no code implementations • 5 Mar 2024 • Sagi Eppel, Jolina Li, Manuel Drehwald, Alan Aspuru-Guzik
This unsupervised approach allows the generated data to capture the vast complexity of the real world while maintaining the precision and scale of synthetic data.
1 code implementation • ICCV 2023 • Manuel S. Drehwald, Sagi Eppel, Jolina Li, Han Hao, Alan Aspuru-Guzik
The synthetic images were rendered using giant collections of textures, objects, and environments generated by computer graphics artists.
2 code implementations • 23 Sep 2022 • Mario Krenn, Lorenzo Buffoni, Bruno Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, Joao P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp
For that, we use more than 100, 000 research papers and build up a knowledge network with more than 64, 000 concept nodes.
no code implementations • 30 Sep 2021 • Haoping Xu, Yi Ru Wang, Sagi Eppel, Alàn Aspuru-Guzik, Florian Shkurti, Animesh Garg
To address the shortcomings of existing transparent object data collection schemes in literature, we also propose an automated dataset creation workflow that consists of robot-controlled image collection and vision-based automatic annotation.
1 code implementation • 15 Sep 2021 • Sagi Eppel, Haoping Xu, Yi Ru Wang, Alan Aspuru-Guzik
We use this to predict 3D models of vessels and their content from a single image.
Ranked #1 on Single-View 3D Reconstruction on TransProteus
1 code implementation • 4 May 2021 • Sagi Eppel, Haoping Xu, Alan Aspuru-Guzik
This work explores the use of computer vision for image segmentation and classification of medical fluid samples in transparent containers (for example, tubes, syringes, infusion bags).
1 code implementation • 17 Dec 2020 • Cynthia Shen, Mario Krenn, Sagi Eppel, Alan Aspuru-Guzik
We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.
1 code implementation • ACS Central Science 2020 • Sagi Eppel, Haoping Xu, Mor Bismuth, Alan Aspuru-Guzik
Visual recognition of vessels and their contents is essential for performing this task.
2 code implementations • 24 Aug 2019 • Sagi Eppel, Alan Aspuru-Guzik
The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects, stuff and parts regions in the image, and an evaluator net that chooses the best segments to be merged into the segmentation map.
Ranked #37 on Panoptic Segmentation on COCO test-dev
1 code implementation • 20 Feb 2019 • Sagi Eppel
The net was tested and trained on the COCO panoptic dataset and achieved 67% IOU for segmentation of familiar classes (that were part of the net training set) and 53% IOU for segmentation of unfamiliar classes (that were not included in the training).
2 code implementations • 1 Dec 2018 • Sagi Eppel
This goal is achieved using a standard image classification net with the addition of a side branch, which converts the ROI mask into an attention map.
no code implementations • 14 Oct 2017 • Sagi Eppel
It applies one fully convolutional neural net to segment the image into vessel and background, and the vessel region is used as an input for a second net which recognizes the contents of the glass vessel.
2 code implementations • 29 Aug 2017 • Sagi Eppel
This valve filter effectively acts as a valve that inhibits specific features in different image regions according to the ROI map.
1 code implementation • 31 Jan 2016 • Sagi Eppel
The bottom 10% of the vessel region in the image is assumed to correspond to the material phase and defined as the graph and source.
no code implementations • 30 May 2015 • Sagi Eppel
Reflections and the functional parts of a vessels surface can create strong edges that can be mistakenly identified as corresponding to the vessel contents, and cause recognition errors.
no code implementations • 20 Jan 2015 • Sagi Eppel
The probability that a curve matches the material boundary in the image is evaluated using a cost function based on some image properties along this curve.
no code implementations • 28 Apr 2014 • Sagi Eppel, Tal Kachman
The method then compares each curve to the image to rate its correspondence with the outline of the real liquid surface by examining various image properties in the area surrounding each point of the curve.