Image Complexity Guided Network Compression for Biomedical Image Segmentation

Document Image Segmentation for Compression

An unsupervised strategy for biomedical image segmentation

Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In ...

Correcting User Guided Image Segmentation

We tackle the problem of segmenting an image into planes given user input. Using a supervised learning approach, we develop an algorithm that adjusts the user input towards true plane segmentations, under the restriction that images have vertical boundary lines. We begin by collecting images for the training set consisting of hallways, buildings, stairs, rooms, etc. Users are then asked to draw...

Segmentation based reversible image compression

mentation information in order to form a smoothly varying Reversible compression of images has been the topic of considerable research [l-31, as it finds applications in many fields in which the deviation of the reproduced image from the original image is intolerable, however small be the deviation. This paper is concerned with the problem of reducing spatial redundancies in gray scale images, ...

Document Image Segmentation and Compression

Cheng, Hui, Ph.D., Purdue University, August, 1999. Document Image Segmentation and Compression. Major Professor: Charles A. Bouman. In the first part of this research, we propose an image segmentation algorithm called the trainable sequential MAP (TSMAP) algorithm. The TSMAP algorithm is based on a multiscale Bayesian approach. It has a novel multiscale context model which can capture complex ...