6D Pose Estimation using an Improved Method based on Point Pair Features

The Point Pair Feature (Drost et al. 2010) has been one of the most successful 6D pose estimation method among model-based approaches as an efficient, integrated and compromise alternative to the traditional local and global pipelines. During the last years, several variations of the algorithm have been proposed. Among these extensions, the solution introduced by Hinterstoisser et al. (2016) is a major contribution. This work presents a variation of this PPF method applied to the SIXD Challenge datasets presented at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV 2017. We report an average recall of 0.77 for all datasets and overall recall of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight, rutgers, tejani and doumanoglou datasets, respectively

The Workforce Needs of New Jersey's Pharmaceutical and Medical Technology Industry

This report is based on an online survey conducted in spring 2006 of pharmaceutical and medical technology companies in New Jersey. It identifies the current and future workforce needs of the pharmaceutical and medical technology industry in New Jersey

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge

Ready for Tomorrow: Demand-Side Emerging Skills for the 21st Century

As part of the Ready for the Job demand-side skill assessment, the Heldrich Center explored emerging work skills that will affect New Jersey's workforce in the next three to five years. The Heldrich Center identified five specific areas likely to generate new skill demands: biotechnology, security, e-learning, e-commerce, and food/agribusiness. This report explores the study's findings and offers recommendations for improving education and training in New Jersey

Trying to Become the Person I Was Before: 9/11 Displaced Workers and the Employment Assistance Program

This report examines the September 11th Fund's Employment Assistance Program (EAP), an effort to enable workers displaced by the 9/11 terrorist attacks to connect with employment services, career counselors, job placement opportunities, education, training, and other resources. The report discusses the EAP's different services, and analyzes participants' employment status prior to the attacks and following their participation in the EA

Linguistically-driven framework for computationally efficient and scalable sign recognition

We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. This parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL)