Machine Intelligence and Signal Processing

✍ Scribed by Richa Singh, Mayank Vatsa, Angshul Majumdar, Ajay Kumar (eds.)

• Publisher: Springer India
• Year: 2016
• Tongue: English
• Leaves: 169
• Series: Advances in Intelligent Systems and Computing 390
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
Advancing Cross-Spectral Iris Recognition Research Using Bi-Spectral Imaging....Pages 1-10
Fast 3D Salient Region Detection in Medical Images Using GPUs....Pages 11-26
Missing Data Interpolation Using Compressive Sensing: An Application for Sales Data Gathering....Pages 27-35
Domain Adaptation for Face Detection....Pages 37-54
Genetically Modified Logistic Regression with Radial Basis Function for Robust Software Effort Prediction....Pages 55-66
Recovering Partially Sampled EEG Signals Using Learned Dictionaries....Pages 67-76
Retinal Vessel Classification Based on Maximization of Squared-Loss Mutual Information ....Pages 77-84
Automated Spam Detection in Short Text Messages....Pages 85-98
Greedy Algorithms for Non-linear Sparse Recovery....Pages 99-108
Reducing Inter-Scanner Variability in Multi-site fMRI Activations Using Correction Functions: A Preliminary Study....Pages 109-117
Comparative Study of Preprocessing and Classification Methods in Character Recognition of Natural Scene Images....Pages 119-129
Adaptive Skin Color Model to Improve Video Face Detection....Pages 131-142
Improving Rating Predictions by Baseline Estimation and Single Pass Low-Rank Approximation....Pages 143-151
Retinal Blood Vessel Extraction and Optic Disc Removal Using Curvelet Transform and Morphological Operation....Pages 153-161
Back Matter....Pages 163-163

✦ Subjects

Computational Intelligence; Signal, Image and Speech Processing; Computer Imaging, Vision, Pattern Recognition and Graphics