Title :
Segmenting images with support vector machines
Author :
Reyna, Roberto A. ; Hernandez, Neil ; Esteve, Daniel ; Cattoen, Michel
Author_Institution :
Lab. d´´Autom. et d´´Anal. des Syst., CNRS, Toulouse, France
Abstract :
The aim of this work is to propose an original image segmentation methodology to detect and localise objects or patterns in an image. This new technology has two parts: (a) the main module is a SVM neural network whose goal is the image segmentation in order to detect and localise objects having regular patterns (represented by a block of pixels), and then, (b) a simple morphological processing, to eliminate isolated misclassified pixels. The importance of this methodology is highlighted with the results obtained in the recognition of 2D symbolic codes. Another advantage of our algorithm is its regularity that may be exploited to propose a parallel hardware architecture
Keywords :
image recognition; image segmentation; learning automata; mathematical morphology; neural net architecture; object detection; parallel architectures; pattern recognition; 2D symbolic codes recognition; SVM neural network; image segmentation; isolated misclassified pixels elimination; morphological processing; neural network architecture; object detection; object localisation; parallel hardware architecture; pixels; support vector machines; Application software; Artificial neural networks; Face recognition; Image segmentation; Neural networks; Object detection; Pattern recognition; Pixel; Support vector machine classification; Support vector machines;
Conference_Titel :
Image Processing, 2000. Proceedings. 2000 International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-6297-7
DOI :
10.1109/ICIP.2000.901085
