1. Shape Detection and Recognition

2. Outline Motivation – Biological Perception Segmentation Shape Detection and Analysis Overview Project – Markov Shape Theory

3. Biological Object Recognition 3D scene reconstruction [Marr 82] – A hierarchical composition of features into complex descriptions – Extended into “Recognition by Components” [Biederman87] Structural-description model

4. Trends (cont) View-Dependant Approach [Logothetis95] – “Multi-view” representation, generalization between views. Mental rotation, interpolation, linear combination Problem: – Experiments based on “within class” discrimination. Eg. Is it robin or a sparrow ?

5. Trends (cont) Multi-view object interpolation extended class interpolation. – Need only siluettes and contours to classify into perceptual categories. Eg. Face, car, tree.

6. Object View Associations Temporal Associations – Occurrence-based association Explicit Structural Information – Global description of object shape Eg. “medial-axis” representation Implicit Structural Information – Codes relations between features

7. Shape Recognition in artificial systems “Shape from x” research, [Marr et al.76] Shape from contours Shape from shading Shape from texture Shape from stereo Shape from fractal geometry

8. Image Segmentation Threshold techniques Edge-based methods Region-based techniques Connectivity-preserving relaxation methods.

9. Threshold techniques make decisions based on local pixel information effective when the intensity levels of the objects fall squarely outside the range of levels in the background. Blurred region boundaries can create problems since spatial information is ignored.

10. Threshold Segmentation

11. Edge-based methods contour detection – weakness in connecting together broken contour lines make them prone to failure in the presence of blurring.

12. Region-based methods Image partitioned into connected regions by grouping neighboring pixels of similar intensity levels. Adjacent regions are then merged under some criterion (homogeneity or sharpness of region boundaries). – Over stringent criteria create fragmentation – Lenient ones overlook blurred boundaries and over merge.

13. Region-based segmentation

14. Active Contours Connectivity-preserving relaxation-based segmentation Start with an initial boundary shape represented in the form of spline curves, and iteratively modify it by applying various shrink/expansion operations according to some energy function. elastic energy-minimizing model coupled with the maintenance of an elastic contour model getting trapped at local minimum is a usual problem

15. Active Contours

16. Shape Analysis 4 Broad Areas – Boundary Scalar Transforms – Boundary Space Domain Techniques – Global Scalar Transform Techniques – Global Space Domain Techniques

17. Shape Analysis Techniques Boundary Scalar Transforms Boundary Scalar Transforms – Construct a 1-D function from a 2-D shape boundary – Use the 1-D function as the characteristic descriptor of the 2-D shape. Centroid-Boundary distance Fourier Transform methods Stochastic Methods

18. Shape Analysis (cont) Boundary Scalar Transforms Boundary Scalar Transforms Fourier Transform methods Stochastic Methods

19. Shape Analysis (cont) Boundary Space Domain Techniques Boundary Space Domain Techniques – Chain Code – Syntactic techniques Boundary features formed as a string S = s 1,…,s n composed of atomic elements

20. Shape Analysis (cont) Boundary Space Domain Techniques Boundary Space Domain Techniques – Boundary Approximations Curve approximation via polygonal and/or spline methods – Scale-Space Techniques Image filtered by several low-pass Gaussian filters of variable width. Any remaining inflection points deemed important object characteristics. – Boundary Decomposition Break boundary into segments

21. Shape Analysis Global Scalar Transform Techniques Global Scalar Transform Techniques – Compute the scalar result based on global properties Moments –calc area, center of mass, … Shape Matrices and Vectors Morphological Methods –Decompose shape into simple “signature” constituents via morph operations.

22. Shape Analysis Global Space Domain Techniques Global Space Domain Techniques – Medial Axis Transform Fire set to shape boundary Quench points created where fire lines intersect creating a skeleton. – Shape Decomposition Breaks shape into sub-regions and uses above methods to store sub-shape descriptions

23. Markov Shape Theory Analysis of shapes within an image Eg. Is there a face in the image

24. Hidden Markov Model

25. HMM (cont) Version 1 – Given a model and observation(s) find the most likely path with a p-depth lookahead Max-Max Search.

26. HMM (cont) Version 2 – Create a B matrix for each radius

27. References T. Fromherz, M. Bichsel, “SHAPE FROM CONTOURS AS INITIAL STEP IN SHAPE FROM MULTIPLE CUES” H.Kauppinen, T.Seppanan, M.Pietikainen, “An Experimental Comparison of Autoregressive and Fourier-based Descriptors in 2-D Shape Classification”, 1995 C.Xu, A.Yezzi, J.L. Prince, “On the Relationship between Parametric and Geometric Active Contours”, 1999. S. Loncaric, “A Survey of Shape Analysis Techniques”,1998. M.J. Tarr, H.H.Bulthoff, “Image-Based Object Recognition in Man, Monkey, and Machine”, 1998.