IEEE
August 17th, 2018
IEEE Signal Processing Society, Bangalore Chapter,
and Department of Computational and Data Sciences, Indian Institute of Science
Invite you to the following talk:
Title: Deep Convolutional Neural Network in Video Analytics for Assistive Healthcare Technologies.
by Dr. Bappaditya Mandal, Lecturer (Computing), Keele University, UK
Time & Date:  11:00 AM, Wednesday, August 22, 2018 
Venue: CDS Seminar Hall (Room No: 102), IISc.
Abstract:
Because of the fast advancement and price reduction in the hardware and computing facilities, deep convolutional neural network (DCNN) for video analytics has been computationally possible in practise and have shown considerable improvement in many video analytics tasks, such as object recognition, face recognition, etc. My talk will cover two aspects of my research: (1) Computer Vision on Wearable devices and (2) DCNN for two Biomedical Applications: Melanoma Skin Cancer Detections and Optic Disc and Cup Segmentation for Glaucoma Assessment. In the first part of my talk, I will talk about the development of computational methodologies in wearable devices that would help people to improve their lives. For example, camera in the wearable devices (such as Google Glass, GoPro) generates first-person-view (FPV) or egocentric videos that show near human vision field of view. They provide immerse opportunities for various applications, such as face recognition for social interaction assistance. Life-logged egocentric data are useful for summarization and retrieval (memory assistance), security, health monitoring, lifestyle analysis to memory rehabilitation (i.e., subject matters being remembered, such as time, place, object, people, context, and mental states) for dementia patients.
In the second portion of my talk I will discuss how we have improved the deep residual network with regularized Fisher framework for differentiating melanoma (malignant) from non-melanoma (benign) skin cancer cases, which is supported by large number of experimental results from benchmark databases. I will conclude my talk on how we have modified deep residual learning framework to extract more patch based discriminating features by improving the information flow in the network by introducing extra skip connections for the challenging optic disk and optic cup segmentation for glaucoma assessment.
Speaker Bio:
Bappaditya Mandal has received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology (IIT), Roorkee, India and the Ph.D. degree in Electrical and Electronic Engineering from Nanyang Technological University (NTU), Singapore, in 2003 and 2008, respectively. His research interest are in the areas of computer vision, machine learning, pattern recognition and video analytics. Bappaditya has worked as a Scientist for >9 years at the Cognitive Vision Lab, Visual Computing Department in the Institute for Infocomm Research, A*STAR, Singapore, between May 2008 to June 2017 for a number of research projects and published extensively in Journals, conferences and workshops. He has been in the Kingston University London for a short while before joining as a Lecturer in Computer Science, School of Computing and Mathematics at Keele University, United Kingdom in March 2018.
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May 11th, 2018

 

Short Course on Adaptive Filters by Prof. V. John Mathews and Prof. K.V.S. HARI

28 th May – 29 th May, 2018

 

IMPORTANT DATES

Last date for receipt of completed application:
23 rd May, 2018
Confirmation for participants through e-mail:
26 th May, 2018
Workshop starts with registration on
28 th May, 9:00AM

 

Details of this Course can be found here

 

 

 

April 20th, 2018

IEEE Signal Processing Society, Bangalore Chapter,
and Department of Computational and Data Sciences, Indian Institute of Science

Invite you to the following talk:

Title: Powering fashion e-commerce with computer vision and deep learning

by Vishnu Vardhan Makkapati, Architect, Myntra,

Time & Date: 11:00 AM, Wednesday, April 25, 2018
Venue: CDS Seminar Hall (Room No: 102), IISc.

Abstract:

Fashion is a highly visual field. Images, though a rich source of domain information, are extremely subjective, and their interpretation is more art than science. Our goal is to teach a machine to interpret these varied images in a consistent manner, while eliminating subjectivity from the process. We have used our industry leading fashion catalog to understand and interpret the inherent fine-grained details in images. These details will help power very interesting use cases in fashion e-commerce such as cataloging, purchasing, personalisation etc. In this talk, we will present an overview of our work on mining catalog images using deep learning and computer vision. We will also discuss some of our recent work on generation of fashion designs using Generative Adversarial Networks.

Speaker Bio:
Vishnu Vardhan Makkapati received the B.E. (Honors) degree in electrical and electronics and the M.Sc. (Honors) degree in mathematics from the Birla Institute of Technology and Science, Pilani, India, in 2000, and the M.Sc. (Engg.) degree from the Indian Institute of Science, Bangalore, in 2007. He was with the IBM India Software Laboratory until April 2001 and, then, with the Honeywell Technology Solutions Laboratory, India, until December 2006, reaching the level of Principal Engineer. He was a Senior Scientist with Philips Research India until July 2015, where he most recently led the efforts on camera based vital signs. He is currently an Architect with Myntra. He holds six US patents with many others pending. He is a senior member of the IEEE.

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April 20th, 2018

Dept of ECE, Indian Institute of Science, Bangalore
&
IEEE Signal Processing Society, Bangalore Chapter

have arranged the following technical lecture; All are welcome.

Speaker: Dr. Madhuri Gore

Title: “Hearing Assessment Technologies (Audiometry)
and some new challenges”
Date/Time: 26th April 2018, Thursday, 4-5pm
Venue: Golden Jubilee Seminar Hall, ECE Dept, IISc
Tea/Coffee: 3.45pm

Abstract:
The auditory system has unique abilities that enable to person to carry out tasks such as listening in noise, detecting differences in sound object, localizing sounds, music appreciation and learning languages, activities that are deceptively simple. Hearing assessment (common parlance referred to as audiometry) refers to behavioural and physiological tests to assess function and sometimes “structure” of the different components of the auditory pathway. For this, a number of stimuli varying from the simple pure tones to synthetic speech which are presented in different modes, at different SNRs, in an attempt to simulate real life conditions, but more often to diagnose the problem. Creating appropriate stimuli and calibration is an issue.

Speaker Bio:
Dr. Madhuri Gore is Principal and Deputy Director (T) of “Dr.S.R.Chandrasekhar Institute of Speech and Hearing,” Bangalore. She has been in academics since 1993. She has vast clinical experience in hearing disorders since 1982. Her special clinical interest in Cochlear Implants, Auditory Plasticity, Auditory Neuropathy, Spectrum Disorder; also interested in Diagnostic Audiology, Psychophysics and Speech Perception.

April 12th, 2018

Dear All,

IEEE Signal Processing Society, Bangalore Chapter,
and Department of Computational and Data Sciences, Indian Institute of Science

Invite you to the following talk:

Title: Image-based Crowd Analytics
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by Vishwanath A. Sindagi, Rutgers University,

Time & Date: 11:00 AM, Thursday, April 19, 2018
Venue: CDS Seminar Hall (Room No: 102), IISc.

Abstract:

The study of human behavior based on computer vision techniques has gained a lot of interest in recent years. In particular, the behavioral analysis of crowded scenes is of great interest due to a variety of reasons. Exponential growth in the world population and the resulting urbanization has led to an increased number of activities involving high density crowd such as sporting events, political rallies, public demonstrations, thereby resulting in more frequent crowd gatherings in the recent years. In such scenarios, it is essential to analyze crowd behavior for better management, intelligence gathering, safety and security. In this talk, I will present some of my recent work on developing algorithms for crowd analytics, including crowd counting from unconstrained imagery, crowd segmentation and human detection from crowded scenes. I will conclude my talk by describing several promising directions for future research.

Bio:

Vishwanath is a second year PhD student in Dept. Of Electrical & Computer Engineering at Rutgers University. He is being advised by Prof. Vishal M Patel. Prior to joining Rutgers, he worked for Samsung R&D Bangalore and AllGo Embedded Systems. He graduated from IIIT-Bangalore with a Master’s degree in Information Technology. His current research is on computer vision and machine learning with a specific focus on crowd analytics, face detection, applications of generative modeling, domain adaptation and low-level vision.

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March 30th, 2018

The IEEE Signal Processing Society Bangalore Chapter and
Department of Electrical Engineering, Indian Institute of Science, Bangalore

invite you to a lecture by

Dr. Mathew Magimai Doss from Idiap, Martigny and EPFL, Switzerland

on the following topic:

“Towards Phonologically Motivated Sign Language Processing”

Abstract:
Sign language is a mode of communication commonly employed by the Deaf community to communicate with each other as well as to communicate with the Hearing community. SMILE is a Swiss NSF funded Sinergia project involving sign language technologists and sign linguists that aims to develop a sign language learning system for Swiss German sign language (DSGS). In this talk, I will present recent developments in the SMILE project. More specifically, I will present (a) development of SMILE DSGS dataset and (b) development of a phonologically motivated sign language assessment approach using hidden Markov models and artificial neural networks, akin to articulatory feature-based speech processing.

Venue: Multimedia Classroom, Department of Electrical Engineering, IISc

Time: 4 PM, April 3, 2018 (Coffee will be served at 3.45 PM)

Speaker Biography:
Dr. Mathew Magimai Doss received the Bachelor of Engineering (B.E.) in Instrumentation and Control Engineering from the University of Madras, India in 1996; the Master of Science (M.S.) by Research in Computer Science and Engineering from the Indian Institute of Technology, Madras, India in 1999; the PreDoctoral diploma and the Docteur dès Sciences (Ph.D.) from Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland in 2000 and 2005, respectively. He was a postdoctoral fellow at International Computer Science Institute (ICSI), Berkeley, the USA from April 2006 until March 2007. Since April 2007, he has been working as a permanent researcher in the Speech and Audio Processing group at Idiap Research Institute, Martigny, Switzerland. He is also a lecturer at EPFL. He is a senior area editor of the IEEE Signal Processing Letters. His main research interest lies in signal processing, statistical pattern recognition, artificial neural networks and computational linguistics with applications to speech and audio processing and multimodal signal processing.

February 18th, 2018

IEEE Signal Processing Society, Bangalore Chapter, IEEE Bangalore Section,
and
Department of Electrical Engineering, Indian Institute of Science

invite you to a seminar by

Prof. Rama Chellappa
Distinguished University Professor,
University of Maryland, College Park

Date: 22 February, 2018, Thursday,
Time: 4:00 pm (coffee will be served at 3:45pm)

Venue: Multimedia Classroom, Department of Electrical Engineering, IISc

Title: Deep Representations, Adversarial Learning and Domain Adaptation for Some Computer Vision Problems

Abstract:
Recent developments in deep representation-based methods for many computer vision problems have knocked down many research themes pursued over the last four decades. In this talk, I will discuss methods based on deep representations, adversarial learning and domain adaptation for designing robust computer vision systems with applications in unconstrained face and action verification and recognition, expression recognition, subject clustering and attribute extraction. The face and action recognition system being built at UMD is based on fusing multiple deep convolutional neural networks (DCNNs) trained using publicly available still
and video face data sets. I will then discuss some new results on generative adversarial learning and domain adaptation for improving the robustness of the recognition system.

Biography: Prof. Rama Chellappa is a Distinguished University Professor, a Minta Martin Professor of Engineering and Chair of the ECE department at the University of Maryland. His current research interests span many areas in image processing, computer vision, machine learning and pattern recognition. Prof. Chellappa is a recipient of an NSF Presidential Young Investigator Award and four IBM Faculty Development Awards. He received the K.S. Fu Prize from the International Association of Pattern Recognition (IAPR). He is a recipient of the Society, Technical Achievement and Meritorious Service Awards from the IEEE Signal Processing Society. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. Recently, he received the inaugural Leadership Award from the IEEE Biometrics Council. At UMD, he received college and university level recognitions for research, teaching, innovation and mentoring of undergraduate students. In 2010, he was recognized as an Outstanding ECE by Purdue University. He received the Distinguished Alumni Award from the Indian Institute of Science in 2016. Prof. Chellappa served as the Editor-in-Chief of PAMI. He is a Golden Core Member of the IEEE Computer Society, served as a Distinguished Lecturer of the IEEE Signal Processing Society and as the President of IEEE Biometrics Council. He is a Fellow of IEEE, IAPR, OSA, AAAS, ACM and AAAI and holds six patents.

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Co-sponsor:
IEEE Signal Processing Society,
Bangalore Chapter
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January 4th, 2018

The IEEE Signal Processing Society Bangalore Chapter

invite you to a talk on

Audio and Acoustics Signal Processing: the Quest for High Fidelity Continues

by
Prof. Dr.-Ing. Dr. rer. nat. h.c. mult. Karlheinz Brandenburg
Head of the electronic media technolgy lab
Director of the Fraunhofer Insitute for Digital Media Technology (IDMT)

Date and time: January 9, 2018; 9:30 AM

Venue: EC 1.06, Department of Electrical Communication Engineering, Indian Institute of Science (IISc).

Abstract:
The dream of high fidelity continues since more than 100 years. In the last decades, signal processing has contributed many new solutions and a vast amount of additional knowledge to this field. This includes mp3 and other audio coding algorithms which changed the way we listen to music.

Current research includes the following areas:
– Music Information Retrieval (MIR), helping us to to find music or even learn playing instruments
– Immersive technologies to really get the best illusion into movie theaters and eventually our homes
– Learning more about hearing, how the ear and the brain work together when we listen to sounds
The talk will explain the basics and touch on some current research topics in these fields.

Biography of the speaker:
Karlheinz Brandenburg is the inventor of MP3 (https://www.tu-ilmenau.de/en/electronic-media-technology/team/brandenburg-karlheinz/).

December 18th, 2017

Dear All:

IEEE Signal Processing Society, Bangalore Chapter, IEEE Bangalore Section,
and Dept. of ECE, IISc cordially invite you to the following talk:

Title: Beyond Worst Case – When data size increases faster than Moore’s law, one has to sacrifice worst-case performance to improve the average-case performance.
Speaker: Vivek Bagaria, Stanford University, USA
Time and Date: 4pm, Thursday, Dec. 21, 2017
Venue: Golden Jubilee Hall, ECE Department, IISc Bangalore

Abstract:

High dimensional median: Computing the medoid of a large number of points in high-dimensional space is an increasingly common operation in many data science problems. We present an algorithm Med-dit which computes the medoid (with high probability) in O(n log n) steps. Med-dit is based on a connection with the multi-armed bandit problem. We empirically evaluate the performance of Med-dit on the Netflix-prize and the single-cell RNA datasets, containing hundreds of thousands of points living in tens of thousands of dimensions, and observe a 5-10x improvement in performance over the current state of the art.

Planted Traveling salesman problem (TSP): Consider a complete graph K_n. The edge weights of a planted hamiltonian path are drawn from distribution P and all other edge weights are drawn from distribution Q. We present an almost-linear time algorithm which solves planted-TSP above information theoretic limit (for certain class of distributions P and Q such as gaussian, poisson etc). We apply this algorithm on Chicago-seq dataset and show a potential improvement in the quality of de novo dna assembly.

Bio:

Vivek Bagaria is a doctoral student at Stanford University, advised by Prof. David Tse. His google scholar profile can be found here: https://scholar.google.com/citations?user=0DD8EREAAAAJ&hl=en

December 16th, 2017

Dear All,

IEEE Signal Processing Society, Bangalore Chapter, IEEE Bangalore Section,
and Department of Computational and Data Sciences, Indian Institute of Science

Invite you to the following talk:

Title: Connecting vision and language for Interpretation, Grounding and Imagination
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by Ramakrishna Vedantam, Georgia Tech,

Time & Date: 4:00 pm, Friday, December 22, 2017
Venue: CDS Seminar Hall (Room No: 102), IISc.
Abstract:

The goal of this talk is to explore how modeling deep interactions between vision and natural language can derive more humanlike inferences from machine learning models. We will consider various situations where humans are able to make intuitive inferences, but where machines are not, and show how appropriate algorithmic or computational choices can improve the inferences made by machines and make them more humanlike.

In pursuit of this overarching goal, I will look at three focus areas: interpretation, grounding, and visual imagination. In interpretation, I will study how to go from computer vision to natural language. Specifically, the focus will be on image captioning: the problem of describing an image with a natural language description. Here, I study how to formalize the task of image captioning and create evaluation schemes to make progress towards generating more `human-like’ descriptions and generate image captions which are more aware of the context in which we want to describe an image.

In the second part, I will consider the inverse problem of “grounding” — associating symbols such as “car” with what they refer to in the physical world. A key focus will be on learning word representations grounded in vision or sound, and a study of how such representations can lead to improved retrieval and commonsense reasoning.

Finally, I will talk about recent work on the problem of imagination — consider how easy it is for people to imagine what a “purple hippo” would look like, even though they do not exist. If we instead said “purple hippo with wings”, they
could just as easily create a different internal mental representation, to represent this more specific concept. To assess whether the person has correctly understood the concept, we can ask them to draw a few sketches,
to illustrate their thoughts. We call the ability to map text descriptions of concepts to latent representations and then to images visually grounded semantic imagination. My work will explore how one can modify a class of joint latent variational autoencoder models to perform such grounded semantic imagination.

Bio:

Ramakrishna Vedantam is a PhD. student in the school of Interactive Computing at the College of Computing at Georgia Tech since 2017. His advisor is Devi Parikh. Before moving to GT, he was a PhD. student at the Bradley Department of ECE at Virginia Tech.

During his Ph.D. he has spent time at Facebook AI Research (Summer, 2017), Google Research (Winter, 2017, and Summer,2016) and INRIA-Saclay (Summer, 2014). He received his M.S. degree in ECE from Virginia Tech in 2016, and his bachelors in ECE from the International Institute of Information Technology (IIIT) – Hyderabad in 2013.

He is interested in problems at the intersection of vision and language, as well as generative models and variational
inference. In the past he has worked on topics like evaluation and generation of image captions, grounding language into visual cues, etc. He was the recipient of the Outstanding Reviewer Award at CVPR, 2017, and a finalist for the Adobe Research Fellowship in 2016.

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