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Image and Video Technology
This book constitutes the conference proceedings of the 9th Pacific Rim Symposium on Image and Video Technology, PSIVT 2019, held in Sydney, NSW, Australia, in November 2019. A total of 31 papers were carefully reviewed and selected from 55 submissions. The main conference comprises 11 major subject areas that span the field of image and video technology, namely imaging and graphics hardware and visualization, image/video coding and transmission, image/video processing and analysis, image/video retrieval and scene understanding, applications of image and video technology, biomedical image processing and analysis, biometrics and image forensics, computational photography and arts, computer and robot vision, pattern recognition, and video surveillance.
Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes
Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.
Advances in Multimedia Information Processing - PCM 2004
Welcome to the proceedings of the 5th Paci?c Rim Conference on Multimedia (PCM 2004) held in Tokyo Waterfront City, Japan, November 30–December 3, 2004. Following the success of the preceding conferences, PCM 2000 in Sydney, PCM 2001 in Beijing, PCM 2002 in Hsinchu, and PCM 2003 in Singapore, the ?fth PCM brought together the researchers, developers, practitioners, and educators in the ?eld of multimedia. Theoretical breakthroughs and practical systems were presented at this conference, thanks to the support of the IEEE Circuits and Systems Society, IEEE Region 10 and IEEE Japan Council, ACM SIGMM, IEICE and ITE. PCM2004featuredacomprehensiveprogramincludingkeynotetalks,regular paperpresen...
Smart Multimedia
This book constitutes the proceedings of the First International Conference on Smart Multimedia, ICSM 2018, which was held in Toulon, France, in August 2018. The 39 papers presented were selected from about 100 submissions and are grouped in sections on social, affective and cognition analysis, person-centered smart multimedia: serving people with disabilities to the general population, haptic and robots for smart multimedia applications, MR, 3D, underwater image processing, smart signal processing meets smart sensing, visual behavior analysis: methods and applications, video analysis, learning, low-level vision, miscellaneous.
Advances in Multimedia Modeling
The two volume set LNCS 4351 and LNCS 4352 constitutes the refereed proceedings of the 13th International Multimedia Modeling Conference, MMM 2007, held in Singapore in January 2007. Based on rigorous reviewing, the program committee selected 123 carefully revised full papers of the main technical sessions and 33 revised full papers of four special sessions from a total of 392 submissions for presentation in two volumes.
Deep Learning Models for Medical Imaging
Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. - Provides a step-by-step approach to develop deep learning models - Presents case studies showing end-to-end implementation (source codes: available upon request)
