Date: October 13th, 2025
Time: 12:30 PM, IST
Venue: A106, R&D
Topic: Computer Vision Applications in Archaeology, Agriculture and Wildlife
Abstract: In my talk I will describe in general how to cooperate with people from various fields of research in computer vision research projects and then describe three research projects that I was involved in in the last few years.
In the first project which is the field of archeology we studied scarabs. Scarabs are seals whose origin is from ancient Egypt (2000 BC) but were also found in Israel. The dataset we obtained from an archeologist consisted of pairs of a photograph and a drawing of a scarab made by an archeological artist. We developed models for classifying the scarabs according to their etchings and according to the era when they were produced. The drawings are naturally of higher quality than the photographs. During training the model was fed with the photograph and the drawing and during inference only photographs were given as input, since they are naturally more common. The algorithm also generated a drawing of the scarab.
In the second project, which is in the field of agriculture, a camera was placed above a drinking facility for sheep. The facility measures the amount of water the sheep drinks and its weight. A video of each sheep was recorded and its face, back and legs were detected. The results of all these detections were fed into classifiers and the identity of the sheep was returned as a combination of the results from the single classifiers. The process was basically automatic without human interaction. This algorithm can be used to monitor the condition of each sheep and report to the farmer if it seems that its medical condition has deteriorated.
In the last project, which is in the field of ecology, a colony of over a thousand terns on a small island was monitored. The terns fly from Europe to Africa and back and stay for some time on the island in Israel. The colony includes two types of terns: common terns and small terns. The whole island was scanned automatically using two PTZ cameras. Using Yolo the types of terns and whether they are brooding or not were classified. In a second stage the results improved since the actual size of the terns, their motion pattern and their population statistics were taken into account. The results are very accurate and also include their geographic position on the island. This method can now be used to monitor the colony population over time
Bio:Ilan Shimshoni has been working in the fields of computer vision computer graphics and machine learning for more than thirty years. He has been working on various problems in computer vision and applying them to applications in robotics and computer graphics. In recent years he jas also been interested in addressing important problems in other fields which are challenging for researchers in my fields of research. He has been working for example on problems in medical rehabilitation, geography, agriculture, and archeology. One of my main fields of interest is developing algorithms in computer vision addressing challenges in the study of animals (wildlife, pets, and domestic animals). This include automatic detection of pain in cats and rabbits, emotion in dogs, and identification of individual sheep on a farm. In the realm of wildlife, He has been working detecting flocks of birds from weather radars, and counting terns of two types and identifying whether they are brooding or not. This helps ecologists estimate their number in one of the main places they stop in Israel while migrating from Europe to Africa,