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

PUGTIFs: Passively user-generated thermal invariant features

Feature detection is a vital aspect of computer vision applications, but adverse environments, distance and illumination can affect the quality and repeatability of features or even prevent their identification. Invariance to these constraints would make an ideal feature attribute. Here we propose the first exploitation of consistently occurring thermal signatures generated by a moving platform, a paradigm we define as passively user-generated thermal invariant features (PUGTIFs). In this particular instance, the PUGTIF concept is applied through the use of thermal footprints that are passively and continuously user generated by heat differences, so that features are no longer dependent on the changing scene structure (as in classical approaches) but now maintain a spatial coherency and remain invariant to changes in illumination. A framework suitable for any PUGTIF has been designed consisting of three methods: first, the known footprint size is used to solve for monocular localisation and thus scale ambiguity; second, the consistent spatial pattern allows us to determine heading orientation; and third, these principles are combined in our automated thermal footprint detector (ATFD) method to achieve segmentation/feature detection. We evaluated the detection of PUGTIFs in four laboratory environments (sand, grass, grass with foliage, and carpet) and compared ATFD to typical image segmentation methods. We found that ATFD is superior to other methods while also solving for scaled monocular camera localisation and providing user heading in multiple environments

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

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

Attacks from lone terrorists in the US are more severe than those who are affiliated with groups

US counterterrorism officials continue to grapple with the issue of lone actor terrorism. However, the extent to which these individuals are more dangerous than group-affiliated terrorists is unclear. In new research, Noah Turner, Steven Chermak, and Joshua Freilich investigate the severity of lone actor terrorist attacks compared to those of other terrorists. They find that lone actors do commit more severe attacks than other terrorists, particularly when both fatalities and injuries are considered