Texture Segmentation Using Kernel-based Techniques

In this work, we propose a novel method for determining oriented energy features of an image. Oriented energy features, useful for many machine vision applications like contour detection, texture segmentation and motion analysis, are determined from the filters whose outputs are enhanced at the edges of the image at a given orientation. We use the eigenvectors and eigenvalues of graph Laplacian for determining the oriented energy features of an image. Our method is based on spectral graph theoretical approach in which a graph is assigned complex-valued edge weights whose phases encode orientation information. These edge weights give rise to a complex-valued Hermitian Laplacian whose spectrum enables us to extract oriented energy features of the image. We perform a set of numerical experiments to determine the efficiency and characteristics of the proposed method. In addition, we apply our feature extraction method to texture segmentation problem. We do this in comparison with other known methods, and show that our method performs better for various test textures.

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Integrating region and edge information for texture segmentation using a modified constraint satisfaction neural network

Image and Vision Computing ◽

10.1016/j.imavis.2007.12.004 ◽

2008 ◽

Vol 26 (8) ◽

pp. 1106-1117 ◽

Cited By ~ 7

Author(s):

Lalit Gupta ◽

Utthara Gosa Mangai ◽

Sukhendu Das

Keyword(s):

Neural Network ◽

Constraint Satisfaction ◽

Texture Segmentation ◽

Edge Information

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Texture segmentation and analysis for tissue characterization

10.1117/12.561112 ◽

2004 ◽

Author(s):

Rafael Redondo ◽

Sylvain Fischer ◽

Gabriel Cristobal ◽

Manuel Forero ◽

Andres Santos ◽

...

Keyword(s):

Tissue Characterization ◽

Texture Segmentation

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Deficits in Complex Visual Perception Following Unilateral Temporal Lobectomy

Journal of Cognitive Neuroscience ◽

10.1162/089892998562816 ◽

1998 ◽

Vol 10 (3) ◽

pp. 395-407 ◽

Cited By ~ 11

Author(s):

Krystel R. Huxlin ◽

William H. Merigan

Keyword(s):

Visual Field ◽

Visual Perception ◽

Temporal Cortex ◽

Stimulus Presentation ◽

Texture Segmentation ◽

Temporal Lobectomy ◽

Long Term Memory ◽

Additional Control ◽

Short And Long Term ◽

Visual Discriminations

Although human temporal cortex is known to be important for short- and long-term memory, its role in visual perception is not well understood. In this study, we compared the performance of three patients with unilateral temporal lobectomies to that of normal controls on both fisimplefl and ficomplexfl visual discriminations that did not involve explicit memory components. Two types of complex tasks were tested that involved discriminations secondary to texture segmentation. These were contrasted with simple discriminations using luminance-defined stimuli. Patients showed impaired thresholds only on tasks involving texture segmentation, performing as well as controls when the targets were defined by luminance rather than texture. The minimum stimulus presentation times for threshold performance were also measured for all tasks and found to be elevated in temporal lobectomy patients relative to controls. Although the magnitude of the deficits observed was substantial, loss was equivalent in ipsi- and contra-lesional regions of the visual field. Additional control experiments showed that the patients' perceptual deficits were not due, even in part, to disturbances of basic visual capacities such as acuity and contrast sensitivity. Our results indicate that temporal lobe damage disrupts complex, but not simple, visual discriminations throughout the visual field.

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