Image segmentation is a primary step in many computer vision tasks. Although many segmentation methods based on either color or texture have been proposed in the last decades, there have been only few approaches combining both these features. This work presents a new image segmentation method using color texture features extracted from 3D co-occurrence matrices combined with spatial dependence,...

(a) [Golovinskiy and Funkhouser 2009] (b) Our co-segmentation method Figure 1: Comparison of an approach based on alignment (a), to our approach based on descriptor-space clustering (b). By contrasting these co-segmentation results, we observe that the results in (a) are less meaningful for sets of shapes that have greater part variability.

We first describe how to “fuzzify” the estimated binary columns to create a [0,1]-valued column. Werefer to this [0,1] -valued column as the soft segmentation column of the noisy spectrogram column.Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply asthe soft segmentation. The band-dependent posterior probability that the hard segmentation columnv...

To overcome the problems in Thai word segmentation, a number of word segmentation has been proposed during the long period of time until today. We propose a novel Thai word segmentation approach so called Co-occurrence-Based Error Correction (CBEC). CBEC generates all possible segmentation candidates using the classical maximal matching algorithm and then selects the most accurate segmentation ...

A number of word segmentation algorithms have been offered in the past; however, there is still room for improvement. Co-occurrence-Based Error Correction (CBEC), the proposed approach in this chapter, is a novel Thai word segmentation approach that was designed to provide accurate segmentation results based on context and purpose. CBEC quickly segments the input string using any available algo...

Article history: Available online 18 February 2016

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. This paper describes a novel technique of feature extraction for characterization and segmentation of texture at multiple scales based on block by block compariso...