Image formation

refracted ray: part of incident ray that enters a surface and is bent according to Snell's law.
Lensmaker's equation: Relates the distance between the point being viewed and the lens to the distance between the lens and the ideal image location.
Optical power of a lens: How strongly a lens bends light; measured in diopters
Accommodation: Adjustment of the focal length of a lens to bring different parts of a scene into focus.
Depth of field: Range of distances in focus
Chromatic abberation: Prism effect of focusing light of different wavelengths at different distances behind the lens.
Fovea: Area of highest concentration of cones in the visual system.

Segmentation by thresholding

pixel: individual grid element of a digital image
intensity or grey level: Measure of reflected light in a pixel
image noise: statistical variations in the observed grey levels; may be due to inherent sensor limitations or visual teture.
blur: Edges in images are not "sharp" but are defocused by either optical imperfections or by smoothing the image before edge detection.
binary image: An image in which each pixel is either a 0 or a 1.
thresholding: Convering a grey scale imageto a binary image based on whether a pixel in the grey scale image has intensity greater than or less than or equal to a given constant (the threshold).
histogram: Observed frequency distribution of image grey levels.

4-neighbors: A pixel's horizontally and vertically adjacent pixels are its 4-neighbors
8-neighbors: The 4-neighbors plus the diagonally adjacent pixels.
4-adjacent: Given two sets of pixels, A and B, a is 4-adjacent to B is a pixel in A is a 4 neighbor of a pixel in B. Similar definition for 8-adjacent.
path: Sequence of pixels in which consecutive pixels are adjacent. Comes in 4 and 8 varieties.
foreground: Set of all 1's in a binary image
connected: Within a set S, if p and q are in S, then p is connected to q is there is a path from p to 1; 4 and 8 versions.
connected components: The equivalence classes of the is-connect-to relation.
background: The component of 0's of a binary image that is adjacent to the edge of the image. For this purpose, we envision the image as being surrounded by extra rows and columns of 0's.
boundary: Boundary of a component of 1's is the subset of that component adjacent to a 0.
interior: The set difference between a component and its boundary

gradient: vector whose first compoenent is the direction in which the first derivative is highest and whose second component is the magnitude of the first derivative in that direction.
convolution: Pointwise multiplication of each kxk neighborhood of a digital image by a kxk kernel of convolution weights
separable function: A function F(x,y) is separable if it can be written as f(x,y)=g(x)h(y)
non-maxima supression: Procedure for thinning the responses of an edge detection. Retain as edge points only pixels whose gradient magnitude is greater than the magnitudes of its neighbors in the gradient direction.
simple point: A 1-point in a binary image is a simple point if changing its value froma 1 to a 0 does not change the number of connected components in its 3x3 neighborhood. There are 4 and 8 simple points.
endpoint: A 1 pixel in a binary image is an end point if it has exactly one neighbor whose value is 1. There are 4 end points and 8 end points.
junction: A junction is a 1 pixel in a thinned binary image that has more than 2 neighbors that are 1's.
curvature: Rate of change of slope with respect to arc length.

template: An image model for an object; can be grey level or binary.
pyramid: Multiresolution image representation in which an image at level i is obtained from the image at level i-1 by smoothing (ordinarily Gaussian) and sampling. The base of the pyramid, level 0, is the original image.
sum of squared differences: An image matching measure in which one integrates the squared difference in grey levels bstween the image and a template.
Hough transform: Parameter space clustering algorithm for template matching. Originally developed for line detection, it can be simply extendedto template matching.
Distance Transform: Given a binary image, its distance transform is an image in which each pixel is assigned its distance from the nearest 1.
chamfer matching: Convolution of a binary edge template with a chamfer image for matching.
Hausdorff matching: a minimax distance metric for comparing a binary template to a binary image.