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Machine Vision 1392.10. The aim of course: To give sufficient theoretical depth on important topics on Machine vision and using the OpenCV programming developing environment to implement programming assignments and course project. - PowerPoint PPT Presentation
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Machine Vision 1392.10.
The aim of course:
To give sufficient theoretical depth on important topics on Machine vision and using the OpenCV programming developing environment to implement programming assignments and course project.
• After graduating this course, it is expected that the students have enough theoretical and practical knowledge to start industrial applications of Machine Vision.
Machine Vision
Text Books: 1. Computer Vision Linda G.Shapiro, George C.Stockman Prentice Hall, 2001
2. Digital Image Processing (3rd Edition) Rafael C.Gonzalez, Richard E.Woods Prentice Hall, 2008
3. Learning OpenCV, Computer Vision with the OpenCV Library Gary Bradski and Adrian Kaebler, O’REILLY, 2008
Course Syllabus Chapter-1
Introduction, What is Machine VisionIts applicationsRelation of Machine Vision with related fields such as
Image Processing, Computer Graphics and Artificial Intelligence
Chapter-2 Digital Image Fundamentals
Elements of visual perceptionStructure of the Human eyeImage formation in the EyeBrightness adaption and discrimination
Light and Electromagnetic spectrum
Chapter-2 Digital Image Fundamentals
Image sensing and acquisitionImage acquisition using a single sensorImage acquisition using sensor stripsImage acquisition using sensor arraysA simple image formation model
Image sampling and quantizationBasic concepts in sampling and quantizationRepresenting digital imagesSpatial and intensity resolutionImage interpolation
Chapter-2 Digital Image Fundamentals
Some basic relationships between pixelsNeighbors of pixelsAdjacency, Connectivity, Regions, and BoundaryDistance measures
An introduction to mathematical tools used in digital image processing
Array versus matrix operationsLinear versus non-linear operationsArithmetic operations
Chapter-3 Binary Image Analysis
Pixels and neighborhoodsApplying masks to imagesCounting the objects in an imageConnected component labeling
Binary Image MorphologyStructuring elementBasic morphological operationsDilation and ErosionOpening and ClosingThe Hit-or-Miss transformation
Chapter-3 Binary Image Analysis
Some basic morphological algorithms
– Boundary extraction– Hole filling– Extraction of connected components– Convex Hull– Thinning– Thickening– Skeleton– Pruning
Chapter-3 Binary Image Analysis
Region Properties
Region adjacency graphs
Thresholding gray-scale images (some basic methods)
4. Image Segmentation
Fundamentals
Point, Line, and edge detectionBackgroundDetection of isolated pointsLine detectionEdge modelsBasic edge detectionMore advanced techniques for edge detectionEdge linking and boundary detection
4. Image Segmentation
Thresholding
FoundationBasic Global thresholdingOptimum global thresholding and Otsu’s MethodMultiple thresholding Variable thresholdingMultivariable thresholdingThresholding in un-even illumination
4. Image Segmentation
Region-based segmentationRegion growingRegion splitting and merging
Segmentation using morphological watersheds
The use of motion in segmentation
5. Color and Shading
Color Fundamentals
Color ModelsThe RGB color modelThe CMY and CMYK color modelThe HIS, YIQ and YUV color models
Pseudo color image processingBasics of full color image processing
5. Color and Shading
Color TransformationColor slicing Tone and color correctionsColor histograms
Image segmentation based on color
Noise in color images
5. Color and Shading
Shading
Radiation from one light sourceDiffuse reflectionSpecular reflectionDarkening with distancePhong model of shading
5. Color and Shading
Color constancy
The color of objects taken under different lighting conditions (lights different from white) look different from their real color.
How can we convert these colors to their real colors as if the image was taken under normal white color.
6. Texture
Texture, Texels (Texture element) and StatisticsTexture descriptions
Quantitative (statistical) texture measures - Co-occurrence matrices - Laws texture energy measures - Tamura texture measure
* In computer graphics, textures are represented by array of texles.
6. Texture
Texture segmentation
Structural approachesSpectral approaches
7. Content based image retrieval
Image database examples
Database organization - Standard indexing - Spatial indexing - Indexing for content based image retrieval with multiple measures
Image database QueriesImage distance measures
- Color, Texture and Shape similarity measures
Precision and Recall measures to evaluate the performance of a CBIR system
8. Representation and description
RepresentationBoundary followingChain codesSignatures
Boundary descriptors Regional descriptorsUse of Principal components for descriptionAn example for object recognition
9. Motion from 2D image sequences
Motion phenomena and applicationsImage subtraction
Computing motion vectorsUsing point correspondences MPEG compression of videoComputing image flow
Computing the path of moving pointsDetecting significant changes in video
10. Perceiving 3D from 2D images
Intrinsic imagesLabeling of line drawing from blocks world3D cues available in 2D imagesThe perspective imaging model
Depth perception from stereoEstimating correspondences using cross correlationCorrespondences using epipolar constrains
10. Perceiving 3D from 2D images
The thin lens equation
Lens distortion
11. Tracking
Different vision systems used for motion detectionReference imageA control traffic application
Corner findingInvariant Features
SIFT (Scale Invariant Feature Transform)
11. Tracking
Mean-Shift segmentation and TrackingCam-shift tracking
Kalman Filter
Particle filtersIntroducing tracking systems based on machine learning approaches
12. Camera model and calibration
Intrinsic camera parameters
Extrinsic camera parameters
A calibration example
13. Omni-directional mirrors and vision system
Applications of Omni-Vision system
Design parameters for omni-mirrors
Camera calibration in omni-vision
Omni-vision application in soccer robots
14. Introducing some industrial applications of machine vision in Iran
1. How to detect defected eggs
2. Selecting the best stones for asphalt
Course Evaluation
1. Mid term exam-1 (2.5)2. Mid term exam-2 (2.5)2. Final Exam ( 5 )3. Seminar (Oral presentation of a research paper or a
book chapter by students) ( 2 )
4. Course work (Implementation Machine Vision algorithms using OpenCV) ( 6 )
5. Course Project (Programming a research topics that may be related to the seminar) ( 2 )
