This presentation is the property of its rightful owner.
1 / 26

# Chapter 6. Color & Shading PowerPoint PPT Presentation

Chapter 6. Color & Shading. Perception of objects. Perception of objects. The spectrum (energy) of light source . The spectral reflectance of the object surface . The spectral sensitivity of the sensor. How do we see an object?. Light. Eyes. Object. Luminance  Lightness

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

### Perception of objects

• The spectrum (energy) of light source.

• The spectral reflectance of the object surface.

• The spectral sensitivity of the sensor.

### How do we see an object?

Light

Eyes

Object

• Luminance Lightness

• Chrominance Color

Human eye is more sensitive to luminance than to chrominance

### RGB Colors

• Colors specify:

• A mixture of red, green, and blue light

• Values between 0.0 (none) and 1.0 (lots)

• Color

• Red Green Blue

• White 1.0 1.0 1.0

• Black 0.0 0.0 0.0

• Yellow 1.0 1.0 0.0

• Magenta 1.0 0.0 1.0

• Cyan 1.0 1.0 0.0

r+g+b=1

### YIQ Model

• TV transmission  digital space YCBCR

•  analog space YIQ (NTSC)

•  YUV (PAL)

### Color Histogram

• Color Histogram are relatively invariant to

• Translation

• Rotation

• Scaling

• Simple methods for color histogram construction

• Concatenate the higher order two bits of each RGB color code.  64 bins

• Compute three separate RGB histograms (4 bits each) and just concatenate them into one.  48 bins

### Similarity measure for histogram matching

• It is common to smoothing the histogram before matching

•  to adapt minor shifts of the reflectance spectrum.

### Color Segmentation

A plot of pixels (r,g) taken from different images containing faces.

(r,g) : normalized red and green values

### Face Detection

• Face region classification. (R>G>B)

• Connected Component Labeling.

• Select the largest component as face object assuming there is only one face in the image.

• Discard remaining components or merges them with the face object.

• Computing the location of eyes and nose.

### Three types of Material Reflection

• Diffuse Color of reflected light from diffuse reflection (light scattered randomly)

• Ambient Amount of background light the surface reflects

• Specular Color of reflected light from specular reflection (light reflected in a regular manner)

### Complications

• The above models of illumination and reflection are simplified.

• Some objects reflect light as well as emit light. For example: light bulbs.

• In uncontrolled scenes, such as outdoor scenes, it is much more difficult to account for the different phenomena.