1 / 9

IBMR Assignment 1

IBMR Assignment 1. Stitching Photo Mosaics. What is Photo Mosaic?. Stitching photos to construct a wild-view scene. Part1: CORNER DETECTION Part2: PERSPECTIVE MAPPING and MOSAICING Handout after Part2 Finished. CORNER DETECTION. Requirement.

marva
Download Presentation

IBMR Assignment 1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. IBMR Assignment 1 Stitching Photo Mosaics

  2. What is Photo Mosaic? • Stitching photos to construct a wild-view scene. • Part1: CORNER DETECTION • Part2: PERSPECTIVE MAPPINGand MOSAICING • Handout after Part2 Finished

  3. CORNER DETECTION

  4. Requirement • Read an image, detect its all corner by HARRIS CORNER DETECTION • (bonus) Invariant or robust features on scale, orientation, illumination

  5. Support • A GDI+ Image Loader • You may use any other library(openCV, matlab, etc) except existing corner detection function.

  6. About GDI+ • Get color from Pixel(X,Y) in gbmpPicL • Color c; • gbmpPicL->getPixel(X, Y, &c); • int red = c.GetR(); • Set color at Pixel(X,Y) in gbmpPicL • gbmpPicL->setPixel(X, Y, Color(0,255,0)); //draw green at (X,Y)

  7. About Harris Detection • Preprocessing: Gaussian Filter / Gray level • Consider the matrix for a small square around (x,y) • Compute x and y derivatives : and • Compute • Find λ1 and λ2 by evaluation of eigenvalues or SVD or • Compute the response of the detector at each pixel: Positive: corner Negative: edge Small: flat k~0.04 to 0.06 + and

  8. Next Step • Show the response cited from http://www.ikaros-project.org/articles/2004/monoculardepth1/ • Non-Maxima suppression cited from http://ssip2003.info.uvt.ro/lectures/chetverikov/edge_detection.pdf

  9. Result – Try your own photo!

More Related