A Review Paper on Interactive Image Segmentation
- May 13, 2018
- Posted by: RSIS
- Categories: Electrical and Electronics Engineering, Electronics & Communication Engineering
International Journal of Research and Scientific Innovation (IJRSI) | Volume V, Issue IV, April 2018 | ISSN 2321–2705
A Review Paper on Interactive Image Segmentation
Prof. Yuvaraj T, Nithin Krishna, Poojary Manish, Priya Naik, Varsha P
Department of Electronics and Communication Engineering, Alva’s Institute of Engineering and Technology, Mijar, Mangaluru, Karnataka, India
Abstract: – Due to advent of computer technology Image processing techniques have become increasingly important in a wide variety of application. Image segmentation is the process that partitions an image into region. One weakness in the existing interactive image segmentation algorithms is the lack of more intelligent ways to understand the intention of user inputs. Interactive Image segmentation aims to separate an object of interest from the rest of an image.
Most of the previous work requires users to trace the whole boundary of the object. When the object has the complicated boundary, or the object is in a highly textured region, users have to put great effort into interactively collecting the selection. It achieves three goals from following three steps. First, merge over segmented region according to themaximal similarity rule using a few markingstrokes as input. Second, detect possible erroneous low contrast object boundaries by analyzing image content. Third, automatically refine those boundary regions using both local and global information.
I. INTRODUCTION
Interactive Image Segmentation has many applications in image processing, computer vision, and computer graphics.Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. Our goal is to develop intuitive and intelligent image segmentation algorithms and tools that allow user to interactively guide the segmentation algorithm via a small amount of intuitive interactions until a satisfactory segmentation results that reflects both user intention and photometric features is achieved. The segmentation process is based on various features found in the image. This might be color information that is used to create histogram, or information about the pixels that indicate edges or boundaries or texture information.
