Outline Texture modeling - continued Filtering-based approaches

Visual Perception Modeling Texture Modeling The structures of images The structures in images are due to the inter-pixel relationships The key issue is how to characterize the relationships 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering Convolving an image with a linear filter yields a representation of the image on a different basis The advantage of the transformation is that the process makes the local structure of the image “clear” A filter can be viewed as a template There is a strong response when the local image pattern looks similar to the filter kernel and a weak response when it does not 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering – cont. 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering – cont. 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering – cont. Gabor filters Gabor filters have been widely in texture modeling Mathematically, Gabor filters are optimal in the sense of local joint spatial/frequency representation 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering – cont. 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering – cont. Problems with filtering-based approaches The filter response itself does not give rise to a representation or a model for textures Even for homogenous textures, the filter responses are not homogenous 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering – cont. 9/18/2018 Visual Perception Modeling

Representing Textures through Filtering – cont. Non-linear smoothing In order to derive a more homogenous and meaningful feature for textures, the filter responses are then passed through a non-linear stage The hope is that smoothed filter response will be relative homogenous within a texture region This can be used for texture classification, texture boundary detection, and texture discrimination 9/18/2018 Visual Perception Modeling

Texture Classification Based on Filtering 9/18/2018 Visual Perception Modeling

Texture Classification Based on Filtering – cont. 9/18/2018 Visual Perception Modeling

Texture Classification Based on Filtering – cont. 9/18/2018 Visual Perception Modeling

Texture Classification Based on Filtering – cont. 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models Histograms as texture features For homogenous textures, histograms should not change very much In other words, texture images with similar histograms of filter responses should look similar Heeger and Bergen proposed a texture synthesis algorithm based on this observation 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models – cont. 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models – cont. 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models – cont. 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models – cont. 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models – cont. 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models – cont. 9/18/2018 Visual Perception Modeling

Histograms of Filter Responses as Texture Models – cont. 9/18/2018 Visual Perception Modeling

Non-Parametric Sampling When we need to decide a pixel value, we calculate the conditional probability given the pixel values in the surrounding neighborhood This is done by finding the similar surrounding neighborhood in the given texture 9/18/2018 Visual Perception Modeling

Non-Parametric Sampling – cont. 9/18/2018 Visual Perception Modeling

Non-Parametric Sampling – cont. 9/18/2018 Visual Perception Modeling

Non-Parametric Sampling – cont. 9/18/2018 Visual Perception Modeling