Computational Vision and Bio Inspired Computing

Lecture Notes in Computational Vision and Biomechanics

Springer
Free sample

This is the proceedings of the International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC 2017) held at RVS Technical Campus, September 21-22, 2017. It includes papers on state of the art innovations in bio-inspired computing applications, where new algorithms and results are produced and described. Additionally, this volume addresses evolutionary computation paradigms, artificial neural networks and biocomputing. It focuses mainly on research based on visual interference on the basis of biological images. Computation of data sources also plays a major role in routine day-to-day life for the purposes such as video transmission, wireless applications, fingerprint recognition and processing, big data intelligence, automation, human centric recognition systems. With the advantage of processing bio-inspired computations, a variety of computational paradigms can be processed. Finally, this book also treats the formation of neural networks by enabling local connectivity within it with the aid of vision sensing elements.

The work also provides potential directions for future research.


Read more
Collapse

About the author

Dr . D. Jude Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. His research areas include Computational Intelligence and Image processing. He has authored more than 100 research papers in reputed International Journals. He has authored 1 book with (VDM-Verlag, Germany) and many book chapters with reputed publishers such as Springer and Inderscience. He has been serving as Associate editor of IEEE Access Journal. He is also the guest editor of many journals with Springer, Inderscience, IOS Press, etc. He has been also the organizing committee member of several international conferences across the globe. He holds professional membership with IEEE Technical Committee on Neural Networks (IEEE Computational Intelligence Society) and IEEE Technical Committee on Soft Computing (IEEE Systems, Man and Cybernatics Society). He has completed 1 funded research project from CSIR, Govt. of India. He also serves as the “Research Scientist” of Computational Intelligence and Information Systems (CI2S) Lab, Argentina and RIADI Lab, Tunisia. Currently, he is working as Associate Professor in Department of ECE, Karunya University, Coimbatore, India.

Dr.S.Smys received his M.E and Ph.D degrees all in Wireless Communication and Networking from Anna University and Karunya University, India. His main area of research activity is localization and routing architecture in wireless networks. He is served as a reviewer for IET, Springer, Inderscience and Elsevier journals. He has published many research articles in refereed journals and IEEE conferences. He has been the General chair, Session Chair, TPC Chair and Panelist in several conferences. He is member of IEEE and senior member of IACSIT wireless research group. He serves as Associate Editor of Computers and Electrical Engineering (C&EE) journal, Elsevier and Guest Editor of Telecommunication system journal, Springer. He has been serving as Organizing Chair and Program Chair of several International conferences, and in the Program Committees of several International conferences. Currently he is working as a professor in the Department of Computer Science and Engineering at RVS Technical Campus, Coimbatore, India.

Read more
Collapse
Loading…

Additional Information

Publisher
Springer
Read more
Collapse
Published on
Feb 19, 2018
Read more
Collapse
Pages
1143
Read more
Collapse
ISBN
9783319717678
Read more
Collapse
Read more
Collapse
Read more
Collapse
Language
English
Read more
Collapse
Genres
Computers / Computer Vision & Pattern Recognition
Medical / Allied Health Services / Medical Technology
Technology & Engineering / Biomedical
Technology & Engineering / Electrical
Technology & Engineering / Electronics / General
Technology & Engineering / Imaging Systems
Read more
Collapse
Content protection
This content is DRM protected.
Read more
Collapse

Reading information

Smartphones and Tablets

Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.

Laptops and Computers

You can read books purchased on Google Play using your computer's web browser.

eReaders and other devices

To read on e-ink devices like the Sony eReader or Barnes & Noble Nook, you'll need to download a file and transfer it to your device. Please follow the detailed Help center instructions to transfer the files to supported eReaders.
As a new interdisciplinary research area, “image-based geometric modeling and mesh generation” integrates image processing, geometric modeling and mesh generation with finite element method (FEM) to solve problems in computational biomedicine, materials sciences and engineering. It is well known that FEM is currently well-developed and efficient, but mesh generation for complex geometries (e.g., the human body) still takes about 80% of the total analysis time and is the major obstacle to reduce the total computation time. It is mainly because none of the traditional approaches is sufficient to effectively construct finite element meshes for arbitrarily complicated domains, and generally a great deal of manual interaction is involved in mesh generation.

This contributed volume, the first for such an interdisciplinary topic, collects the latest research by experts in this area. These papers cover a broad range of topics, including medical imaging, image alignment and segmentation, image-to-mesh conversion, quality improvement, mesh warping, heterogeneous materials, biomodelcular modeling and simulation, as well as medical and engineering applications.

This contributed volume, the first for such an interdisciplinary topic, collects the latest research by experts in this area. These papers cover a broad range of topics, including medical imaging, image alignment and segmentation, image-to-mesh conversion, quality improvement, mesh warping, heterogeneous materials, biomodelcular modeling and simulation, as well as medical and engineering applications.

This contributed volume, the first for such an interdisciplinary topic, collects the latest research by experts in this area. These papers cover a broad range of topics, including medical imaging, image alignment and segmentation, image-to-mesh conversion, quality improvement, mesh warping, heterogeneous materials, biomodelcular modeling and simulation, as well as medical and engineering applications.

This contributed volume, the first for such an interdisciplinary topic, collects the latest research by experts in this area. These papers cover a broad range of topics, including medical imaging, image alignment and segmentation, image-to-mesh conversion, quality improvement, mesh warping, heterogeneous materials, biomodelcular modeling and simulation, as well as medical and engineering applications.

This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence and Signal Processing was one of the few unique events that are focused on the convergence of the two fields. The book is comprised of chapters based on the top presentations at the workshop. This book has three chapters on various topics of biometrics – two are on face detection and one on iris recognition; all from top researchers in their field. There are four chapters on different biomedical signal / image processing problems. Two of these are on retinal vessel classification and extraction; one on biomedical signal acquisition and the fourth one on region detection. There are three chapters on data analysis – a topic gaining immense popularity in industry and academia. One of these shows a novel use of compressed sensing in missing sales data interpolation. Another chapter is on spam detection and the third one is on simple one-shot movie rating prediction. Four other chapters cover various cutting edge miscellaneous topics on character recognition, software effort prediction, speech recognition and non-linear sparse recovery. The contents of this book will prove useful to researchers, professionals and students in the domains of machine learning and signal processing.
This book concerns a new method of image data compression which weil may supplant the well-established block-transfonn methods that have been state-of-the art for the last 15 years. Subband image coding or SBC was first perfonned as such in 1985, and as the results became known at first through conference proceedings, and later through journal papers, the research community became excited about both the theoretical and practical aspects of this new approach. This excitement is continuing today, with many major research laboratories and research universities around the world investigating the subband approach to coding of color images, high resolution images, video- including video conferencing and advanced tele vision, and the medical application of picture archiving systems. Much of the fruits of this work is summarized in the eight chapters of this book which were written by leading practitioners in this field. The subband approach to image coding starts by passing the image through a two- or three-dimensional filter bank. The two-dimensional (2-D) case usually is hierarchical' consisting of two stages of four filters each. Thus the original image is split into 16 subband images, with each one decimated or subsampled by 4x4, resulting in a data conservation. The individual channel data is then quantized ·for digital transmission. In an attractive variation an octave-like approach, herein tenned subband pyramid, is taken for the decomposition resulting in a total of just eleven subbands.
This book gathers papers presented at the VipIMAGE 2017-VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing. It highlights invited lecturers and full papers presented at the conference, which was held in Porto, Portugal, on October 18–20, 2017. These international contributions provide comprehensive coverage on the state-of-the-art in the following fields: 3D Vision, Computational Bio-Imaging and Visualization, Computational Vision, Computer Aided Diagnosis, Surgery, Therapy and Treatment, Data Interpolation, Registration, Acquisition and Compression, Industrial Inspection, Image Enhancement, Image Processing and Analysis, Image Segmentation, Medical Imaging, Medical Rehabilitation, Physics of Medical Imaging, Shape Reconstruction, Signal Processing, Simulation and Modelling, Software Development for Image Processing and Analysis, Telemedicine Systems and their Applications, Tracking and Analysis of Movement, and Deformation and Virtual Reality.

In addition, it explores a broad range of related techniques, methods and applications, including: trainable filters, bilateral filtering, statistical, geometrical and physical modelling, fuzzy morphology, region growing, grabcut, variational methods, snakes, the level set method, finite element method, wavelet transform, multi-objective optimization, scale invariant feature transform, Laws’ texture-energy measures, expectation maximization, the Markov random fields bootstrap, feature extraction and classification, support vector machines, random forests, decision trees, deep learning, and stereo vision.

Given its breadth of coverage, the book offers a valuable resource for academics, researchers and professionals in Biomechanics, Biomedical Engineering, Computational Vision (image processing and analysis), Computer Sciences, Computational Mechanics, Signal Processing, Medicine and Rehabilitation.

©2020 GoogleSite Terms of ServicePrivacyDevelopersArtistsAbout Google|Location: United StatesLanguage: English (United States)
By purchasing this item, you are transacting with Google Payments and agreeing to the Google Payments Terms of Service and Privacy Notice.