CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding Outline: Motivation Human vision and illusions Image representation Sampling Quantization Thresholding CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding Motivation Allow computer and robots to read books. Allow mobile robots to navigate using vision. Support applications in industrial inspection, medical image analysis, security and surveillance, and remote sensing of the environment. Permit computers to recognize users’ faces, fingerprints, and to track them in various environments. Provide prostheses for the blind. Develop artistic intelligence. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding Human Vision 25% of brain volume is allocated to visual perception. Human vision is a parallel & distributed system, involving 2 eyes, retinal processing, and multiple layers of processing in the striate cortex. Most humans are trichromats and they perceive color in a 3-D color space (except for bichromats and monochromats). Vision provides a high-bandwidth input mechanism... “a picture is worth 1000 words.” CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding Visual Illusions They provide insights about the nature of the human visual system, helping us understand how it works. Mueller-Lyer illusion CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding Herman Grid Illusion CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

Herman Grid Illusion (dark on light) CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

Subjective Contour (Triangle) CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding Image Representation Sampling: Number and density of “pixel” measurements Quantization: Number of levels permitted in pixel values. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

Image Representation (cont.) Sampling: e.g., 4 by 4, square grid, 1 pixel/cm Quantization: e.g., binary, {0, 1}, 0 = black, 1 = white. 1 1 1 1 1 1 CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

Aliasing due to Under-sampling Here the apparent frequency is about 1/5 the true frequency. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding

CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding Quantization Capturing a wide dynamic range of brightness levels or colors requires fine quantization. Common is 256 levels of each of red, green and blue. Segmentation is simplified by having a small number of levels -- provided foreground and background pixels are reliably distinguished by their dark or light value. Grayscale thresholding is typically to used to reduce the number of quantization levels to 2. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding