Search Results for author: Gal Oren

Found 6 papers, 4 papers with code

ChangeChip: A Reference-Based Unsupervised Change Detection for PCB Defect Detection

1 code implementation13 Sep 2021 Yehonatan Fridman, Matan Rusanovsky, Gal Oren

In this paper, we introduce ChangeChip, an automated and integrated change detection system for defect detection in PCBs, from soldering defects to missing or misaligned electronic elements, based on Computer Vision (CV) and UL.

Defect Detection

An End-to-End Computer Vision Methodology for Quantitative Metallography

2 code implementations22 Apr 2021 Matan Rusanovsky, Ofer Beeri, Sigalit Ifergane, Gal Oren

(4) Deep anomaly detection and pattern recognition is performed on the inclusions masks to determine spatial, shape and area anomaly detection of the inclusions.

Anomaly Detection Image Inpainting +1

Optimized Memoryless Fair-Share HPC Resources Scheduling using Transparent Checkpoint-Restart Preemption

no code implementations25 Feb 2021 Kfir Zvi, Gal Oren

Common resource management methods in supercomputing systems usually include hard divisions, capping, and quota allotment.

Fairness Distributed, Parallel, and Cluster Computing

Complete CVDL Methodology for Investigating Hydrodynamic Instabilities

1 code implementation3 Apr 2020 Re'em Harel, Matan Rusanovsky, Yehonatan Fridman, Assaf Shimony, Gal Oren

In fluid dynamics, one of the most important research fields is hydrodynamic instabilities and their evolution in different flow regimes.

Image Retrieval Template Matching +1

MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach

1 code implementation27 Feb 2020 Matan Rusanovsky, Gal Oren, Sigalit Ifergane, Ofer Beeri

The micro-structure of most of the engineering alloys contains some inclusions and precipitates, which may affect their properties, therefore it is crucial to characterize them.

Anomaly Detection

Cooperative image captioning

no code implementations26 Jul 2019 Gilad Vered, Gal Oren, Yuval Atzmon, Gal Chechik

Second, we show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions.

Image Captioning

Cannot find the paper you are looking for? You can Submit a new open access paper.