Colour and Texture

Extract 3-D information Using Vision Extract 3-D information for performing certain tasks such as manipulation, navigation, and recognition. There are three aspects of this: –Segmentation of the scene into distinct objects –Determining the position, orientation and shape of each object relative to the observer –Feedback to modify the motion of the robot

How Do We Recover 3-D Information? There are number of cues available in the visual stimulus –Motion –Binocular stereopsis –Texture –Shading –Contour Each of these cues relies on background assumptions about physical scenes in order to provide unambiguous interpretation.

Studies by psychologist and artists have demonstrated that the presence and distribution of colours induce sensations and convey meanings in the observer, according to specific rules - colour images can also be retrieved according to the meaning they convey or to sensations they provoke.

Colour Light is energy, specifically electromagnetic energy – eye (energy detector) The eye can distinguish between some types of electromagnetic energy. Those distinctions are seen as colours. The whiteness of an image area and the amount of light hitting the eye –The actual reflectance –The brightness of incident light –The incidence angle: the angle at which the light hits the object (one per light source, and there may be several) –The surface orientation of the area with respect to the viewer

"Colour is the visual effect that is caused by the spectral composition of the light emitted, transmitted, or reflected by objects.“

Texture What is texture? How to detect texture information? How to measure it? How to use it?

Shape and texture

Texture

Texture Together with colour, texture is a powerful discriminating feature, present almost everywhere in nature. Like colours, textures are connected with psychological effects. In particular, they emphasize orientations and spatial depth between overlapping object.

Texture There are three standard problems to do with texture: –Texture segmentation is the problem of breaking an image into components within which the texture is constant. Texture segmentation involves both representing a texture, and determining the basis on which segment boundaries are to be determined. How texture should be represented. –texture synthesis seeks to construct large regions of texture from small example images. –Shape from texture involves recovering surface orientation or surface shape from image texture.

Traditional Definition of Texture Texture refers to a spatially repeating pattern on a surface that can be sensed visually In the image, the apparent size, shape, spacing etc, of the texture elements (the texels) do indeed vary –Varying distances of the different texels from the camera –Varying foreshortening of the different texels. texture gradients - systematic change in the size and shape of the elements making up a texture

 recover shape from texture

Recover Shape From Texture After some mathematical analysis, one can compute expressions for the rate of change of various image texel features, such as area, foreshortening, and density. These texture gradients are functions of the surface shape as well as its slant and tilt with respect to the viewer. To recover shape from texture, one can use two- step process: –1) measure the texture gradients –2) estimate the surface shape, slant, and tilt that would give rise to the measured texture gradients.

Recent Technical Definition of texture:- Texture is a broad term used in pattern recognition to identify image patches (of any size) that are characterized by differences in brightness.

Techniques to extract meaningful texture descriptors from image are many, based on different models and assumptions. An effective representation of textures can be based on statistical and structural properties of brightness patterns.

Texture Content Measurement Textures may be described according to their spatial, frequency or perceptual properties. Periodicity, coarseness, preferred direction, degree of complexity are some of the most perceptually salient attributes of a texture. Feature spaces based on these attributes are particularly interesting for image retrieval by texture similarity.

Space – based models Auto-correlation function A texture can be represented taking into account the spatial size of grey-level primitives. Fine textures have a small size of their grey-level primitives. Coarse textures a large size. Co – occurrence matrix A different way of measuring textures is by taking into account the spatial arrangement of grey-level primitives.

Co-Occurrence Matrices Define a relative separation vector –e.g. 3 pixels across, 2 up Use each pair of pixels separated by the vector as matrix indices Increment this matrix element Shape of matrix characterises the texture Can be characterised by factors derived from it.

Edge Frequency Density of microedges is characteristic of texture Apply an edge detector –Sobel is suitable Threshold result Compute density of edge elements

Calculate Texture energy entropy contrast

Image features