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| April 3
| April 3
| Austin Myers
| Austin Myers
| TBA
| Affordance of Object Parts from Geometric Features
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|-
| April 10
| April 10
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.
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.
===Affordance of Object Parts from Geometric Features===
Speaker: [https://sites.google.com/site/austinomyers/ Austin Myers] -- Date: April 3, 2014
Understanding affordance is a first step to a deeper understanding of the world, one in which a robot knows how an object and its parts can be used. To assist in everyday activities, robots must not only be able to recognize a tool, but also localize the its parts and identify how each part is used. We propose a preliminary approach to jointly localize and identify the function, or affordances, of a tool’s parts for objects from known or completely novel categories. We combine superpixel segmentation, feature learning, and conditional random fields to provide precise 3D predictions of functional parts that can be used directly by a robot to interact with the world. To investigate this problem, we introduce a new RGB-D Part Affordance Dataset consisting of 105 kitchen, workshop, and garden tools with pixel-level affordance labels for over 10,000 RGB-D images. We analyze the effectiveness of different feature types, and show that geometric features are most important for successful affordance identification. We demonstrate that by identifying the affordances of tools at the level of parts, we can generalize to novel object categories and identify the useful parts of never before seen tools.
==Past Semesters==
==Past Semesters==
