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3D Recognition
Team Leader: Seung Lee
Contact: Seung Lee (leeseung72@gmail.com)
Mailing address:
Intelligent System Research Institute (ISRI)
Research Complex 2, 6th floor, Building C, Natural Sciences Campus, Sungkyunkwan University,
2066, Seobu-ro, Jangan-gu, Suwon-si,
Gyeonggi-do 440-746, South Korea
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Team Description |
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To realize service robots with the capability of visually guided human-like manipulation in an unstructured environment cluttered with various daily life objects, it is crucial to guarantee dependability in visual perception. In order to solve such critical issues as robustness and real-time processing in vision based 3D recognition and pose estimation, we are developing a novel vision based framework designed under the concept of “behavioral perception."
The main features of our approach as follows:
- Behavioral Perception: Dependability of perception may not come from the perfection of individual components, but from the integration of individual components into dependable system behaviors that guarantees reaching perceptual goals. - Automatic selection and collection of an optimal set of evidences based on in-situ monitoring. - A probabilistic and spatial-temporal integration of top-down and bottom-up perceptual processes: multiple evidence-model matching in a sequence of images based on particle filtering till reaching a credible decision.
* Overall Framework

Currently, we are focusing on developing 3D recognition system using FER-CNN and an image translation approach based on deep learning.
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Personnel |
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Current Projects |
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Line-Based Recognition |
This project group members have been developing a recognition system based on 3D line features for identifying of an object and estimating of the pose of it. |
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Particle Filtering Framework |
we are developing a particle filter based probabilistic method for recognizing an object and estimating its pose based on a sequence of images. |
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FER-CNN |
FER-CNN has capability of not only extracting but also reconstructing a hierarchy of features with the layer-wise independent feedback connections that can be trained. |
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Image Translation |
The features reconstructed by the feedback CNN represent those learned by the feedforward CNN. By analyzing how clusters are formed in the layers of feature spaces in the feedback CNN, we can interpret which features critically contribute to recognition. |
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3D Recognition |
To Recognize the 3D Object, 3D Object Position and Rotation In Real-time |
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Recent publications |
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