N V Kartheek Medathati

N V Kartheek Medathati
National Institute for Research in Computer Science and Control | INRIA · NEUROMATHCOMP - Mathematical and Computational Neuroscience Research Team

PhD

About

19
Publications
9,704
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
226
Citations
Additional affiliations
August 2013 - September 2016
University of Nice Sophia Antipolis
Position
  • PhD Student
Description
  • Developing computational models for motion perception.
June 2008 - December 2011
International Institute of Information Technology, Hyderabad
Position
  • Research Assistant

Publications

Publications (19)
Article
Full-text available
In sensory systems, a range of computational rules are presumed to be implemented by neuronal subpopulations with different tuning functions. For instance, in primate cortical area MT, different classes of direction-selective cells have been identified and related either to motion integration, segmentation or transparency. Still, how such different...
Thesis
In this thesis, we studied the problem of motion estimation in mammals and propose that scaling up models rooted in biology for real world applications can give us fresh insights into the biological vision. Using a classic model that describes the activity of directionally-selective neurons in V1 and MT areas of macaque brain, we proposed a feedfor...
Poster
Full-text available
Bifurcation analysis and numerical continuation techniques are applied on a ring network of direction selective neurons to understand the interplay between local recurrent interactions and the driving input to the tuning behavior of the population and individual neurons present within.
Article
Full-text available
Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for...
Conference Paper
Full-text available
In this paper we propose a new set of bio-inspired descrip-tors for image classification based on low-level processing performed by the retina. Taking as a starting point a descriptor called FREAK (Fast Retina Keypoint), we further extend it mimicking the center-surround organization of ganglion receptive fields. To test our approach we compared th...
Research
Full-text available
Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilize models that were primarily developed for...
Article
Motion estimation has been studied extensively in neuroscience in the last two decades. Even though there has been some early interaction between the biological and computer vision communities at a modelling level, comparatively little work has been done on the examination or extension of the biological models in terms of their engineering efficacy...
Conference Paper
Full-text available
Motion processing in primates is an intensely studied problem in visual neurosciences and after more than two decades of research, representation of motion in terms of motion energies computed by V1-MT feedforward interactions remains a strong hypothesis. Thus, decoding the motion energies is of natural interest for developing biologically inspired...
Article
Full-text available
We study the impact of local context of an image (contrast and 2D structure) on spatial motion integration by MT neurons. To do so, we revisited the seminal work by Heeger and Simoncelli (HS) using spatio-temporal filters to estimate optical flow from V1-MT feedforward interactions. However, the HS model cannot deal with several problems encountere...
Conference Paper
Full-text available
Context and motivation • Visual motion information is useful for a multiplicity of tasks. • Analysis of it is of great interest both in machine vision for developing applications and biological vision as is it subserves several functional needs. • Owing to the application potential, the problem of visual motion estimation has also received a lot of...
Poster
Full-text available
This study identifies the peak discrimination and asymmetry sensitivity of a recurrently connected neural network with respect to the excitatory and inhibitory interactions among the neurons based on a distance dependant center surround connectivity kernel.
Conference Paper
Full-text available
Multi-Modal image registration is the primary step in fusing complementary information contained in different imaging modalities for diagnostic purposes. We focus on two specific retinal imaging modalities namely, Color Fundus Image(CFI) and Fluroscein Fundus Angiogram(FFA). In this paper we investigate a projection based method using Radon transfo...
Article
Full-text available
The aim of a local descriptor or a feature descriptor is to efficiently represent the region detected by an interest point operator in a compact format for use in various applica-tions related to matching. The common design principle be-hind most of the mainstream descriptors like SIFT, GLOH, Shape context etc is to capture the spatial distribution...

Questions

Questions (2)
Question
I trying to see if blender could be used for event driven sensing simulations, are there any camera models which could be used?
Thanks,
Kartheek.
Question
The traditional research papers are written in a format that is primarily meant for human consumption and has not changed despite the revolution in the technologies we have to share the information. That begs a questions, is it the time for massively changing the format in which we report the research and format it in a way that makes machine summary generation much more reliable and fast. This reduces burden of coping up with tremendous influx of papers that are being generated. I ask this question particularly in relation to neurosciences as the amount of experimental literature published is quite huge and its very hard to bring the bits and pieces together manually.

Network

Cited By