Abstract:

In image enhancement,analyzes two enhancement algorithms,i.e.,the spatial and spatiotemporal homomorphic filters (SHF and STHF) to enhance far infrared images based upon a far infrared imaging model,and proves theoretically and experimentally that the resulting images with SHF are in general smoother than those with STHF,although STHF may reduce the processing time greatly in comparison with SHF.Based on this conclusion,an adaptive spatiotemporal homomorphic filter (ASTHF) is proposed.With ASTHF,the resulting images are smoother than those with STHF,while the processing time is less than that with SHF for a similar degree of convergence.ASTHF keeps the advantages of both SHF and STHF,featuring both good quality and less processing time.In image segmentation,proposes an integrative segmentation framework-general scheme of region competition (GSRC) based on scale space.GSRC first automatically labels pixels whose corresponding regions can be determined in large likelihood,and then fine-tunes the final regions with the help of probability model,boundary smoothing,and region competition.Although the description of the scheme is non-parametric in the dissertation,GSRC can also work parametrically if all non-parametric procedures are substituted with the parametric counterparts.

Key words: image segmentation, nonparametric probability, region competition, scale-space-based classification, image, enhancement, adaptive filtering