 # The local contrast at an image point denotes the (relative) difference between the intensity of the point and the intensity of its neighborhood

Yüklə 445 b.
 tarix 06.05.2018 ölçüsü 445 b. #43134    • ## The local contrast at an image point denotes the (relative) difference between the intensity of the point and the intensity of its neighborhood: • ## In general it is said that the image contrast is high if the image gray-levels fill the entire range • ## Problems:

• Outlier sensitive. • ## defines the accumulated histogram  • ## The image histogram does not fully represent the image  • ## The image s.t.d. : • ## Measures the averaged amount of information required to encode the image values. • ## Entropy is a measure of histogram dispersion • ## Examples:

• Pattern detection
• tracking • ## H(x,y)= H(x,y-1)+H(x-1,y) – H(x-1,y-1) • ## Using integral histogram we can calculate local histograms of any window H(x1:x2,y1:y2) • ## Measuring image properties:

• Average
• Variance
• Entropy
• Contrast
• Area (for a given gray-level range)

• ## Image Enhancement

• Histogram equalization
• Histogram stretching
• Histogram matching • ## How can we measure the amount of blurring? • ## Algorithm: Adjust lens according the changes in the histogram s.t.d.     • ## How can we choose the the local threshold values? • ## Segment each pixel to its closest cluster. • ## Example Usage:

• Tracking
• Image retrieval
• Registration
• Detection
• Many more ... • ## Problem: distance may not reflects the perceived dissimilarity: • ## Suffers from the same drawback of the Minkowski distance. • ## The term F={fij} is an admissible flow from HA(i) to HB(j)      • ## Can be applied in high dim. histograms (color). • ## Define CA and CB as the accumulated histograms of image A and B respectively:  Yüklə 445 b.

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