Image segmentation techniques via robust hypothesis testing

In this thesis, the multiple segmentation of color images is examined using the method based on robust hypothesis testing. The robust hypothesis test has been adapted to image segmentation by several different methods. In the first approach, to ease this NP-hard problem, we provide the user to choose certain areas over the image whose pixels are regarded as candidates from different labels. The suggested algorithm is started through this selection process, and the empirical histograms of the pixel densities are then employed as nominal densities in a reliable hypothesis test. By modifying the test framework to include soft metrics obtained over the multi-dimensional color image, we specifically apply the DGL test. In other ways, we included superpixels in our test. But just adding superpixels, the baseline method, does not take advantage of the spatial information of user input, and only works in the color domain. In addition to superpixels, we have applied the DGL test based on spatial-temporal distribution, where the spatial information of bounding boxes is included in the test for improved performance without increasing the complexity. We also provided simulations on Berkeley's BSDS500 image dataset and showed that the last method applied could result in a 10\% improvement in segmentation accuracy to the first method. The methods we develop can multiple segmentation color images with low complexity. This performance advantage has also been verified by simulations.