Wavelet packets and co-occurrence matrices for texture-based image segmentation

In this paper, a texture-based segmentation approach using wavelet packets, co-occurrence matrices and normalised modified histogram thresholding is discussed and developed. Background and objects in remotely sensed Light Detection And Ranging (LIDAR) data are successfully partitioned into rivers, fields and residential areas using the developed algorithms. The issue of wavelet packet decomposition level is addressed by analysing the sub-images’ energy and entropy. The standard deviation of the modified histogram, which is derived from the main diagonal of the sub-image’s cooccurrence matrix, is used as a measure to evaluate the sub-images in terms of thresholdability. Finally, the segmentation results are presented.

• Type: Conference
• Year: 2005
• Where: AVSS
• Authors: Marc Bartels, Hong Wei, David C. Mason
• Keywords: AVSS 2005, LIght Detection And Ranging, Signal Processing, Texture-based Segmentation Approach, Wavelet Packet