Interview with Dr. Jerome Chermak - University School Headmaster

University School, Head Master, Ray Ferrero, student instruction K-12, University School expansion, Dr. Frank DePiano, autonomous, Abe Fischler, Mickey Segal, Lower School, early childhood expert, autistic children, “Mommy and Me”, Mailman Segal Institute, Jim & Jan Moran Family Center Village, Baudhuin School, Oral School, waiver, SACS-accredited, FCATs, advisory board, PTA, public schools, art, music, theater, private school, Upper School, Roman amphitheater, College prep, SAT, teacher/student ratiohttps://nsuworks.nova.edu/nsudigital_oralhistories/1008/thumbnail.jp

Scale robust IMU-assisted KLT for stereo visual odometry solution

We propose a novel stereo visual IMU-assisted (Inertial Measurement Unit) technique that extends to large inter-frame motion the use of KLT tracker (Kanade–Lucas–Tomasi). The constrained and coherent inter-frame motion acquired from the IMU is applied to detected features through homogenous transform using 3D geometry and stereoscopy properties. This predicts efficiently the projection of the optical flow in subsequent images. Accurate adaptive tracking windows limit tracking areas resulting in a minimum of lost features and also prevent tracking of dynamic objects. This new feature tracking approach is adopted as part of a fast and robust visual odometry algorithm based on double dogleg trust region method. Comparisons with gyro-aided KLT and variants approaches show that our technique is able to maintain minimum loss of features and low computational cost even on image sequences presenting important scale change. Visual odometry solution based on this IMU-assisted KLT gives more accurate result than INS/GPS solution for trajectory generation in certain context

Stixel Based Scene Understanding for Autonomous Vehicles

We propose a stereo vision based obstacle detection and scene segmentation algorithm appropriate for autonomous vehicles. Our algorithm is based on an innovative extension of the Stixel world, which neglects computing a disparity map. Ground plane and stixel distance estimation is improved by exploiting an online learned color model. Furthermore, the stixel height estimation is leveraged by an innovative joined membership scheme based on color and disparity information. Stixels are then used as an input for the semantic scene segmentation providing scene understanding, which can be further used as a comprehensive middle level representation for high-level object detectors

B-HoD: A Lightweight and Fast Binary Descriptor for 3D Object Recognition and Registration

3D object recognition and registration in computer vision applications has lately drawn much attention as it is capable of superior performance compared to its 2D counterpart. Although a number of high performing solutions do exist, it is still challenging to further reduce processing time and memory requirements to meet the needs of time critical applications. In this paper we propose an extension of the 3D descriptor Histogram of Distances (HoD) into the binary domain named the Binary-HoD (B-HoD). Our binary quantization procedure along with the proposed preprocessing step reduce an order of magnitude both processing time and memory requirements compared to current state of the art 3D descriptors. Evaluation on two popular low quality datasets shows its promising performance

Law Enforcement’s Information Sharing Infrastructure: A National Assessment

The September 11 attacks impacted society generally, and law enforcement specifically, in dramatic ways. One of the major trends has been changing expectations regarding criminal intelligence practices among state, local, and tribal (SLT) law enforcement agencies and the need to coordinate intelligence efforts and share information at all levels of government. In fact, enhancing intelligence efforts has emerged as a critical issue for the prevention of all threats and crimes. To date, an increasing number of SLT law enforcement agencies have expanded their intelligence capacity, and there have been fundamental changes in the national, state, and local information sharing infrastructure. Moreover, critical to these expanding information sharing expectations is the institutionalization of fusion centers (FCs). Despite these dramatic changes, an expanding role, and the acknowledgement that local law enforcement intelligence is critical to the prevention and deterrence of threats and crimes, very little research exists that highlights issues related to the intelligence practices of SLT law enforcement agencies and FCs.1 This research describes what agencies are doing to build an intelligence capacity and assesses the state of information sharing among agencies. Specifically, a national survey was developed to examine the experiences of SLT agencies and FCs for building an intelligence capacity as well as to understand critical gaps in the sharing of information regarding intelligence