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Advanced Topics – I (EENG 4010) Computer Vision & Image Analysis (EENG 5640). Introduction to Computer Vision. Image Processing System. Image. Image. Computer Vision/ Image Analysis/ Image Understanding System. Image/ Scene Description. Image. Pattern Classification Label.
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Advanced Topics – I (EENG 4010)Computer Vision & Image Analysis (EENG 5640)
Introduction to Computer Vision Image Processing System Image Image Computer Vision/ Image Analysis/ Image Understanding System Image/ Scene Description Image Pattern Classification Label Pattern Recognition System Pattern Vector (with Image measurements as components in the current application) Computer Vision generally involves pattern recognition
Typical Computer Vision Applications • Medical Imaging • Automated Manufacturing (some experts use Machine vision as the term to describe Computer Vision for Industrial applications; others use it as synonym for computer vision) • Remote Sensing • Character Recognition • Robotics
Binary Image Analysis • Grey scale to Binary transformation (Otsu’s method) • Counting holes • Counting objects • Connected Component Labeling Algorithms • Recursive Algorithm • Two Pass Row by Row Labeling Algorithm
Two-Pass Algorithm: Illustrative Example 1 2 3 4 Label Parent
Binary Image analysis (Contd.) • Morphological Processing • Dilation, Erosion, Opening and Closing Operations • Example to Illustrate the effects of the operations • Region Properties • Area • Perimeter • Circularity
Grey Level Image Processing • Image Enhancement Methods • Histogram Equalization and Contrast Stretching • Mitigation of Noise Effects • Image Smoothing • Median Filtering • Frequency Domain Operations (Low Pass Filtering) • Image Sharpening and Edge Detection • High Pass Filtering • Differencing Masks (Prewitt, Sobel, Roberts, Marr-Hildreth operators) • Canny Edge Detection and Linking
Color Fundamentals [0, 1, 1] Cyan [0, 1, 0] Green [1, 1, 0] yellow [0, 0, 1] Blue [1, 0, 1] Magenta [1, 1, 1] White [1, 1, 1] White [0, 1, 1] Cyan [1, 0, 0] Red [0, 0, 0] Black [0, 1, 0] Green [1, 0, 0] Red [1, 0, 1] Magenta [0, 0, 1] Blue [1, 1, 0] yellow
HIS-RGB Conversion Method is given. You need to reason out why? Or explore web for answer.
YIQ and YUV Systems for TVs, etc. YIQ system is used in TV Signals. Its components are: Luminance Y = 0.30R + 0.59G + 0.11B R-Cyan I = 0.60R - 0.28G - 0.32B Magenta-Green Q = 0.21R - 0.52G + 0.31B In some digital products and JPEG/MPEG Compression algorithms, YUV System as follows is used: Y = 0.30R + 0.59G + 0.11B U = 0.493* (B – Y) V = 0.877*(R – Y) Advantage: Luminance and Chromaticity components can be coded with different number of bits.
Texture • Pattern caused by a regular spatial arrangement of pixel colors or intensities. • Two approaches • Structural or Syntactic (usually used in case of synthetic images by defining a grammar on texels). • Statistical or quantitative (more useful in natural texture analysis; can be used to identify texture primitives (texels) in the image.
Quantitative Texture Measures • Edge related • Edginess (proportion of strong edges in a small window around pixels. • Edge direction histograms (the pattern vector constituted by the proportion of the edgels in the horizontal, vertical, and other quantized directions among the total pixels in a chosen window around a pixel (I,j) under consideration • Co-occurrence matrix based
Co-occurrence matrix based Measures • Construction of Co-occurrence matrix Cd [i,j] where d is the displacement of j from i (e.g. (0,1), (1, 1), etc. • Normalized and symmetric co-occurrence matrices Nd [i,j] and Sd [i,j]. • Zucker and Terzopoulos’s Chi-square metric to choose the best d (i.e. d with most structure). • Numeric measures from Nd [i,j]
Choice of the Best Co-occurrence Matrix and Computation of Features
Laws’ Texture Energy Measures • Simple because masks are used • 2-D masks are created using 1-D masks: • L5 (Level) = [ 1 4 6 4 1] • E5 (Edge) = [-1 -2 0 2 1] • S5 (Spot) = [-1 0 2 0 -1] • W5 (Wave) = [-1 2 0 -2 1] (not in the text!) • R5 (Ripple) = [ 1 – 4 6 -4 1] (e.g. 5x5 matrix of L5E5 mask is obtained by multiplying transpose of L5 by E5).
Laws’ Algorithm for Texture Energy Pattern Vector Construction
Gabor Filter Based Texture Analysis Gabor Filter is mathematically represented by (refer Wikipedia): Where and θ Orientation of the normal to parallel stripes λ Wavelength or inverse of the frequency of the cosine function g σ Spatial aspect ratio Sigma of the Gaussian function ψ Phase offset of the cosine function
Image Segmentation Image Segmentation Contour-Based Methods (e.g. Canny Edge Detection and Linking) Region-Based Methods Region Growing (e.g. Haralick and Shapiro Method) Partitioning/Clustering (e.g. K-Means Clustering, Isodata clustering, Ohlander’ et al.’s recursive histogram-based technique)
Clustering Algorithms • Clustering (partitioning) of pixels in the pattern space • Each pixel is represented by a pattern vector of properties. For example, in case of a colored image, we could have • could be of any dimensionality (even 1, i.e. could be a scalar as in case of a grey level image). • Depending upon the problem, may include other measurements on texture, shading, etc. that constitute additional dimensional components of the pattern vector. Note: i and j denote pixel row & columns.
ISODATA Clustering Problem m3 g m2 m1 r To which cluster does X belong to? If split threshold TS = 3.0 and Merge Threshold TM = 1.0, what will be the new cluster configuration? Get new cluster means in case of a Split.
Image Databases- Content-Based Image Retrieval Any Problem with Traditional Text (in Caption) Based Retrieval? Typical SQL (Structured Query Language) Query: SELECT * FROM IMAGEDB WHERE CATEGORY = ‘GEMS’ AND SOURCE = ‘SMITHSONIAN’ AND (KEYWORD = ‘AMETHYST’ OR KEYWORD = ‘CRYSTAL’ OR KEYWORD = ‘PURPLE’) This will retrieve the gem collection of the Smithsonian Institute from its IMAGEDB database restricting its search based on the logical combination of the keyword specified. Looks like no problem here!
Limitations of the Key Word Based Retrieval • Human coding of key words is expensive; but still some keywords by which one likes to retrieve the image cannot be visualized and hence may be left out. Key words may sometimes retrieve unexpected images as well! • What kind of images do you expect with the key word ‘pigs’?
Content-Based Image Retrieval • Uses Query-By-Example (QBE) Concept • IBM’s QBIC (Query By Image Content) is the first system • In QBE systems, you specify an example plus some constraints • Typical example images for specification- • A digital Photograph • User painted drawing • A line-drawing sketch
Matching- Image Distance (Similarity Measures) 4 Major classes: • Color Similarity • Texture Similarity • Shape Similarity • Object and relationship similarity
Color Similarity Measures • QBIC lets the user choose up to 5 colors from the color table and specify their percentages • Color histograms (K-bin) can be used Here h(I) and h(Q) are K-bin histograms of images I and Q, and A is (K x K) similarity matrix.
Color-Layout-Based Similarity • Distances between corresponding grid squares of the database and example images are found and summed up. • Each grid square spans over multiple pixels. Then how do you compare grid squares? • Use Mean Color • Use Mean and Standard Deviation • Use Multi-bin Histogram
Texture-Based Similarity Measure • Pick-and-Click distance • Grid based texture similarity can be found by the same process as in the gridded color case
Shape-Based Similarity Measures • Histogram approach is difficult to apply particularly when you want scale and rotation invariance. • Boundary Matching • Granlund’s Fourier Descriptors for Translation, Scale, starting point (for boundary tracing), and rotation invariant matching.
Boundary (Sketch) Matching • Obtain a normalized image- reduce the original image to a fixed size, e.g., (64x64) & median filter • 2 stage edge detection-global and local thresholds. • Perform linking and thinning. • Find Correlation between line drawing (L)’s grid square and various shifts (n) of the DB image A’s grid square & sum up best correlations.
