0
Upcoming SlideShare
×

# Image segmentation ajal

795

Published on

SEGMENTATION BASICS, Evaluations of RGB Color space ,HSV VS RGB ,minimum cut ,Unknown clusters and centers

0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

Views
Total Views
795
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
45
0
Likes
0
Embeds 0
No embeds

No notes for slide
• purple color
• ### Transcript of "Image segmentation ajal"

1. 1. SEGMENTATION OF FOREGROUND – BACKGROUND FROM NATURAL IMAGES B Y AJAL.A.J ASSISTANT PROFESSOR UNIVERSAL ENGINEERING COLLEGE
2. 2. OUTLINE  Introduction  Types of segmentation algorithms  Evaluations of RGB Color space  SEGMENTATION  EXPERIMENTAL RESULTS  Summary  Appendix
3. 3. ABSTRACT  This paper presents a part of a more challenging research project aimed at developing a computer vision system for a robot capable of identifying all objects from known natural backgrounds such as forest, sky, ocean, under-water scenes and etc.  Segmentation is an import issue in the field of machine vision for detection and recognition of objects.  The success of segmentation is solely depends on the separation of foreground objects from background objects.  We present a simple framework to extract the foreground objects from the known natural backgrounds in still and moving images using pixel based color segmentation in RGB space.
4. 4. What is an Image?  2D array of pixels  Binary image (bitmap)  Pixels are bits  Grayscale image  Pixels are scalars  Typically 8 bits (0..255)  Color images  Pixels are vectors  Order can vary: RGB, BGR  Sometimes includes Alpha
5. 5. What is an Image?  2D array of pixels  Binary image (bitmap)  Pixels are bits  Grayscale image  Pixels are scalars  Typically 8 bits (0..255)  Color images  Pixels are vectors  Order can vary: RGB, BGR  Sometimes includes Alpha
6. 6. What is an Image?  2D array of pixels  Binary image (bitmap)  Pixels are bits  Grayscale image  Pixels are scalars  Typically 8 bits (0..255)  Color images  Pixels are vectors  Order can vary: RGB, BGR  Sometimes includes Alpha
7. 7. What is an Image?  2D array of pixels  Binary image (bitmap)  Pixels are bits  Grayscale image  Pixels are scalars  Typically 8 bits (0..255)  Color images  Pixels are vectors  Order can vary: RGB, BGR  Sometimes includes Alpha
8. 8. What is an Image?  2D array of pixels  Binary image (bitmap)  Pixels are bits  Grayscale image  Pixels are scalars  Typically 8 bits (0..255)  Color images  Pixels are vectors  Order can vary: RGB, BGR  Sometimes includes Alpha
9. 9. HSV VS RGB.  In day to day practice, we'll most likely use two models: HSV and RGB. HSV stands for Hue, Saturation, and Value, and it uses these three concepts to describe a color. RGB the three colors that make up an image on a monitor.
10. 10. RGB Color cube
11. 11. Color segmentation  In the problem of segmentation, the goal is to separate spatial regions of an image on the basis of similarity within each region and distinction between different regions.  Approaches to color-based segmentation range from empirical evaluation of various color spaces, to clustering in feature space , to physics-based modeling  The essential difference between color segmentation and color recognition is that the former uses color to separate objects without a priori knowledge about specific surfaces; the latter attempts to recognize colors of known color characteristics
12. 12. Segmentation: Elephant and Blind Men Syndrome
13. 13. SEGMENTATION Segmented image – giving us the outline of her face, hand etc Colour Image having a bimodal histogram
14. 14. Results on color segmentation
15. 15. SEGMENTATION  Partitioning images into meaningful pieces, e.g. delineating regions of anatomical interest.  Edge based – find boundaries between regions  Pixel Classification – metrics classify regions  Region based – similarity of pixels within a segment
16. 16. minimum cut “allegiance” = cost of assigning two nodes to different layers (foreground versus background) foreground node background node pixel nodes allegiance to foreground allegiance to background pixel-to-pixel allegiance
17. 17. minimum cut “allegiance” = cost of assigning two nodes to different layers (foreground versus background) foreground node background node pixel nodes allegiance to foreground allegiance to background pixel-to-pixel allegiance
18. 18. Normalized Cuts • Graph partitioning technique • Bi-partitions an edge-weighted graph in an optimal sense • Normalized cut (Ncut) is the optimizing criterion i j wij Edge weight => Similarity between i and j A B Minimize Ncut(A,B) Nodes • Image segmentation • Each pixel is a node • Edge weight is similarity between pixels • Similarity based on color, texture and contour cues
19. 19. 21 Unknown clusters and centers Maximization step: Find the center (mean) of each class Start with random model parameters Expectation step: Classify each vector to the closest center
20. 20. 22 Finding the centers from known clustering
21. 21. Segmentation fault  A segmentation fault (often shortened to segfault) or access violation is a particular error condition that can occur during the operation of computer software.  A segmentation fault occurs when a program attempts to access a memory location that it is not allowed to access, or attempts to access a memory location in a way that is not allowed (for example, attempting to write to a read-only location, or to overwrite part of the operating system).
22. 22. Segmentation Methods  Thresholding approaches  Region Growing approaches  Classifiers  Clustering approaches  Markov random fields (MRF) models  Artificial neural networks  Deformable models  Atlas-guided approaches 24
23. 23. Thresholding  Suppose that an image, f(x,y), is composed of light objects on a dark background, and the following figure is the histogram of the image.  Then, the objects can be extracted by comparing pixel values with a threshold T. 25
24. 24. Region Growing 1. Define seed point 2. Add n-neighbors to list L 3. Get and remove top of L 4. Test n-neighbors p if p not treated if P(p,R)=True then p→L and add p to region else p marked boundary 5. Go to 2 until L is empty  Two Regions R and ¬ R SeedpointsSeedpoints ElementinElementinL BorderelementBorderelementRegionelementRegionelement
25. 25. Our approach: The Algorithm  The left and right images areThe left and right images are segmented and each areasegmented and each area identifies a node of a graphidentifies a node of a graph  A bipartite graph matchingA bipartite graph matching between the two graphs isbetween the two graphs is computed in order to match eachcomputed in order to match each area of the left image with onlyarea of the left image with only one area of the right imageone area of the right image  This process yields a list ofThis process yields a list of reliably matched areas and a listreliably matched areas and a list of so-called don’t care areas.of so-called don’t care areas.  The Outputs of the algorithmThe Outputs of the algorithm are the disparity map and theare the disparity map and the performance mapperformance map
26. 26. GPCA Generalized Principal Component Analysis (GPCA) method for.  modeling and segmenting mixed data using a collection of subspaces  done by introducing certain algebraic models into data clustering.  Unique property (applied to images) is that it decomposes images into regions with fundamentally different characteristics and derives an optimal PCA-based transformation for each region.
27. 27. Computing a principal component analysis To compute a principal component analysis in SPSS, select the Data Reduction | Factor… command from the Analyze menu.
28. 28. Segmentation Example
29. 29. Intelligent Scissors  Fully automatic segmentation is an unsolved problem due to wide variety of images.  Intelligent Scissors is a semi-automatic general purpose segmentation tool.  The efficient and accurate boundary extraction, which requires minimal user input with a mouse, is obtained.  The underlying mechanism for the Intelligent Scissors is the “live-wire” path selection tool.
30. 30. More Complex Segmentation Methods - snakes
31. 31. One More Thing
32. 32. VLSI IMPLEMENTATION
33. 33. Floor plan of the prototype chip Layout of the encoder module
34. 34. Pros & Cons  Very useful for rapid prototyping  Strongly growing community and code base  Problems:  Very complex  Overhead -> higher run-times  Still under development
35. 35. Summary / Closing Thoughts  Segmentation is the essential but critical problem in the field of machine vision. At a stretch, robotics can not be done with a complete knowledge about foreground and background objects.  We have proposed pixel based color segmentation approach to segment the known backgrounds such as forest, sky, ocean, underwater scenes and etc. which will be of unique color generally and the results obtained were satisfactory.  This color segmentation process will overcome the main problems with change of pose and occlusion and overcomes the limitation occurs in the motion analysis and background subtraction methods.
36. 36. Conclusions  Translation (visual to semantic) model for object recognition  Identify and evaluate low-level vision processes for recognition  Feature evaluation  Color and texture are the most important in that order  Shape needs better segmentation methods  Segmentation evaluation  Performance depends on # regions for annotation  Mean Shift and modified NCuts do better than original NCuts for # regions < 6  Color constancy evaluation  Training with illumination helps  Color constancy processing helps (scale-by-max better than gray-world)
37. 37. Reference Reading  Digital Image Processing Gonzalez & Woods, Addison-Wesley 2002  Computer Vision Shapiro & Stockman, Prentice-Hall 2001  Computer Vision: A Modern Approach Forsyth & Ponce, Prentice-Hall 2002  Introductory Techniques for 3D Computer Vision Trucco & Verri, Prentice-Hall 1998
38. 38. REFERENCES :  S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Color and texture-based image segmentation using EM and its application to content-based image retrieval," 6th International Conference on Computer Vision, pp.675–682, 1998.  E. Saber, A.M. Tekalp, R. Eschbach, and K. Knox, "Automatic image annotation using adaptive color classification," Graph. Models Image Process., vol.58, no.2, pp.115–126, 1996.  S.C. Pei and C.M. Cheng, "Extracting color features and dynamic matching for image data-base retrieval," IEEE Trans. Circuits Syst. Video Technol., vol.9, no.3, pp.501–512, April 1999.  T. Pavlidis and Y.-T. Liow, "Integrating region growing and edge detection," IEEE Trans. Pattern Anal. Mach. Intell., vol.12, no.3, pp.225–233, March 1990.  C.-C. Chu and J.K. Aggarwal, "The integration of image segmentation maps using region and edge information," IEEE Trans. Pattern Anal. Mach. Intell., vol.15, no.12, pp.1241–1252, Dec. 1993.  J. Fan, D.K.Y. Yau, A.K. Elmagarmid, and W.G. Aref, "Automatic image segmentation by integrating color-edge extraction and seeded region growing," IEEE Trans. Image Process., vol.10, no.10, pp.1454–1466, Oct. 2001.
39. 39. QUERRIES ? Thank you.
40. 40. AJAL.A.J ASSISTANT PROFESSOR UNIVERSAL ENGINEERING COLLEGE THRISSURMAIL : ec2reach@gmail.com MOB : 0890 730
1. #### A particular slide catching your eye?

Clipping is a handy way to collect important slides you want to go back to later.