Program Studi S-1 Teknik Informatika FMIPA Universitas Padjadjaran VISI KOMPUTER D10K-7C02 Semester Ganjil 2016-2017 CV09: Corner Detection (Interest Point Detection) Dr. Setiawan Hadi, M.Sc.CS. Program Studi S-1 Teknik Informatika FMIPA Universitas Padjadjaran

Perkuliahan dalam 2 Minggu yad Topik 1 November 2016: Corner Detection dan Chaincode 8 November 2016: Object Recognition 15 November 2016: Stereo Vision

Overview Corner Detection In general terminology : interest point detection to extract certain kinds of features from an image frequently used in motion detection image matching, tracking image mosaicking panorama stitching 3D modelling object recognition.

Corner the intersection of two or more edges (special case of interest points In general, interest points could be: Isolated points of local intensity maximum or minimum. Line endings. Points on a curve where the curvature is locally maximized

Why are interest points useful? For establishing corresponding points between images. panorama stitching stereo matching left camera right camera

How could we find corresponding points? ( ) = ? feature descriptor

Interest point detectors should be covariant Features should be detected in corresponding locations despite geometric or photometric changes.

Interest point descriptors should be invariant Should be similar despite geometric or photometric transformations ( ) = ? feature descriptor

Invariance vs Covariance Invariance: refers to the property of objects being left unchanged by symmetry operations. Covariance: preserved by a change of coordinate system.

Invarian dan Kovarian

Example

Harris Corner Detection Basic Idea

Constraints

Concept Methods Contour based Intensity based Extract contours and search for maximal curvature or inflexion points (i.e., curvature zero-crossing) along the contour. Intensity based Compute a measure that indicates the presence of an interest point (1) directly from gray (or color) values or (2) by first fitting a parametric model to the gray (or color) values. Methods using parametric models can localize corners to sub-pixel accuracy but are more expensive.

Methods Moravec Operator Harris Operator Others See Wikipedia

Moravec Operator First operators for interest point detection Developed by Hans P. Moravec in 1977 for his research involving the automatic navigation of a robot through a clustered environment. Moravec defined the concept of “points of interest” in a image and concluded these interest points could be used to find matching regions in different images.

Harris Operator Developed by Chris Harris and Mike Stephens in 1988 as a processing step to build interpretations of a robot’s environment based on image sequences. Like Moravec, they needed a method to match corresponding points in consecutive image frames, but were interested in tracking both corners and edges between frames. Improved upon Moravec’s corner detector by considering the differential of the corner score with respect to direction directly. The Harris corner detector computes the locally averaged moment matrix computed from the image gradients, and then combines the Eigenvalues of the moment matrix to compute a corner measure, from which maximum values indicate corners positions

Harris Detector Examples

Harris Detector Examples

Harris Detector Examples

Harris Detector Examples

Harris Detector Examples

Tugas C# MATLAB File hasil dikirim ke email Akses ke http://crsouza.com/2010/05/12/harris-corners-detector-in-c/ Cobalah program yang dipaparkan dalam artikel tersebut Program memerlukan Accord.Net framework MATLAB Akses ke https://www.mathworks.com/help/vision/ref/detectharrisfeatures.html Pelajari dan cobalah kode yang ada. Gunakan objek yang berbeda File hasil dikirim ke email