<p>Top row: classification of textures D21 and D28. Middle row: classification of textures D21 and D77. Bottom row: classification of textures D28 and D77. Experiments with patch sizes of 3 × 3 and 5 × 5 pixels are shown in left and right columns, respectively. Note that the <i>y</i>-axes of the plots are scaled differently to allow appreciation of the effect of using multiple sparse representations.</p
<p>In Experiment 1, we tested six models in one session (RND, ICA, L2, LP, IPS, GPS) and the four mi...
The illustration of texture analysis considering neighboring pixel pairs of four directions: 0°, 45°...
AbstractInstantaneous texture discrimination performance was examined for different texture stimuli ...
<p>Top to bottom: results of SRC, mSRC (using 15 representations), <i>K</i>-NN, and SVM. Left to rig...
In Section 8.6 we used a set of 100 VisTex texture mosaics, courtesy of Computer Vi-sion Group at th...
AbstractRecently, researchers have started using texture for data visualization. The rationale behin...
Textures often show multiscale properties and hence multiscale techniques are considered useful for ...
a. A texture (right) synthesized from the input on the left using the Portilla & Simoncelli [29] sum...
The left most column shows the image presented. The second column in each row names the object from ...
This paper addresses the problem of texture discrimination in range images. The range data is transf...
Image statistics are often classified as first-order (e.g., luminance), second-order (e.g., contrast...
Scale change exists very commonly in real-world textural images which remains one of the biggest cha...
Abstract: Texture classification is one of the most studied and challenging problems in computer vis...
In a 4 × 4 image, three gray-levels are represented by numerical values from 1 to 3. The GLCM is con...
Dans un contexte de classification de texture par caractérisation d'invariant, cet article propose d...
<p>In Experiment 1, we tested six models in one session (RND, ICA, L2, LP, IPS, GPS) and the four mi...
The illustration of texture analysis considering neighboring pixel pairs of four directions: 0°, 45°...
AbstractInstantaneous texture discrimination performance was examined for different texture stimuli ...
<p>Top to bottom: results of SRC, mSRC (using 15 representations), <i>K</i>-NN, and SVM. Left to rig...
In Section 8.6 we used a set of 100 VisTex texture mosaics, courtesy of Computer Vi-sion Group at th...
AbstractRecently, researchers have started using texture for data visualization. The rationale behin...
Textures often show multiscale properties and hence multiscale techniques are considered useful for ...
a. A texture (right) synthesized from the input on the left using the Portilla & Simoncelli [29] sum...
The left most column shows the image presented. The second column in each row names the object from ...
This paper addresses the problem of texture discrimination in range images. The range data is transf...
Image statistics are often classified as first-order (e.g., luminance), second-order (e.g., contrast...
Scale change exists very commonly in real-world textural images which remains one of the biggest cha...
Abstract: Texture classification is one of the most studied and challenging problems in computer vis...
In a 4 × 4 image, three gray-levels are represented by numerical values from 1 to 3. The GLCM is con...
Dans un contexte de classification de texture par caractérisation d'invariant, cet article propose d...
<p>In Experiment 1, we tested six models in one session (RND, ICA, L2, LP, IPS, GPS) and the four mi...
The illustration of texture analysis considering neighboring pixel pairs of four directions: 0°, 45°...
AbstractInstantaneous texture discrimination performance was examined for different texture stimuli ...