Texture Segmentation with Optimal Linear Prediction Error Filters
نویسنده
چکیده
An approach to feature extraction for texture images using optimal linear (autoregressive) predictors is presented. The features used for classiication are calculated from the prediction error using a local energy function. Experimental results are given to show the applicability of the method.
منابع مشابه
The Design of Multiple Gabor Filters for Segmenting Multiple Textures
Gabor filters have been successfully employed in texture segmentation problems, yet a general multi-filter multi-texture Gabor filter design procedure has not been offered. To this end, we first present a multichannel paradigm that provides a mathematical framework for the design of the filters. The paradigm establishes relationships between the predicted texture-segmentation error, the power s...
متن کاملAn efficient technique of texture representation in segmentation-based image coding schemes
In segmentation-based image coding techniques the image to be compressed is first segmented. Then, the information is coded describing the shape and the interior of the regions. A new method to encode the texture obtained in segmentation-based coding schemes is presented in this paper. The approach combines 2-D linear prediction and stochastic vector quantization. To encode a texture, a linear ...
متن کاملUnsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملNew Optimal Observer Design Based on State Prediction for a Class of Non-linear Systems Through Approximation
This paper deals with the optimal state observer of non-linear systems based on a new strategy. Despite the development of state prediction in linear systems, state prediction for non-linear systems is still challenging. In this paper, to obtain a future estimation of the system states, initially Taylor series expansion of states in their receding horizons was achieved to any specified order an...
متن کاملUnsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007