Ching Yee Suen – författare

This book constitutes the refereed proceedings of the 11th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2024, held in Montreal, QC, Canada, during October 10–12, 2024.The 27 full papers presented together were carefully reviewed and selected from 46 submissions. The conference focuses on: learning algorithms and architectures; applications in medical and health sciences; applications in computer vision; applications in NLP, speech, and music; applications in environmental and biological sciences.

The fifth volume in this book series consists of a collection of new papers written by a diverse group of international scholars. Papers and presentations were carefully selected from 160 papers submitted to the International Conference on Pattern Recognition and Artificial Intelligence held in Montreal, Quebec (May 2018) and an associated free public lecture entitled "Artificial Intelligence and Pattern Recognition: Trendy Technologies in Our Modern Digital World". Chapters address topics such as the evolution of AI, natural language processing, off and on-line handwriting analysis, tracking and detection systems, neural networks, rating video games, computer-aided diagnosis, and digital learning.Within an increasingly digital world, 'artificial intelligence' is becoming a household term and a topic of great interest to many people worldwide. Pattern recognition, in using key features to classify data, has a strong relationship with artificial intelligence. This book not only complements other monographs in the series, it also provides the latest information. It is geared to promote interest and understanding about pattern recognition and artificial intelligence to the general public. It may also be of interest to graduate students and researchers in the field. Rather than focusing on one specific area, the book introduces readers to various basic concepts and to various potential areas where pattern recognition and artificial intelligence can be applied to make valuable contributions to other fields such as medicine, teaching and learning, forensic science, surveillance, online reviews, computer vision and object tracking.

Thinning is a technique widely used in the pre-processing stage of a pattern recognition system to compress data and to enhance feature extraction in the subsequent stage. It reduces a digitized pattern to a skeleton so that all resulting branches are 1 pixel thick. The method seems easy at first and has many advantages, however after two decades of intensive research, it has been found to be very challenging due to the difficulties in programming computers to do it.This collection of 15 papers by leading scientists working in the area examines the theoretical and experimental aspects of thinning methodologies. The authors have addressed the problems faced, compared their performance results with others, and assessed the challenges ahead. Researchers will find the volume helpful in shedding light on difficult issues and stimulating further research in the area.

Wavelet analysis and its applications have been one of the fastest growing research areas in the past several years. Wavelet theory has been employed in numerous fields and applications, such as signal and image processing, communication systems, biomedical imaging, radar, air acoustics, and many other areas. Active media technology is concerned with the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating, and delegating in a dynamic environment.This book captures the essence of the current state of the art in wavelet analysis and active media technology. It includes nine invited papers by distinguished researchers: P Zhang, T D Bui and C Y Suen from Concordia University, Canada; N A Strelkov and V L Dol'nikov from Yaroslavl State University, Russia; Chin-Chen Chang and Ching-Yun Chang from Taiwan; S S Pandey from R D University, India; and I L Bloshanskii from Moscow State Regional University, Russia.The proceedings have been selected for coverage in: