Current Projects:

	d-Oracle: Distributed Object Recognition via a Camera Wireless Net Harnessing the multiple-view information from a wireless camera sensor network to improve the recognition of objects or actions.
	d-WAR: Distributed Wearable Action Recognition We propose a distributed recognition method to classify human actions using a low-bandwidth wearable motion sensor network. Given a set of pre-segmented motion sequences as training examples, the algorithm simultaneously segments and classifies human actions, and it also rejects outlying actions that are not in the training set. The classification is distributedly operated on individual sensor nodes and a base station computer. Using up to eight body sensors, the algorithm achieves state-of-the-art 98.8% accuracy on a set of 12 action categories. We further demonstrate that the recognition precision only decreases gracefully using smaller subsets of sensors, which validates the robustness of the distributed framework.
	Robust Face Recognition with Facial Occlusion This research is featured in the following reports: Communications of the ACM ABC7 Bay Area News Wired.com Nuit Balanche Rice Compressive Sensing Online
	Image Analysis and Segmentation via Lossy Data Compression We cast natural-image segmentation as a problem of clustering texure features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. Unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures in an image. We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach.

Past Projects:

	Feature Selection in Face Recognition: A Sparse Representation Perspective Formulating the problem of face recognition under the emerging theory of compressed sensing, we examine the role of feature selection/dimensionality reduction from the perspective of sparse representation. Our experiments show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical is whether the number of features is sufficient and whether the sparse representation is correctly found.
	Robust Algebraic Segmentation of Mixed Rigid-Body and Planar Motions in Two Views We study segmentation of multiple rigidbody motions in a 3-D dynamic scene under perspective camera projection. Based on the well-known epipolar and homography constraints between two views, we propose a hybrid perspective constraint (HPC) to unify the representation of rigid-body and planar motions. Given a mixture of K hybrid perspective constraints, we propose an algebraic process to partition image correspondences to the individual 3-D motions, called Robust Algebraic Segmentation (RAS). We conduct extensive simulations and real experiments to validate the performance of the new algorithm. The results demonstrate that RAS achieves notably higher accuracy than most existing robust motion segmentation methods, including random sample consensus (RANSAC) and its variations. The implementation of the algorithm is also two to three times faster than the existing methods.We will make the implementation of the algorithm and the benchmark scripts available on our website.
	Generalized Principal Component Analysis (GPCA)                                             An algebraic framework for modeling and segmenting mixed data using a union of subspaces, a.k.a. subspace arrangements. Yet the statistical implementation of the framework is robust to data noise and outliers.
	Symmetry-based 3-D Reconstruction from Perspective Images We investigated a unified framework to extract poses and structures of 2-D symmetric patterns from perspective images. The framework uniformly encompasses all three fundamental types of symmetry: Reflection, Rotation, and Translation, based on a systematic study of the homography groups in image induced by the symmetry groups in space. We claim the following principle: If a planar object admits rich enough symmetry, no 3-D geometric information is lost through perspective imaging.
	RoboTalk A unified robot motion interface and tele-communication protocols for controlling arms, bases, and androids. Copyright (c) Honda Research, Mountain View, CA.

Teaching:

• CVPR Tutorial, 2009: Sparse Representation and Its Applications in Computer Vision.
• Introduction: Face Recognition [slides]
• Comopressive Sensing Theory [slides]
• Applications: Distributed Sensing and Perception [slides]
• Robust PCA [slides]
  
• Spring, 2009: High-Dimensional Multi-Model Estimation -- A Mini Course. [slides]
  
• Spring, 2008: Compressed Sensing Meets Machine Learning -- A Mini Course. [lecture 1, lecture 2, lecture 3]
  
• Spring, 2007: Generalized Principal Component Analysis -- A Mini Course. [lecture 1, lecture 2, lecture 3]
• Fall, 2006: CS294-6: Theoretical, Conceptual and Experimental Vision.

Life outside Cory

• Frankly, Cory Hall feels like a bunker inside out. My phone calls are constantly being cut-off. That is the Berkeleyway saying: Hey, no sweat in an earthquake! :)
  
• I enjoy listening to musical CDs in my apartment from a pair of Paradigm Reference Studio 20 speakers biwired to an NAD C350 amp.
  
• Sometimes in a weekend, I will drive to St. Helena and Rutherford in Napa for a wine tasting.
  

Links:

(Last Modified on Dec. 18, 2008)