Research and Design on Vision System of the Mobile Robot

2013 ◽  
Vol 341-342 ◽  
pp. 797-800
Author(s):  
Xin Ma ◽  
Rong Guang Sun ◽  
Yong Feng Dong
Keyword(s):  
Object Recognition ◽  
Mobile Robot ◽  
Vision System ◽  
Color Space ◽  
Color Images ◽  
Light Conditions ◽  
Hsi Color Space ◽  
Research And Design

The paper presents the design of vision system of the mobile robot, and shows methods of object recognition based on color images in the system of mobile robot. To adapt to the different light conditions, HSI color space is used. The system is able to meet the demand on rapidity and veracity of system of mobile robot. Experiment results show that the proposed technique is liable to accomplish object recognition in presence of changing illumination environment conditions.

Analysis of Characters of HSI Color Space and Its Application in Moving Robot

2010 ◽  
Vol 26-28 ◽  
pp. 48-54
Author(s):  
Jin Ling Wei ◽  
Jun Meng ◽  
Wei Song
Keyword(s):  
Color Image ◽  
Color Space ◽  
Color Images ◽  
Value Range ◽  
Hsi Color Space ◽  
Rgb Color Space

According to the analysis of every feature element’s grey images in RGB color space and HSI color space, each of the elements represents different information of the color image. From the analysis of the Histogram of color images, the value range of hue H basically keeps stable, which is proved by experiments to be the most stable and representative one. Finally we illustrated by application instances that the method of recognition and tracking of the objective moving robot based on hue character H is applicable.

scholarly journals A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 113 ◽  
Author(s):  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Davood Kalantari ◽  
José Luis Hernández-Hernández ◽  
Juan Ignacio Arribas
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Vision System ◽  
Color Space ◽  
Majority Voting ◽  
Hsi Color Space ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

Environmentally Adaptive Segmentation Algorithm for Outdoor Crops Using Gaussian Mixture Model

2014 ◽  
Vol 1049-1050 ◽  
pp. 1747-1750
Author(s):  
Zheng Hong Yu ◽  
Hong Mei Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Space ◽  
Gaussian Mixture ◽  
Decision Function ◽  
Light Conditions ◽  
Segmentation Method ◽  
Learning Stage ◽  
Hsi Color Space ◽  
Two Stages

Crop segmentation from outdoor images is still an open problem. In this paper, we proposed a novel crop segmentation method using Gaussian Mixture Model (GMM), which is robust and not sensitive to the challenging outdoor light conditions and complex environmental elements. The method mainly consists of two stages, supervised learning stage and segmentation stage. The GMM is utilized in the former stage to establish crop color model in the HSI color space and a decision function is provided in the latter stage to realize the final crop segmentation. Comparing experimental results show that our method outperforms the other commonly used methods in yielding the highest performance of 94.91% with the lowest standard deviation of 3.14%.

scholarly journals Estimation of percentage of impurities in coffee using a computer vision system

2022 ◽  
Vol 26 (2) ◽  
pp. 142-148
Author(s):  
Anderson G. Costa ◽  
Eudócio R. O. da Silva ◽  
Murilo M. de Barros ◽  
Jonatthan A. Fagundes
Keyword(s):  
Linear Models ◽  
Digital Images ◽  
Vision System ◽  
Color Space ◽  
Principal Component Regression ◽  
Principal Component ◽  
Coffee Beans ◽  
Hsi Color Space ◽  
Pcr Method ◽  
Data Group

ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.

scholarly journals A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

2021 ◽  
Vol 13 (6) ◽  
pp. 1211
Author(s):  
Pan Fan ◽  
Guodong Lang ◽  
Bin Yan ◽  
Xiaoyan Lei ◽  
Pengju Guo ◽  
...  
Keyword(s):  
Vision System ◽  
False Positive Rate ◽  
Clustering Algorithms ◽  
False Negative ◽  
Color Space ◽  
False Negative Rate ◽  
Feature Selection Method ◽  
Image Features ◽  
Accurate Identification ◽  
Rgb Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

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