Dimitris Samaras

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Human activity recognition has potential to impact a wide range of applications from surveillance to human computer interfaces to content based video retrieval. Recently, the rapid development of inexpensive depth sensors (e.g. Microsoft Kinect) provides adequate accuracy for real-time full-body human tracking for activity recognition applications. In this(More)
MAXIMUM ENTROPY MODELS FOR NATURAL LANGUAGE AMBIGUITY RESOLUTION Adwait Ratnaparkhi Supervisor: Professor Mitch Marcus This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state-of-the-art accuracies using a single statistical modeling technique based on the principle of maximum entropy. We discuss the(More)
In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the result which demonstrated that the set of images of a convex Lambertian object obtained under a(More)
We propose a new approach for face recognition under arbitrary illumination conditions, which requires only one training image per subject (if there is no pose variation) and no 3D shape information. Our method is based on the recent result [1] which demostrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting(More)
We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of(More)
AUTOMATIC GRAMMAR GENERATION FROM TWO DIFFERENT PERSPECTIVES Fei Xia Supervisors: Professor Martha Palmer and Aravind Joshi Grammars are valuable resources for natural language processing. We divide the process of grammar development into three tasks: selecting a formalism, de ning the prototypes, and building a grammar for a particular human language.(More)
In this paper, we propose a new PDE-based methodology for deformable surfaces that is capable of automatically evolving its shape to capture the geometric boundary of the data and simultaneously discover its underlying topological structure. Our model can handle multiple types of data (such as volumetric data, 3D point clouds and 2D image data), using a(More)
We propose a new technique for fusing multiple cues to robustly segment an object from its background in video sequences that suffer from abrupt changes of both illumination and position of the target. Robustness is achieved by the integration of appearance and geometric object features and by their estimation using Bayesian filters, such as Kalman or(More)
Topology is an important prior in many image segmentation tasks. In this paper, we design and implement a novel graph-based min-cut/max-flow algorithm that incorporates topology priors as global constraints. We show that optimization of the energy function we consider here is NPhard. However, our algorithm is guaranteed to find an approximate solution that(More)
In this paper, we propose a high-order graph matching formulation to address non-rigid surface matching. The singleton terms capture the geometric and appearance similarities (e.g., curvature and texture) while the high-order terms model the intrinsic embedding energy. The novelty of this paper includes: 1) casting 3D surface registration into a graph(More)