I hold a BSc. in Electrical & Computer Engineering from Ben-Gurion University in Israel as well as a MBA from Technion Institute in Israel. I am a member of Professional Engineeros Ontario (PEO), Institute of Electrical and Electronic Engineers (IEEE) and Ontario Association of Certified Engineering Technicians and Technologists (OACETT). I have more the 20 years of industrial R&D experience in the areas of analog and digital systems, telecom, data and wireless communication, VLSI chip design, digital mapping systems, video surveillance, automotive electronics, mass production engineering and more. I served as a Director of Research and Development at several companies in the past 20 years.

Currently I am working for the National Research Council – Industrial Research Assistance Program (NRC-IRAP). NRC-IRAP is Canada’s premier innovation assistance program for small and medium-sized enterprises (SMEs). (https://nrc.canada.ca/en)
I help Canadian companies to improve their innovation capability by providing advice on technology, the innovation process, business strategy and market opportunities.

Email: shimon.schwartz@nrc.gc.ca

Email: cscharfenberger@uwaterloo.ca

Sample detection

 Overall three-stage classifier cascade framework for sample detection and classification

The proposed sample detection architecture follows a general design principle of using a cascade of increasingly more expensive operations that run on successively fewer pixels or sub-regions. The first layer – Presence Detection – uses histogram features extracted from saliency maps to judge the presence of interesting objects in images. It can also be viewed as a domain adaptation scheme that maintains the overall object classification performance which might degrade abruptly in novel and untrained environments. The second layer, Localization, isolates and segments objects in very small and noisy pixel patches by applying thresholding on saliency maps, followed by non-maxima suppression and connected labeling stages. The extraction of objects in saliency maps together with advanced image enhancement techniques such bilateral filtering makes the approach feasible for a wide range of outdoor scenarios with unknown backgrounds. The third and final stage, Classification, is very fast and uses a Support Vector Machine along with a Histogram Intersection Kernel to assign class labels and probability output to each candidate object. The development of this work has been evaluated and tested within the NASA Sample Return Robot Challenge where ten objects were randomly scattered throughout a large 80,000m² outdoor environment.

Existence detection

 The proposed framework of the existence detection scheme. The light-red block show the training, and the light-blue boxes the detection procedure.

Autonomous robots performing search tasks in large areas rarely see objects of interest. In the case of images containing background only, existing saliency approaches tend to highlight regions with unique attributes, yet irrelevant information. These regions may lead to false positive detection rates when fed into object detection approaches. As such, we are interested in quickly pruning uninteresting images containing only background information, and in keeping those that are crucial to the task of a robot. To predict the existence of potentially interesting objects, a histogram of saliency values is constructed to effectively remove geometrical information in the saliency map data. By exhibiting the largest variance across training samples using Principal Component Analysis (PCA), the probabilities of the saliency values that best discriminate between object and background images are selected and stacked into a feature vector. A binary classification approach is then applied to robustly predict the existence of interesting objects in images and to quickly discard background images.

Related people

Prof. Steven L. Waslander, Ph.D., P.Eng.,
Mechanical & Mechatronics Engineering, University of Waterloo, CA

Alumni

Related research areas

Computer Vision

Image Segmentation/Classification

Related publications