Princeton, FL, United States
Princeton, FL, United States

Sarnoff Corporation, with headquarters in West Windsor Township, New Jersey, was a research and development company specializing in vision, video and semiconductor technology. It was named for David Sarnoff, the longtime leader of RCA and NBC.The cornerstone of Sarnoff Corporation's David Sarnoff Research Center in the Princeton vicinity was laid just before the attack on Pearl Harbor in 1941. That facility, later Sarnoff Corporation headquarters, was the site of several historic developments, notably color television, CMOS integrated circuit technology, electron microscopy, and many other important technologies affecting everyday life worldwide. Following 47 years as a central research laboratory for its corporate owner RCA as RCA Laboratories, in 1988 the David Sarnoff Research Center was transitioned to Sarnoff Corporation, a wholly owned subsidiary of SRI International. In January 2011, Sarnoff Corporation was integrated into its parent company, SRI International, and continues to engage in similar research and development activities at the Princeton, New Jersey facility. Wikipedia.

Time filter

Source Type

Seibert C.S.,University of Notre Dame | Hall D.C.,University of Notre Dame | Liang D.,University of Notre Dame | Liang D.,University of California at Santa Barbara | Shellenbarger Z.A.,Sarnoff Corporation
IEEE Photonics Technology Letters | Year: 2010

We demonstrate the efficacy of oxidation smoothing of sidewall roughness in high-index-contrast AlGaAs heterostructure ridge waveguides via oxygen-enhanced nonselective wet thermal oxidation for reducing scattering loss. Single-mode waveguides of core widths between 1.5 and 2.2 μm are fabricated using both the inward growth of a ∼600-nm sidewall-smoothing native oxide outer cladding and, for comparison, encapsulation of an unoxidized etched ridge with a ∼600-nm deposited silicon oxide cladding layer. On average, measured loss coefficients are reduced by a factor of 2 with the oxidation smoothing process. © 2009 IEEE.

Ali S.,Sarnoff Corporation | Javed O.,Sarnoff Corporation | Haering N.,ObjectVideo | Kanade T.,Robotics Institute
MM'10 - Proceedings of the ACM Multimedia 2010 International Conference | Year: 2010

We address the problem of interactive search for a target of interest in surveillance imagery. Our solution consists of iteratively learning a distance metric for retrieval, based on user feedback. The approach employs (retrieval) rank based constraints and convex optimization to efficiently learn the distance metric. The algorithm uses both user labeled and unlabeled examples in the learning process. The method is fast enough for a new metric to be learned interactively for each target query. In order to reduce the burden on the user, a model-independent active learning method is used to select key examples, for response solicitation. This leads to a significant reduction in the number of user-interactions required for retrieving the target of interest. The proposed method is evaluated on challenging pedestrian and vehicle data sets, and compares favorably to the state of the art in target re-acquisition algorithms. © 2010 ACM.

Kuthirummal S.,Sarnoff Corporation | Nagahara H.,Osaka University | Zhou C.,Columbia University | Nayar S.K.,Columbia University
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2011

The range of scene depths that appear focused in an image is known as the depth of field (DOF). Conventional cameras are limited by a fundamental trade-off between depth of field and signal-to-noise ratio (SNR). For a dark scene, the aperture of the lens must be opened up to maintain SNR, which causes the DOF to reduce. Also, today's cameras have DOFs that correspond to a single slab that is perpendicular to the optical axis. In this paper, we present an imaging system that enables one to control the DOF in new and powerful ways. Our approach is to vary the position and/or orientation of the image detector during the integration time of a single photograph. Even when the detector motion is very small (tens of microns), a large range of scene depths (several meters) is captured, both in and out of focus. Our prototype camera uses a micro-actuator to translate the detector along the optical axis during image integration. Using this device, we demonstrate four applications of flexible DOF. First, we describe extended DOF where a large depth range is captured with a very wide aperture (low noise) but with nearly depth-independent defocus blur. Deconvolving a captured image with a single blur kernel gives an image with extended DOF and high SNR. Next, we show the capture of images with discontinuous DOFs. For instance, near and far objects can be imaged with sharpness, while objects in between are severely blurred. Third, we show that our camera can capture images with tilted DOFs (Scheimpflug imaging) without tilting the image detector. Finally, we demonstrate how our camera can be used to realize nonplanar DOFs. We believe flexible DOF imaging can open a new creative dimension in photography and lead to new capabilities in scientific imaging, vision, and graphics. © 2006 IEEE.

Van Der Wal G.S.,Sarnoff Corporation
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

The Sarnoff Acadia® II is a powerful vision processing SoC (System-on-a-Chip) that was specifically developed to support advanced vision applications where system size, weight and/or power are severely constrained. This paper, targeted at vision system developers, presents a detailed technical overview of the Acadia® II, highlighting its architecture, processing capabilities, memory and peripheral interfaces. All major subsystems will be covered, including: video preprocessing, specialized vision processing cores for multi-spectral image fusion, multi-resolution contrast normalization, noise coring, image warping, and motion estimation. Application processing via the MPCore®, an integrated set of four ARM®11 floating point processors with associated peripheral interfaces is presented in detail. The paper will emphasize the programmability of the Acadia® II, while describing its ability to provide state-of-the-art realtime image processing in a small, power optimized package. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Bansal M.,Sarnoff Corporation
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2010

Localizing blood vessels in eye images is a crucial step in the automated and objective diagnosis of eye diseases. Most previous research has focused on extracting the centerlines of vessels in large field of view images. However, for diagnosing diseases of the optic disk region, like glaucoma, small field of view images have to be analyzed. One needs to identify not only the centerlines, but also vessel widths, which vary widely in these images. We present an automatic technique for localizing vessels in small field of view images using multi-scale matched filters. We also estimate local vessel properties - width and orientation - along the length of each vessel. Furthermore, we explicitly account for highlights on thick vessels - central reflexes - which are ignored in many previous works. Qualitative and quantitative results demonstrate the efficacy of our method - e.g. vessel centers are localized with RMS and median errors of 2.11 and 1 pixels, respectively in 700×700 images.

Berends D.,Sarnoff Corporation | Van Der Wal G.S.,Sarnoff Corporation
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

Vision system designers often face the daunting challenge of implementing powerful image processing capabilities in severely size, weight and power constrained systems. Multi-sensor fusion, image stabilization, image enhancement, target detection and object tracking are fundamental processing techniques required by UAVs (Unmanned Aerial Vehicles), smart cameras, weapon sights, and vehicle situational awareness systems. All of these systems also process non-vision data while communicating large amounts of information elsewhere. To meet their demanding requirements, Sarnoff developed the Acadia® II System-on-a-Chip, combining dedicated image processing cores, four ARM®11 processors and an abundance of peripherals in a single Integrated Circuit. This paper will describe how to best use the power of the Acadia® II as both an all-in-one image processor and as a general purpose computer for performing other critical non-vision tasks, such as flight control and system-to-system communication. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Xiao J.,Sarnoff Corporation | Cheng H.,Sarnoff Corporation | Sawhney H.,Sarnoff Corporation | Han F.,Sarnoff Corporation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2010

This paper presents a joint probabilistic relation graph approach to simultaneously detect and track a large number of vehicles in low frame rate aerial videos. Due to low frame rate, low spatial resolution and sheer number of moving objects, detection and tracking in wide area video poses unique challenges. In this paper, we explore vehicle behavior model from road structure and generate a set of constraints to regulate both object based vertex matching and pairwise edge matching schemes. The proposed relation graph approach then unifies these two matching schemes into a single cost minimization framework to produce a quadratic optimized association result. The experiments on hours of real videos demonstrate the graph matching framework with vehicle behavior model effectively improves tracking performance in large scale dense traffic scenarios. ©2010 IEEE.

Khan S.M.,Sarnoff Corporation | Cheng H.,Sarnoff Corporation | Matthies D.,Sarnoff Corporation | Sawhney H.,Sarnoff Corporation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2010

We present an approach that uses detailed 3D models to detect and classify objects into fine levels of vehicle categories. Unlike other approaches that use silhouette information to fit a 3D model, our approach uses complete appearance from the image. Each 3D model has a set of salient location markers that are determined a-priori. These salient locations represent a sub-sampling of 3D locations that make up the model. Scene conditions are simulated in the rendering of 3D models and the salient locations are used to bootstrap a HoG based feature classifier. HoG features are computed in both rendered and real scenes and a novel object match score the 'Salient Feature Match Distribution Matrix' is computed. For each 3D model we also learn the patterns of misalignment with other vehicle types and use it as an additional cue for classification. Results are presented on a challenging aerial video dataset consisting of vehicle imagery from various viewpoints and environmental conditions. ©2010 IEEE.

Bansal M.,Sarnoff Corporation | Jung S.-H.,Sarnoff Corporation | Matei B.,Sarnoff Corporation | Eledath J.,Sarnoff Corporation | Sawhney H.,Sarnoff Corporation
Proceedings - IEEE International Conference on Robotics and Automation | Year: 2010

We present a real-time pedestrian detection system based on structure and appearance classification. We discuss several novel ideas that contribute to having low-false alarms and high detection rates, while at the same time achieving computational efficiency: (i) At the front end of our system we employ stereo to detect pedestrians in 3D range maps using template matching with a representative 3D shape model, and to classify other background objects in the scene such as buildings, trees and poles. The structure classification efficiently labels substantial amount of non-relevant image regions and guides the further computationally expensive process to focus on relatively small image parts; (ii)We improve the appearance-based classifiers based on HoG descriptors by performing template matching with 2D human shape contour fragments that results in improved localization and accuracy; (iii) We build a suite of classifiers tuned to specific distance ranges for optimized system performance. Our method is evaluated on publicly available datasets and is shown to match or exceed the performance of leading pedestrian detectors in terms of accuracy as well as achieving real-time computation (10 Hz), which makes it adequate for in-vehicle navigation platform. ©2010 IEEE.

Sarnoff Corporation | Date: 2012-04-18

A bipolar plate comprises:a first corrosion resistant layer having a plurality of vias formed therein, each via extending through the first corrosion resistant layer and being filled with an electrically conductive, corrosion resistant material;a second corrosion resistant layer having a plurality of vias formed therein, each via extending through the second corrosion resistant layer and being filled with an electrically conductive, corrosion resistant material;a conductive metallic core having a first primary surface in electrical contact with the material in the vias of the first corrosion resistant layer and a second primary surface in electrical contact with the material in the vias of the second corrosion resistant layer;the material in the vias being different from the material forming the conductive metallic core, the first corrosion resistant layer, and the second corrosion resistant layer.

Loading Sarnoff Corporation collaborators
Loading Sarnoff Corporation collaborators