How can the results of your doctoral research be utilised in practice?
Modern machine-vision methods have higher-level goals like extracting an anatomical object of interest for diagnosis of diseases, scene classification, detection of human activity in visual surveillance, assigning a name to a human face in an image, and classifying handwritten characters. A crucial step in the design of such machine-vision systems is the extraction of discriminant objects from the images. Image segmentation also has an extensive use in robotics where robots equipped with camera do tasks like assembly of parts, drive vehicles, clean homes, detect and perform surgeries, and many more. The approach we have proposed can be applied to obtain a low-level segmentation based on color features, which can be further fed to these machine-vision systems.
What are the key research methods and materials used in your doctoral research?
To optimize the Mumford-Shah model, we propose three different approaches using (1) Douglas-Rachford algorithm, (2) k-means clustering, (3) PNN clustering algorithm. To apply the similarity criterion of the Mumford-Shah model, we use methods which group the pixels into the same segment based on their RGB values. These methods are known as clustering methods. For the second criterion of the Mumford-Shah model, we develop a new and easy method for finding the boundary length of an individual pixel and in turn the boundary length of the segment. Our first method obtains initial segmentation using one of the clustering methods, k-means. The initial segmentation is then optimized using the Douglas-Rachford algorithm. This approach is somewhat complicated and slightly slow when compared to other methods. Our second method is a variant of the k-means clustering method. The k-means method obtains the grouping using only the pixel values. Here, while grouping we also consider whether including the pixel in a group increases or decreases the boundary length. Our third method is a variant of another clustering method, pairwise nearest neighbour. This method finds similar areas by merging a pair of segments, starting with a segment as small as a single pixel. The pair to be merged is the one which is the most similar in terms of pixel values among all the pairs. In our method, while finding the pair to be merged, we also consider the boundary length. The increase in boundary length of the merged pair should also be minimum. The methods were tested using a dataset containing 200 random images with labeled objects. The first two methods are dependent on initial grouping. Our third method is slower but achieves better segmentation. It does not depend on initial grouping and gives segmentation with different number of partitions.