Special Issue on Advances on Intelligent Multimedia Processing
As computer and sensor technology advances make increasing amounts of multimedia data available, multimedia processing methodologies are needed for multimodal data fusion, efficient data processing, information extraction, and added-value data generation.
This special issue introduces the following latest practical developments in image processing, acoustical signal processing, pattern recognition, data mining, and visualization:
S. Kobashi et al. propose a robust algorithm for reconstructing total knee arthroplasty implants from X-ray cone-beam images. Y. Hatakeyama et al. propose an algorithm for classifying ultrasonic abdominal images with the help of reports in text format. T. Miyazaki et al. analyze breathy and rough speech by the elderly. M. Nii et al. present a genetic algorithm for classifying nursing-care text. These papers provide useful insights into medical diagnosis and nursing care in the aging society coming to dominate 21st century.
T. Toyota et al. visualize Japanese law networks based on granular computing. K. Sawase et al. present a management system for large databases of tagged images. These graphical user interface techniques will be helpful to those who are not computer experts.
H. Kawano et al. propose a classification algorithm for segmenting range data into multiple quadric surfaces. Y. Arai et al. propose a nearest-neighbor method for personal authentication. H. Kawano et al. present an algorithm for extracting the structure of decorative characters based on graph spectral decomposition. These classification and discriminant algorithms provide a basis for multimedia data processing.
G. Tanaka et al. propose a color transfer algorithm. H. Orii et al. present an image completion algorithm. G. Tanaka et al. present an image enhancement algorithm to noisy images. M. Mizumachi et al. propose a stochastic algorithm for estimating sound source direction. These image and acoustical processing algorithms improve the quality of digital data and will provide new applications in these areas.
We thank the authors and referees whose invaluable work and kind comments have made this special issue possible and improved overall paper quality.