A Real-Time and Effective Object Recognition and Localization Method

2014 ◽  
Vol 615 ◽  
pp. 107-112 ◽  
Author(s):  
Xiao Ling Ding ◽  
Qiang Zhao ◽  
Yi Bin Li ◽  
Xin Ma
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Color Image ◽  
Color Image Segmentation ◽  
Geometry Model ◽  
Perspective Geometry ◽  
Hsv Color Model ◽  
Camera Perspective ◽  
Color Object Recognition ◽  
Image Segmentation Algorithm

In this paper, we realize object recognition and localization in a real time based on appearance features of object. For object recognition, we proposed to use global feauture (color) of images, and with an improved color image segmentation algorithm to realize threshold segmentation based on pixels in the image’s HSV color model by using the tool OpenCV, so we can realize the special color object recognition. Further the object can be localized with the ground constrained method by using the camera perspective geometry model. In the lab conditions, we realized single color object recognition and localization by transplanting the algorithm into Amigobots mobile robot and proved this method is simple, effective and real-time.

Color Image Segmentation Based on Self-Organizing Maps

2011 ◽  
Vol 214 ◽  
pp. 693-698
Author(s):  
Rui Geng
Keyword(s):  
Image Segmentation ◽  
Optimization Problems ◽  
Color Image ◽  
Optimal Solution ◽  
Implementation Process ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Image Segmentation Algorithm

The colony intellectual behavior performed by many organisms in nature can solve various kinds of problems on scientific and technological research. Bees are a socialized insect colony, which perform different types of activities according to their different divisions of labor, and achieve information sharing and exchanges among the bee colony to find the optimal solution for problems. According to this characteristic, researchers have proposed the algorithm of bee colony for solving combinatorial optimization problems. In this paper, it will describe the implementation process of such an image segmentation algorithm, and the result shows that this method is a potential image segmentation algorithm.

scholarly journals Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Edgar Chavolla ◽  
Arturo Valdivia ◽  
Primitivo Diaz ◽  
Daniel Zaldivar ◽  
Erik Cuevas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Community ◽  
Color Image ◽  
The Other ◽  
Color Image Segmentation ◽  
Color Segmentation ◽  
Color Model ◽  
Wide Range ◽  
Hsv Color Model ◽  
Relevant Topic

Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.

Fuzzy Clustering Color Image Segmentation Algorithm Based on CPSO

2012 ◽  
Vol 461 ◽  
pp. 526-531
Author(s):  
Xiao Hong Zhang ◽  
Hong Mei Ning
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Fcm Clustering ◽  
Clustering Center ◽  
Membership Matrix ◽  
Image Segmentation Algorithm

Fuzzy C-mean algorithm (FCM) has been well used in the field of color image segmentation. But it is sensitive to initial clustering center and membership matrix, and likely converges into the local minimum, which causes the quality of image segmentation lower. By use of the properties-ergodicity, randomicity of chaos, a new image segmentation algorithm is proposed, which combines the chaos particle swarm optimization (CPSO) and FCM clustering. Some experimental results are shown that this method not only has the ability to prevent the particles to convergence to local optimum, but also has faster convergence and higher accuracy for segmentation. Using the feature distance instead of Euclidian distance, robustness of this method is enhanced.

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