Texture Segmentation from Non-Textural Background Using Enhanced MTC
Mudassir Rafi and Susanta
Mukhopadhyay
Department of Computer
Science and Engineering, Indian Institute of Technology, India
Abstract: In image
processing, segmentation of textural regions from non-textural background has
not been given a significant attention, however, considered to be an important
problem in texture analysis and segmentation task. In this paper, we have
proposed a new method, which fits under the framework of mathematical
morphology. The entire procedure is based on recently developed textural
descriptor termed as Morphological Texture Contrast (MTC). In this work authors
have employed the bright and dark top-hat transformations to handle the bright
and dark features separately. Both bright and dark features so extracted are
subjected to MTC operator for identification of the texture components which in
turn are used to enhance the textured parts of the original input image.
Subsequently, our method is employed to segment the bright and dark textured
regions separately from the two enhanced versions of the input image. Finally,
the partial segmentation results so obtained are combined to constitute the
final segmentation result. The method has been formulated, implemented and
tested on benchmark textured images. The experimental results along with the
performance measures have established the efficacy of the proposed method.
Keywords: Texture segmentation, top-hat transformation, bottom-hat
transformation, MTC.
Received October 25, 2015; accepted January 12, 2017