no code implementations • ECCV 2020 • Zudi Lin, Donglai Wei, Won-Dong Jang, Siyan Zhou, Xupeng Chen, Xueying Wang, Richard Schalek, Daniel Berger, Brian Matejek, Lee Kamentsky, Adi Peleg, Daniel Haehn, Thouis Jones, Toufiq Parag, Jeff Lichtman, Hanspeter Pfister
As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics.
no code implementations • 15 Mar 2022 • Susmit Agrawal, Prabhat Kumar, Siddharth Seth, Toufiq Parag, Maneesh Singh, Venkatesh Babu
Recent algorithms for image manipulation detection almost exclusively use deep network models.
no code implementations • 20 Oct 2021 • Soumya Banerjee, Vinay Kumar Verma, Toufiq Parag, Maneesh Singh, Vinay P. Namboodiri
We propose a novel approach (CIOSL) for the class-incremental learning in an \emph{online streaming setting} to address these challenges.
no code implementations • 19 Mar 2020 • Toufiq Parag, Hongcheng Wang
Classification is a pivotal function for many computer vision tasks such as object classification, detection, scene segmentation.
no code implementations • 29 Feb 2020 • Longlong Jing, Toufiq Parag, Zhe Wu, YingLi Tian, Hongcheng Wang
To minimize the dependence on a large annotated dataset, our proposed semi-supervised method trains from a small number of labeled examples and exploits two regulatory signals from unlabeled data.
no code implementations • 11 Sep 2018 • Felix Gonda, Donglai Wei, Toufiq Parag, Hanspeter Pfister
For video and volumetric data understanding, 3D convolution layers are widely used in deep learning, however, at the cost of increasing computation and training time.
1 code implementation • 8 Jul 2018 • Toufiq Parag, Daniel Berger, Lee Kamentsky, Benedikt Staffler, Donglai Wei, Moritz Helmstaedter, Jeff W. Lichtman, Hanspeter Pfister
The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types.
no code implementations • 27 Jul 2017 • Toufiq Parag, Fabian Tschopp, William Grisaitis, Srinivas C. Turaga, Xuewen Zhang, Brian Matejek, Lee Kamentsky, Jeff W. Lichtman, Hanspeter Pfister
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals.
no code implementations • 30 May 2017 • David Rolnick, Yaron Meirovitch, Toufiq Parag, Hanspeter Pfister, Viren Jain, Jeff W. Lichtman, Edward S. Boyden, Nir Shavit
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology.
no code implementations • 27 Oct 2016 • Felix Gonda, Verena Kaynig, Ray Thouis, Daniel Haehn, Jeff Lichtman, Toufiq Parag, Hanspeter Pfister
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures.
no code implementations • ICCV 2015 • Toufiq Parag, Dan C. Ciresan, Alessandro Giusti
The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms.
no code implementations • 18 Mar 2015 • Toufiq Parag
The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms.
no code implementations • 9 Sep 2014 • Toufiq Parag
Each pixel is associated with a label which indicates whether it is a target or background pixel.
no code implementations • 5 Sep 2014 • Stephen M. Plaza, Toufiq Parag, Gary B. Huang, Donald J. Olbris, Mathew A. Saunders, Patricia K. Rivlin
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics.
1 code implementation • 6 Jun 2014 • Toufiq Parag, Stephen Plaza, Louis Scheffer
Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms.
1 code implementation • 5 Jun 2014 • Toufiq Parag, Anirban Chakraborty, Stephen Plaza, Lou Scheffer
In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron.
no code implementations • 2 Jul 2013 • Toufiq Parag
A set label disagreement function is defined over the number of variables that deviates from the dominant label.
1 code implementation • 25 Mar 2013 • Juan Nunez-Iglesias, Ryan Kennedy, Toufiq Parag, Jianbo Shi, Dmitri B. Chklovskii
We aim to improve segmentation through the use of machine learning tools during region agglomeration.