Events

PhD Defense: "Labeling Large Scale Image Datasets: Exploring Priors, Scalability and Semantics"

Vignesh Jagadeesh

February 19th (Tuesday), 11:30am
Harold Frank Hall (HFH), Rm 4164


Recent advances in high throughput imaging have led to the creation of massive image repositories, where human analysis is often infeasible. Automated image analysis offers a promising alternative for reducing analysis time by several orders of magnitude. In order to design algorithms that are robust and practically usable, there are a variety of design considerations that require investigation.

This dissertation explores three specific considerations in visual segmentation and detection, namely domain specific priors, scalability, and semantics inherent in the data. The first part of this work proposes a framework that adapts a generic segmentation/tracing technique to application specific ones using priors such as topological dynamics and shape in a Markov Random Field (MRF) setting. Subsequently, techniques to scale algorithms for tracing a large number of targets are explored. These tracing algorithms are based on graph diffusion, and are capable of scaling gracefully with increasing number of targets. The final part of this work explores semantic attributes that humans utilize for object detection in weakly supervised settings. Kernel methods are utilized to learn classifiers in multiple feature spaces proposed in this work for detecting non-rigid objects.

This work adopts the problem of connectomics (neuronal circuit reconstruction from Electron Micrographs) to illustrate the applicability of proposed techniques. Specifically, the segmentation and tracing algorithms are shown to isolate neuronal structures in 3D while the detection algorithms localize synaptic junctions, thus taking a step closer to automated neural circuit constructions from raw image data. Further, the proposed algorithms are also applied on natural image and video data to illustrate their generalization capability.

About Vignesh Jagadeesh:

Vignesh is a graduate student working with Prof.Manjunath at the Vision Research Lab since October 2008. He received his MS in 2009 from UCSB and BE from Anna University in 2007. His research interests are in computer vision and scalable data driven image/video analytics. He has spent time as a research intern during Summers of 2008 and 2010 at True Vision 3D Surgical, Summer of 2011 at Mayachitra Inc, and the Summer of 2012 at eBay Research, where he will be headed after graduation.

Hosted by: Professor B.S. Manjunath, Vision Research Lab