학술논문

Using entropy information measures for edge detection in digital images
Document Type
Conference
Source
2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015 38th International Convention on. :352-355 May, 2015
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Photonics and Electrooptics
Power, Energy and Industry Applications
Entropy
Image edge detection
Information entropy
Joints
Kernel
Shape
Digital images
Language
Abstract
Shannon information entropy measures were used as filters of different kernel sizes to detect edges in digital images. The concept is based on communication theory with splitting of edge detection kernel into source and destination parts. The arbitrary shape of the kernel parts and the fact that information filter output is a real number with reduced problem of edge's continuity represents the major advantage of this approach. The results are compared with traditional edge detection algorithms like Sobel to illustrate performance and sensitivity of the information entropy filters. Besides the well known test image Lena, the real life examples are taken from medical X-Ray imaging of knee joints in order to illustrate the algorithm performance on real data.