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| March 27
| March 27
| Swaminathan Sankaranarayanan
| Swaminathan Sankaranarayanan
| TBA
| Estimating 3D Face Models
|-
|-
| April 3
| April 3
The integrity of safety-critical infrastructure, such as railway tracks, roads, or bridges needs to be monitored regularly to prevent catastrophic failures. For example, federal regulations require visual inspection of all high speed tracks twice each week. Traditional manual inspection methods are time-consuming and prone to human error. With the availability of high-speed cameras, it is possible to survey large areas in less time. However, detecting cracks and other anomalies on these images is a particularly challenging problem because of the uncontrolled environment arising from differences in material composition, and superficial degradation caused by outdoor elements. Due to speed requirements, images acquired from a moving vehicle have limited resolution, causing the smallest of these cracks to be under-sampled in the transversal dimension. Therefore, these cracks get mixed with background texture, resulting in negative signal-to-noise ratio. State-of-the art methods are based on linear filters, which are only optimal under additive Gaussian noise assumptions. This problem of simultaneous detection and clustering of anomalies in textured images can be posed as a blind source separation problem, and by exploiting the mutual incoherence of the dictionaries of shearlets and isotropic wavelets, which sparsely represent cracks and texture, we can separate each component using an iterative shrinkage algorithm. In this talk, I will present an integrated framework for image separation, feature extraction, clustering and classification that takes advantage of this decomposition.
The integrity of safety-critical infrastructure, such as railway tracks, roads, or bridges needs to be monitored regularly to prevent catastrophic failures. For example, federal regulations require visual inspection of all high speed tracks twice each week. Traditional manual inspection methods are time-consuming and prone to human error. With the availability of high-speed cameras, it is possible to survey large areas in less time. However, detecting cracks and other anomalies on these images is a particularly challenging problem because of the uncontrolled environment arising from differences in material composition, and superficial degradation caused by outdoor elements. Due to speed requirements, images acquired from a moving vehicle have limited resolution, causing the smallest of these cracks to be under-sampled in the transversal dimension. Therefore, these cracks get mixed with background texture, resulting in negative signal-to-noise ratio. State-of-the art methods are based on linear filters, which are only optimal under additive Gaussian noise assumptions. This problem of simultaneous detection and clustering of anomalies in textured images can be posed as a blind source separation problem, and by exploiting the mutual incoherence of the dictionaries of shearlets and isotropic wavelets, which sparsely represent cracks and texture, we can separate each component using an iterative shrinkage algorithm. In this talk, I will present an integrated framework for image separation, feature extraction, clustering and classification that takes advantage of this decomposition.
===Estimating 3D Face Models===
Speaker: Swaminathan Sankaranarayanan -- Date: March 27, 2014
In this talk, I will focus on the topic of 3D Face Model Estimation from Single Grayscale Images. This problem is usually formulated as a Shape from Shading problem involving assumptions about the Image Formation and the Illumination framework. I will review some of the state-of-art methods that attempt to solve this problem by using knowledge from existing 3D shape models of face images. I will the introduce the idea of using Sparse Depth Representations and motivate my method of formulating the Model Estimation problem as a Bilevel Sparse Coding Optimization. I will conclude my talk by explaining the algorithm that is used to solve the objective function and the issues that I am facing with it.
==Past Semesters==
==Past Semesters==
