Machine Intelligence and Signal Processing

  • Richa Singh
  • Mayank Vatsa
  • Angshul Majumdar
  • Ajay Kumar

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 390)

Table of contents

  1. Front Matter
    Pages i-x
  2. Rahul Thota, Sharan Vaswani, Amit Kale, Nagavijayalakshmi Vydyanathan
    Pages 11-26
  3. S. Spoorthy, Sandhyasree Thaskani, Adithya Sood, M. Girish Chandra, P. Balamuralidhar
    Pages 27-35
  4. Vidit Jain
    Pages 37-54
  5. Arpita Gang, Janki Mehta, Angshul Majumdar
    Pages 67-76
  6. D. Relan, L. Ballerini, E. Trucco, T. MacGillivray
    Pages 77-84
  7. Gaurav Goswami, Richa Singh, Mayank Vatsa
    Pages 85-98
  8. Kavya Gupta, Angshul Majumdar
    Pages 99-108
  9. Shrey Jairath, Samarth Bharadwaj, Mayank Vatsa, Richa Singh
    Pages 131-142
  10. Back Matter
    Pages 163-163

About these proceedings

Introduction

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.

Keywords

Biomedical Signal Processing Biometrics Compressed Sensing Data Analytics Intelligent Signal Processing MISP Pattern Recognition

Editors and affiliations

  • Richa Singh
    • 1
  • Mayank Vatsa
    • 2
  • Angshul Majumdar
    • 3
  • Ajay Kumar
    • 4
  1. 1.Computer ScienceIndraprastha Inst of Info. TechNew DelhiIndia
  2. 2.Computer ScienceIndraprastha Inst of Info. TechnoloyNew DelhiIndia
  3. 3.Computer ScienceIndraprastha Inst of Info Tech.New DelhiIndia
  4. 4.Department of ComputingHong Kong Polytechnic UniversityHong KongChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-81-322-2625-3
  • Copyright Information Springer India 2016
  • Publisher Name Springer, New Delhi
  • eBook Packages Engineering
  • Print ISBN 978-81-322-2624-6
  • Online ISBN 978-81-322-2625-3
  • Series Print ISSN 2194-5357
  • Series Online ISSN 2194-5365
  • About this book