2CE433 IMAGE PROCESSING AND PATTERN RECOGNITION [ 3 0 2 4 ]

weakassuredAI and Robotics

Nov 6, 2013 (3 years and 10 months ago)

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2CE433 IMAGE PROCESSING AND PATTERN RECOGNITION



[ 3 0 2 4 ]


Introduction: What is Digital Image Processing? Origin and Applications of subject,

Fundamental Steps in Digital Image Processing and Components of an Image Processing System


Digital Image Fundamentals: Elements of Visual Perception, Light and Electromagnetic Spectrum, Image
Sensing and Acquisition, Image Sampling and Quantization, Some Basic Relationships Between Pixels
and Linear and Nonlinear Operations


Image Enhancement in

the Spatial Domain: Background, Some Basic Gray Level Transformations,
Histogram Processing Enhancement Using Arithmetic/Logic Operations, Basics of Spatial Filtering,
Smoothing Spatial Filters, Sharpening Spatial Filters, Combining Spatial Enhancement Me
thods.


Image Enhancement in Frequency Domain: Background, Introduction to the Fourier Transform and the
Frequency Domain, Smoothing Frequency Domain Filters, Sharpening Frequency Domain Filters,
Implementation.


Image Restoration: A Model of the Image D
egradation/Restoration Process, Noise Models, Restoration in
the Presence of Noise Only
-
Spatial Filtering, Periodic Noise Reduction by Frequency Domain Filtering,
Linear, Position
-
invariant Degradations, Estimating the Degradation Function, inverse Filteri
ng,
Minimum Mean Square Error(Wiener) Filtering, Constrained Least Squares Filtering, Geometric Mean
Filter, Geometric Transformations.


Color Image Processing: Pseudocolor Image Processing


Image Compression: Fundamentals, Image Compression Models, Error
-
Free Compression.

Image Segmentation: Detection of Discontinuities, Thresholding, Region
-
Based Segmentation, the Use
of Motion in Segmentation.


Introduction to Pattern Recognition: Applications of Pattern Recognition, Statistical Decision Theory,
Baye’s
Theorem, Multiple features, Condition Independent features, Characterization of Dissimilarities.
Estimation of Error Rates, Classification Techniques, Classifiers, Decision Making Techniques,
Clustering Hierarchical, Partition Clustering, Computer Vision.



Laboratory work:

The Practical and Term work will be based on the topics covered in the syllabus.

Minimum 10 experiments should be carried out.


Text/Reference Book:

1.

Digital Image Processing
-
By Rafael C. Gonzalez, Richard E. Woods, Pearson
Education

2.

Pattern Recognition and Image Analysis


By Earl Gose, Johnsonbaug, Steve Jost, PHI

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