An Automatic Changeable Edge Detection Model for Digital Images

摘要

Edge detection and feature extraction play an important role in digital image processing field. It reduces the amount of data and filters out useless information while preserving the important structural properties in an image. It was observed that using the same edge detection operator for different images make some images suffer from the details (high) and missing (low) edges. This limitation may affect the features for image understanding. Hence, the aim is enhancement of the edge pixels which suffer from the details and missing edge’s pixel by adjustment edge pixel in an automatic way for different images. This paper simulates the mechanism of how our body normally controls high and low blood pressure level to regulate the features of high and low edge images. The efficiency of proposed model is demonstrated experimentally on the hand posture dataset. The recognition accuracy obtained is 98.66%. The model provides better performance than conventional methods.