CS4487/9587 Homework Assignment #2

Color+Boundary segmentation: graph cuts

Overview

BJ results
Figure 1: Graph cut segmentation
This homework covers standard binary (object/backgroud) segmentation methods using s/t graph cut or max-flow algorithms. In general, energies combining unary data (e.g. color) terms and pair-wise boundary regularization terms (see below) are common in many computer vision problems. Such energies integrate different cues in a coherent fashion.

Similarly to the first assignemnt, you can start from an EZi-based Graph cut project implementing an interface that allows to load images, enter seeds or boxes, play with parameters, switch between EDGE, COLOR_F, and COLOR_E segmentation modes, and save your results as images. EDGE mode is binary segmentation based on seeds (hard constraints) and edge alignemnt cues (see Topic 6 slide 5). It is fully implemented using an included max-flow library. COLOR_F is a mode with additional data term using fixed color models/histograms (slides 43-49), as in energy

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Mode COLOR_E estimates color histograms treating them as optimization variables jointly with segmnetation S (slides 70-74), as in "GrabCut". These two COLOR modes should be implemented. You can work with function compute_mincut in file Graph2D.cpp. You can also use MATLAB or other software, as long as you write your own code constructing and manipulating the graphs. MATLAB wrapper for the same max-flow library can be found here (see Max-flow/min-cut).

Specific Goals

What to submit

Submit a pdf file with your report (no more than 1.5 pages of text, but any number of images representing your results) summarizing your efforts in achieving the goals described above. In general, images of your resulst are highly encourages as they are an ideal way to represent your experiemnts in image analysis. The report should be submitted electronically via OWL. You should also submit your code (in a .zip file). The project report and code should be your independent effort.