Lecture 6 Color and Texture Slides by: David A. Forsyth Clark F. Olson Linda G. Shapiro

Color Used heavily in human vision Color is a pixel property, making some recognition problems easy Visible spectrum for humans is 400nm (blue) to 700 nm (red) Machines can “see” much more; ex. X-rays, infrared, radio waves

Causes of color The sensation of color is caused by the brain. Some ways to get this sensation include: Pressure on the eyelids Dreaming, hallucinations, etc. Main way to get it is the response of the visual system to the presence/absence of light at various wavelengths. Issues that affect perception of color: Light sources with different spectrums (compare the sun and a fluorescent light bulb) Differential reflection (e.g. some pigments) and absorption Differential refraction - (e.g. Newton’s prism) Different distance and angle of reflection Sensitivity of sensor

Some physics White light is composed of all visible frequencies (400-700) Ultraviolet and X-rays are of much smaller wavelength Infrared and radio waves are of much longer wavelength

Albedos Color varies along a linear scale (wavelength). Spectral albedos for different leaves, with color names attached. Color varies along a linear scale (wavelength). Different colors typically have different spectral albedo. Measurements by E.Koivisto. Violet Indigo Blue Green Yellow Orange Red

The appearance of colors Color appearance is strongly affected by (at least): other nearby colors, adaptation to previous views “state of mind” Image from: http://web.mit.edu/persci/people/adelson/checkershadow_illusion.html

The appearance of colors Color appearance is strongly affected by (at least): other nearby colors, adaptation to previous views “state of mind” Image from: http://web.mit.edu/persci/people/adelson/checkershadow_illusion.html

Color spaces RGB: primaries are monochromatic (formally 645.2nm, 526.3nm, 444.4nm) CIE XYZ: Primaries are imaginary (negative spectral radiance), but have other convenient properties Also: CMY: subtractive color space used for printing HSV: perceptually salient space for several applications YIQ: used for TV – good for compression A choice of three primaries yields a linear color space - the coordinates of a color are given by the weights of the primaries used to match it. Choice of primaries is equivalent to choice of color space.

Comparing color spaces

Color cube R, G, B values normalized to (0, 1) interval humans perceive gray for triples on the diagonal “Pure colors” on corners

Color receptors and color deficiency Trichromacy is justified - in most people, there are three types of color receptor, called cones, which vary in their sensitivity to light at different wavelengths (shown by molecular biologists). Some people have fewer than three types of receptor; most common deficiency is red-green color blindness in men.

Texture Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image. Structural approach: Texture is a set of primitive texels in some regular or repeated relationship.

Texture Finding texels is difficult in most images:

Statistical texture Most common approach in computer vision is to compute statistics in the image to represent texture. - Computationally efficient - Can be used for classification and segmentation Simplistic approach: apply edge detection - Number of edge pixels is one measure of texture - Orientation is another (average or histogram)

Co-occurrence matrix A co-occurrence matrix is a 2D array N (or C) in which: Both the rows and columns represent a set of possible image values. Nd(i,j) indicates how many times value i co-occurs with value j in a particular spatial relationship d. The spatial relationship is specified by a vector d = (dr,dc). This is essentially a 2D histogram storing a particular spatial relationship between intensity values.

Co-occurrence matrix 1 0 1 2 1 1 0 0 0 0 2 2 i j 1 2 6 0 4 2 2 0 0 0 4 1 1 0 0 0 0 2 2 i j 1 2 6 0 4 2 2 0 0 0 4 d = (0,1) C d co-occurrence matrix grayscale image

Co-occurrence features Numeric features computed from the co-occurrence matrix can be used to represent and compare textures.

Co-occurrence matrix How do you choose d? Are the textures small, medium, large? One suggestion (Zucker and Terzopoulos): use a statistical test to select value(s) that have the most “structure”.

Texture representation Another method to represent image texture is by convolving the image with a set of filters. Each pixel is represented by a vector of filter responses, the “texture signature” Strong response when image is similar to filter Weak response when not similar The filters that are typically used look like: Spots Bars

Filters are templates Applying a filter at some point can be seen as taking a dot-product between the image and the filter. Both are viewed as 1D vectors rather than 2D images Filtering the image is a set of dot products. Insight: filters look like the effects they are intended to find filters find effects they look like why?

Filters are templates Positive responses The filter is the little block in the top left hand corner. Notice this is a fair spot-detector. Positive responses

Filters are templates Positive responses The filter is the little block in the top left hand corner. Notice this is a fair bar-detector. Positive responses

Scaled representations Big bars and little bars (elongated features like limbs or stripes) are both interesting features to detect in an image. - Also could be dots or other shapes It is inefficient to detect big bars with big filters. - And there is superfluous detail in the filter kernel Alternative: Apply filters of fixed size to images of different sizes Typically, a collection of images whose edge length changes by a factor of 2 (or the square root of 2) This is a pyramid by visual analogy (sometimes called a Gaussian pyramid)

Scaled representations A bar in the biggest image is a hair on the zebra’s nose; in middle images, a stripe; in the smallest, the animal’s nose

Representing textures Real textures are made up of patterns of irregular subelements. What are the subelements? not well defined, in general usually reduced to most basic shapes: spots and bars at various sizes and orientations How do we find them? by applying filters After applying bar and spot filters apply statistics locally: mean standard deviation histograms

Representing textures

Representing textures Filters (not to scale): Original image: Filter responses: Spots and bars at a fine scale for the butterfly; images show squared response for corresponding filter