◽  
Direct Application ◽  
Distance Transform ◽  
Fast Computation ◽  
Distance Transforms ◽  
Euclidean Distance Transform ◽  
Image Pixels

Fast computation of distance transforms find direct application in various computer vision problems. Currently there exists two image filters in the ITK library which can be used to generate distance maps. Unfortunately, these filters produce only approximations to the Euclidean Distance Transform (EDT). We introduce into the ITK library a third EDT filter which was developed by Maurer {} . In contrast to other algorithms, this algorithm produces the exact signed squared EDT using integer arithmetic. The complexity, which is formally verified, is O(n) O(n) with a small time constant where n n is the number of image pixels.

scholarly journals A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Jin ◽  
Chenglin Wang ◽  
Kaikang Chen ◽  
Jiangtao Ji ◽  
Suchwen Liu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Three Dimensional ◽  
Comparison Method ◽  
Block Matching ◽  
Identification Accuracy ◽  
Position Information ◽  
Block Matching Algorithm ◽  
Fundamental Process ◽  
Vegetable Seedlings ◽  
Vegetable Cultivation

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

Sign in / Sign up

Export Citation Format

Share Document