Search Results for author: Matan Rusanovsky

Found 5 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

Flat-Combining-Based Persistent Data Structures for Non-Volatile Memory

no code implementations23 Dec 2020 Matan Rusanovsky, Ohad Ben-Baruch, Danny Hendler, Pedro Ramalhete

Flat combining (FC) is a synchronization paradigm in which a single thread, holding a global lock, collects requests by multiple threads for accessing a concurrent data structure and applies their combined requests to it.

Distributed, Parallel, and Cluster Computing Operating Systems

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

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