An Algorithm of Leaf Image Segmentation Based on Color Features

2011 ◽  
Vol 474-476 ◽  
pp. 846-851 ◽  
Author(s):  
Jie Yun Bai ◽  
Hong E Ren
Keyword(s):  
Image Segmentation ◽  
Digital Image ◽  
Color Information ◽  
Color Features ◽  
Green Component ◽  
Good Consistency ◽  
High Degree ◽  
Similarity Distance ◽  
Leaf Image ◽  
Rgb Model

The paper proposes a digital image extraction and segmentation algorithm based on color features. The traditional transformation from RGB model to HSI model is improved, meanwhile the leaf color information is extracted by similarity distance between pixels. The green component of leaf image in the RGB model is strengthened, and then the digital image is transformed to the HSI model by the improved method. Finally the image is divided by similarity distance of pixels’ H weight which determines whether the pixel belongs to the blade. The results of simulation experiment shows that this algorithm can achieve a good image segmentation effect, and it has a high degree of accuracy as well as a clearly distinguish degree and many other advantages such as good consistency with human visual system. It completely meets the effectiveness and clarity requirements of image segmentation.

Face Image Segmentation Using Color Information and Saliency Map

2011 ◽  
Vol 55-57 ◽  
pp. 77-81
Author(s):  
Hui Ming Huang ◽  
He Sheng Liu ◽  
Guo Ping Liu
Keyword(s):  
Image Segmentation ◽  
Human Skin ◽  
Skin Color ◽  
Saliency Map ◽  
Face Image ◽  
Color Information ◽  
Attention Model ◽  
Face Area ◽  
Face Region ◽  
The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

scholarly journals Techniques for Image Segmentation: A Critical Review

2021 ◽  
Vol 9 (3) ◽  
pp. 1-4
Author(s):  
Harshita Mishra ◽  
Anuradha Misra
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Image Segmentation ◽  
Digital Image ◽  
Electronic Devices ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Mathematical Algorithms ◽  
Picture Element

In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.

Theoretical Concepts and Technical Aspects on Image Segmentation

2018 ◽  
pp. 2333-2348
Author(s):  
Anju Pankaj ◽  
Sonal Ayyappan
Keyword(s):  
Image Segmentation ◽  
Digital Image ◽  
High Variability ◽  
Technical Aspects ◽  
Theoretical Concepts ◽  
Detailed Assessment

Image segmentation is the process of partitioning a digital image into multiple segments (super pixels). Segmentation is typically used to locate objects and boundaries in images. The result of segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property. Adjacent regions are significantly different with respect to the same characteristics. A predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image is defined. An important characteristic of the method is its ability to preserve detail in low-variability image regions and ignoring detail in high variability regions. This chapter discuss basic aspects of segmentation and an application and presents a detailed assessment on different methods in image segmentation and discusses a case study on it.

Sign in / Sign up

Export Citation Format

Share Document