Edge Map Information Research Articles

Vector Image Model to Object Boundary Detection in Noisy Images

A New model is designed for boundary detection and applied it to object segmentation problem in medical images. Our edge following technique incorporates a vector image model and the edge map information. The proposed technique was applied to detect the object boundaries in several types of noisy images where the ill-defined edges were encountered. The proposed techniques performances on object segmentation and computation time were evaluated by comparing with the popular methods, i.e., the ACM, GVF snake models. Several synthetic noisy images were created and tested. The method is successfully tested in different types of medical images including aortas in cardiovascular MR images, and heart in CT images.

KSS: Using Region and Edge Maps to Detect Image Boundaries

This fully automated process uses edge map information to eliminate false boundaries in an image's region map, and region map information to remove noise in its edge map. It then integrates the two maps into a single, final result. Experiments on a large dataset of natural color images show that this approach matches human perception better than individual methods in terms of both quantity and quality.