A Study on Image Segmentation Method for Image Processing

Abstract

Image processing techniques are essential part of the current computer technologies and that it plays vital role in various applications like medical field, object detection, video surveillance system, computer vision etc. The important process of Image processing is Image Segmentation. Image Segmentation is the process of splitting the images into various tiny parts called segments. Image processing makes to simplify the image representation in order to analyze the images. So many algorithms are developed for segmenting images, based on the certain feature of the pixel. In this paper different algorithms of segmentation can be reviewed, analyzed and finally list out the comparison for all the algorithms. This comparison study is useful for increasing accuracy and performance of segmentation methods in various image processing domains.

Invariant-Based World Models for Robust Robotic Systems Demonstrated on an Autonomous Football Table

“…The value of their colour (in HSV colour space) is stored in the world model. This colour value is used to group neighboring pixels into blobs [17], which are regarded as potential puppets. By comparing the blobs to the known layout of the puppets, false positives are omitted.…”

“…Combining ( 14) and (15) gives expressions for dp i/dW and si /d W , respectively in ( 16) and (17).…”

“…Moreover, the ball will only be inside the playable region, potential blobs outside this region are omitted. K-means clustering of the image reduces the colour palette of the image [17], making edges more clearly defined. Canny edge detection is performed on the resulting image [4].…”