24-06-2013, 04:06 PM
Vision Review:Image Processing
Vision Review.ppt (Size: 6.93 MB / Downloads: 360)
Images
An image is a matrix of pixels Note: Matlab uses
Resolution
Digital cameras: 1600 X 1200 at a minimum
Video cameras: ~640 X 480
Grayscale: generally 8 bits per pixel Intensities in range [0…255]
RGB color: 3 8-bit color planes
Color Representation
RGB, HSV (hue, saturation, value), YUV, etc.
Luminance: Perceived intensity
Chrominance: Perceived color
HS(V), (Y)UV, etc.
Normalized RGB removes some illumination dependence:
Binary Operations
Dilation, erosion (Matlab: imdilate, imerode)
Dilation: All 0’s next to a 1 1 (Enlarge foreground)
Erosion: All 1’s next to a 0 0 (Enlarge background)
Connected components
Uniquely label each n-connected region in binary image
4- and 8-connectedness
Matlab: bwfill, bwselect
Moments: Region statistics
Zeroth-order: Size
First-order: Position (centroid)
Second-order: Orientation
Image Transformations
Geometric: Compute new pixel locations
Rotate
Scale
Undistort (e.g., radial distortion from lens)
Photometric: How to compute new pixel values when non-integral
Nearest neighbor: Value of closest pixel
Bilinear interpolation (2 x 2 neighborhood)
Bicubic interpolation (4 x 4)
Filtering
Idea: Analyze neighborhood around some point in image with filter function ; put result in new image at corresponding location
System properties
Shift invariance: Same inputs give same outputs, regardless of location
Superposition: Output on sum of images =
Sum of outputs on separate images
Scaling: Output on scaled image = Scaled output on image
Linear shift invariance Convolution