AT&T Labs Research - Gopalan, Raghuraman

Projects
Enhanced Indexing and Representation with Vision-Based Biometrics, Leveraging visual biometrics for indexing and representations of content for retrieval and verification.

MIRACLE and the Content Analysis Engine (CAE), The MIRACLE project develops media processing technologies that enable the content-based retrieval and presentation of multimedia data over a range of devices, and a wide range of available bandwidth.

Video - Content Delivery and Consumption, A background on the delivery and consumption of video and multimedia and references to projects within the AT&T Video and Multimedia Technologies and Services Research Department.

Video - Indexing and Representation (Metadata), Video and multimedia indexing and representations (i.e. metadata), their production, and use. Links to projects within the AT&T Video and Multimedia Technologies and Services Research Department.

Video and Multimedia Technologies and Services Research, The AT&T Video and Multimedia Technologies and Services Research Department strives to acquire multimedia and video for indexing,retrieval,and consumption with textual,semantic,and visual modalities.

This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012. , 2012-03-31

{Road scene analysis is a challenging problem that has applications in autonomous navigation of vehicles. An integral component of this system is the robust detection and tracking of lane markings. It is a hard problem primarily due to large appearance variations in lane markings caused by factors such as occlusion (traffic on the road), shadows (from objects like trees), and changing lighting conditions of the scene (transition from day to night). In this paper, we address these issues through a learning-based approach using visual inputs from a camera mounted in front of a vehicle. We propose, (i) a pixel-hierarchy feature descriptor to model contextual information shared by lane markings with the surrounding road region, (ii) a robust boosting algorithm to select relevant contextual features for detecting lane markings, and (iii) particle filters to track the lane markings, without the knowledge of vehicle speed, by assuming the lane markings to be static through the video sequence and then learning the possible road scene variations from the statistics of tracked model parameters. We investigate the effectiveness of our algorithm on challenging daylight and night-time road video sequences.}

This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE Transactions on Pattern Analysis and Machine Intelligence , Volume 34, Issue 6, 2012-03-31

{Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition, by a fusion of image-formation models, and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a pre-specified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise, and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces, by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard datasets.}

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