Iterative Hillclimbing Optimization Techniques

This book is a reproduction of my thesis work. An iterative hillclimbing optimization technique was introduced in this thesis. It was used to tackle many index assignment problems, i.e. transform image encoding, noisy image decoding, and image segmentation. We introduced an iterative algorithm which has a hillclimbing property on the cost function. We then extended the algorithm to hyperspectral image coding. We realized that the algorithm can be generalized to other applications as well. We applied the iterative hillclimbing idea to noisy channel image decoding. We also investigated a Turbo-like joint source channel decoding technique, which is another kind of iterative decoding. Lastly, we re-investigated the image segmentation application, using the iterative hillclimbing algorithm. The hillclimbing method inspired development of another iterative algorithm which is an extension to mean-field annealing, with applications both to image decoding and image segmentation. The hillclimbing algorithm at the heart of this thesis thus yielded several promising offshoot directions for continuing research.

Autorentext

Piya Bunyaratavej holds a Ph.D. in Electrical Engineering from the Pennsylvania State University. He has been involved in digital signal processing and communications industry for over 10 years. He is currently working at Ericsson (Thailand). He is also a lecturer for several universities in Thailand.