no code implementations • ECCV 2020 • Yujun Chen, Manoj Kumar Sharma, Ashutosh Sabharwal, Ashok Veeraraghavan, Aswin C. Sankaranarayanan
A transmission matrix (TM) describes the linear relationship between input and output phasor fields when a coherent wave passes through a scattering medium.
no code implementations • ECCV 2020 • Yicheng Wu, Vivek Boominathan, Xuan Zhao, Jacob T. Robinson, Hiroshi Kawasaki, Aswin Sankaranarayanan, Ashok Veeraraghavan
The projected pattern can be observed in part or full by any camera, to reconstruct both the 3D map of the scene and the camera pose in the projector coordinates.
no code implementations • 4 Feb 2025 • Matt Y. Cheung, Sophia Zorek, Tucker J. Netherton, Laurence E. Court, Sadeer Al-Kindi, Ashok Veeraraghavan, Guha Balakrishnan
First, we find that classical priors are superior to diffusion priors when the number of projections is ``sufficient''.
no code implementations • 30 Jan 2025 • Bhargav Ghanekar, Lianne R. Johnson, Jacob L. Laughlin, Marcia K. O'Malley, Ashok Veeraraghavan
In recent years, the explosion of deep learning for vision applications has led to many works in surgical instrument segmentation, while lesser focus has been on tracking specific tool keypoints, such as tool tips.
1 code implementation • 7 Oct 2024 • Matt Y. Cheung, Tucker J. Netherton, Laurence E. Court, Ashok Veeraraghavan, Guha Balakrishnan
Specifically for absolute residual and quantile-based non-conformity scores, we prove: 1) the upper bound of symmetrically adjusted interval lengths increases by $2|b|$ where $b$ is a globally applied scalar value representing bias, 2) asymmetrically adjusted interval lengths are not affected by bias, and 3) conditions when asymmetrically adjusted interval lengths are guaranteed to be smaller than symmetric ones.
no code implementations • 1 Oct 2024 • Yuhao Liu, James Doss-Gollin, Guha Balakrishnan, Ashok Veeraraghavan
Understanding local risks from extreme rainfall, such as flooding, requires both long records (to sample rare events) and high-resolution products (to assess localized hazards).
no code implementations • 15 Sep 2024 • Kushal Vyas, Ahmed Imtiaz Humayun, Aniket Dashpute, Richard G. Baraniuk, Ashok Veeraraghavan, Guha Balakrishnan
We evaluate STRAINER on multiple in-domain and out-of-domain signal fitting tasks and inverse problems and further provide detailed analysis and discussion on the transferability of STRAINER's features.
1 code implementation • 27 Aug 2024 • Yiran Sun, Hana Baroudi, Tucker Netherton, Laurence Court, Osama Mawlawi, Ashok Veeraraghavan, Guha Balakrishnan
Computed Tomography (CT) scans are the standard-of-care for the visualization and diagnosis of many clinical ailments, and are needed for the treatment planning of external beam radiotherapy.
no code implementations • 14 Jun 2024 • Akshat Dave, Tianyi Zhang, Aaron Young, Ramesh Raskar, Wolfgang Heidrich, Ashok Veeraraghavan
We develop an experimental multi-axis polariscope setup to capture 3D photoelasticity and experimentally demonstrate that NeST reconstructs the internal stress distribution for objects with varying shape and force conditions.
no code implementations • 2 Jun 2024 • Tianyi Zhang, Matthew Dutson, Vivek Boominathan, Mohit Gupta, Ashok Veeraraghavan
To the best of our knowledge, our approach is the first to achieve online, real-time image reconstruction on QIS.
1 code implementation • 23 Apr 2024 • Matt Y Cheung, Tucker J Netherton, Laurence E Court, Ashok Veeraraghavan, Guha Balakrishnan
Recent advancements in machine learning have led to the development of novel medical imaging systems and algorithms that address ill-posed problems.
no code implementations • CVPR 2024 • Mingyang Xie, Haiyun Guo, Brandon Y. Feng, Lingbo Jin, Ashok Veeraraghavan, Christopher A. Metzler
Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy.
no code implementations • 19 Mar 2024 • Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar
It learns photorealistic scene representations by decomposing users' 3D views into personal and global NeRFs and a novel optimally weighted aggregation of only the latter.
no code implementations • CVPR 2024 • Bhargav Ghanekar, Salman Siddique Khan, Pranav Sharma, Shreyas Singh, Vivek Boominathan, Kaushik Mitra, Ashok Veeraraghavan
Our resulting CADS imaging system demonstrates improvement of >1. 5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings.
no code implementations • 7 Dec 2023 • Haoming Cai, Jingxi Chen, Brandon Y. Feng, Weiyun Jiang, Mingyang Xie, Kevin Zhang, Ashok Veeraraghavan, Christopher Metzler
tmospheric turbulence presents a significant challenge in long-range imaging.
no code implementations • 16 Nov 2023 • Aniket Dashpute, Jiazhang Wang, James Taylor, Oliver Cossairt, Ashok Veeraraghavan, Florian Willomitzer
In this paper, we present a novel event-based structured light system that enables fast 3D imaging of mixed reflectance scenes with high accuracy.
1 code implementation • 21 Sep 2023 • Yuhao Liu, Pranavesh Panakkal, Sylvia Dee, Guha Balakrishnan, Jamie Padgett, Ashok Veeraraghavan
Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST).
1 code implementation • 4 Aug 2023 • Yiran Sun, Tucker Netherton, Laurence Court, Ashok Veeraraghavan, Guha Balakrishnan
In this work, we propose a method to generate CT volumes from few (<5) planar X-ray observations using a prior data distribution, and perform the first evaluation of such a reconstruction algorithm for a clinical application: radiotherapy planning.
no code implementations • 1 Aug 2023 • Weiyun Jiang, Yuhao Liu, Vivek Boominathan, Ashok Veeraraghavan
The atmospheric and water turbulence mitigation problems have emerged as challenging inverse problems in computer vision and optics communities over the years.
no code implementations • CVPR 2023 • Siddharth Somasundaram, Akshat Dave, Connor Henley, Ashok Veeraraghavan, Ramesh Raskar
Specifically, we study how ToF information can reduce the number of measurements and spatial resolution needed for shape reconstruction.
1 code implementation • CVPR 2023 • Aniket Dashpute, Vishwanath Saragadam, Emma Alexander, Florian Willomitzer, Aggelos Katsaggelos, Ashok Veeraraghavan, Oliver Cossairt
Our key observation is that the rate of heating and cooling of an object depends on the unique intrinsic properties of the material, namely the emissivity and diffusivity.
2 code implementations • CVPR 2023 • Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk
Implicit neural representations (INRs) have recently advanced numerous vision-related areas.
no code implementations • CVPR 2023 • Kushagra Tiwary, Akshat Dave, Nikhil Behari, Tzofi Klinghoffer, Ashok Veeraraghavan, Ramesh Raskar
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
no code implementations • 13 Dec 2022 • Vishwanath Saragadam, Zheyi Han, Vivek Boominathan, Luocheng Huang, Shiyu Tan, Johannes E. Fröch, Karl F. Böhringer, Richard G. Baraniuk, Arka Majumdar, Ashok Veeraraghavan
A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high-resolution center and a lower-resolution large field of view context.
1 code implementation • 8 Dec 2022 • Kushagra Tiwary, Akshat Dave, Nikhil Behari, Tzofi Klinghoffer, Ashok Veeraraghavan, Ramesh Raskar
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
no code implementations • European Conference on Computer Vision (ECCV) 2022 • Zaid Tasneem, Giovanni Milione, Yi-Hsuan Tsai, Xiang Yu, Ashok Veeraraghavan, Manmohan Chandraker, Francesco Pittaluga
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others.
no code implementations • 3 Jul 2022 • Bhargav Ghanekar, Vishwanath Saragadam, Dushyant Mehra, Anna-Karin Gustavsson, Aswin Sankaranarayanan, Ashok Veeraraghavan
A special property of the phase mask used for generating the DHPSF is that a separation of the phase mask into two halves leads to a spatial separation of the two lobes.
no code implementations • 7 Apr 2022 • Vishwanath Saragadam, Randall Balestriero, Ashok Veeraraghavan, Richard G. Baraniuk
DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks.
no code implementations • 25 Mar 2022 • Akshat Dave, Yongyi Zhao, Ashok Veeraraghavan
Reconstructing an object's geometry and appearance from multiple images, also known as inverse rendering, is a fundamental problem in computer graphics and vision.
1 code implementation • 7 Feb 2022 • Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G. Baraniuk, Ashok Veeraraghavan
We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals.
1 code implementation • 18 Aug 2021 • Vishwanath Saragadam, Akshat Dave, Ashok Veeraraghavan, Richard Baraniuk
We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation.
no code implementations • CVPR 2021 • Shiyu Tan, Yicheng Wu, Shoou-I Yu, Ashok Veeraraghavan
Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these variables.
no code implementations • 27 Jan 2021 • Yongyi Zhao, Ankit Raghuram, Hyun K. Kim, Andreas H. Hielscher, Jacob T. Robinson, Ashok Veeraraghavan
Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue -- well beyond the conventional $\sim$10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy.
no code implementations • ICCV 2021 • Ewa M. Nowara, Daniel McDuff, Ashok Veeraraghavan
The core idea is that the signal of interest is stronger in some pixels ("foreground"), and by selectively focusing computation on these pixels, networks can extract subtle information buried in noise and other sources of corruption.
1 code implementation • ICCV 2021 • Yonggan Fu, Yang Zhang, Yue Wang, Zhihan Lu, Vivek Boominathan, Ashok Veeraraghavan, Yingyan Lin
PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck.
1 code implementation • 28 Dec 2020 • Vishwanath Saragadam, Michael DeZeeuw, Richard Baraniuk, Ashok Veeraraghavan, Aswin Sankaranarayanan
Hence, a scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions.
1 code implementation • ICCV 2021 • Yicheng Wu, Qiurui He, Tianfan Xue, Rahul Garg, Jiawen Chen, Ashok Veeraraghavan, Jonathan T. Barron
When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts.
1 code implementation • 29 Oct 2020 • Salman S. Khan, Varun Sundar, Vivek Boominathan, Ashok Veeraraghavan, Kaushik Mitra
Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera.
no code implementations • 14 Oct 2020 • Ewa Nowara, Daniel McDuff, Ashok Veeraraghavan
A convolutional attention network is used to learn which regions of a video contain the physiological signal and generate a preliminary estimate.
no code implementations • 19 Nov 2018 • Li Niu, Ashok Veeraraghavan, Ashu Sabharwal
In the extreme case, given a set of test categories without any well-labeled training data, the majority of existing works can be grouped into the following two research directions: 1) crawl noisy labeled web data for the test categories as training data, which is dubbed as webly supervised learning; 2) transfer the knowledge from auxiliary categories with well-labeled training data to the test categories, which corresponds to zero-shot learning setting.
1 code implementation • ICML 2018 • Junru Wu, Yue Wang, Zhen-Yu Wu, Zhangyang Wang, Ashok Veeraraghavan, Yingyan Lin
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e. g., GoogLeNet, ResNet and Wide ResNet).
1 code implementation • 24 Jun 2018 • Junru Wu, Yue Wang, Zhen-Yu Wu, Zhangyang Wang, Ashok Veeraraghavan, Yingyan Lin
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e. g., GoogLeNet, ResNet and Wide ResNet).
no code implementations • CVPR 2018 • Li Niu, Ashok Veeraraghavan, Ashutosh Sabharwal
The drawbacks of the above two directions motivate us to design a new framework which can jointly leverage both web data and auxiliary labeled categories to predict the test categories that are not associated with any well-labeled training images.
no code implementations • 16 May 2018 • Wanjia Liu, Huaijin Chen, Rishab Goel, Yuzhong Huang, Ashok Veeraraghavan, Ankit Patel
Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks.
no code implementations • CVPR 2018 • Li Niu, Qingtao Tang, Ashok Veeraraghavan, Ashu Sabharwal
As tons of photos are being uploaded to public websites (e. g., Flickr, Bing, and Google) every day, learning from web data has become an increasingly popular research direction because of freely available web resources, which is also referred to as webly supervised learning.
2 code implementations • ICML 2018 • Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan, Richard G. Baraniuk
Phase retrieval algorithms have become an important component in many modern computational imaging systems.
no code implementations • 16 Jan 2018 • Huaijin Chen, Jinwei Gu, Orazio Gallo, Ming-Yu Liu, Ashok Veeraraghavan, Jan Kautz
Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference.
no code implementations • 16 Nov 2017 • Li Niu, Jianfei Cai, Ashok Veeraraghavan
Zero-Shot Learning (ZSL) aims to classify a test instance from an unseen category based on the training instances from seen categories, in which the gap between seen categories and unseen categories is generally bridged via visual-semantic mapping between the low-level visual feature space and the intermediate semantic space.
no code implementations • 29 Jun 2017 • Subhadip Mukherjee, Deepak R., Huaijin Chen, Ashok Veeraraghavan, Chandra Sekhar Seelamantula
The proposed online algorithm is useful in a setting where one seeks to design a progressive decoding strategy to reconstruct a sparse signal from linear measurements so that one does not have to wait until all measurements are acquired.
no code implementations • CVPR 2016 • Huaijin Chen, Suren Jayasuriya, Jiyue Yang, Judy Stephen, Sriram Sivaramakrishnan, Ashok Veeraraghavan, Alyosha Molnar
Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications.
no code implementations • 21 Dec 2015 • Ryuichi Tadano, Adithya Kumar Pediredla, Kaushik Mitra, Ashok Veeraraghavan
Transient imaging or light-in-flight techniques capture the propagation of an ultra-short pulse of light through a scene, which in effect captures the optical impulse response of the scene.
no code implementations • ICCV 2015 • Ryuichi Tadano, Adithya Kumar Pediredla, Ashok Veeraraghavan
Time of flight (ToF) cameras use a temporally modulated light source and measure correlation between the reflected light and a sensor modulation pattern, in order to infer scene depth.
no code implementations • 28 Oct 2015 • Jason Holloway, M. Salman Asif, Manoj Kumar Sharma, Nathan Matsuda, Roarke Horstmeyer, Oliver Cossairt, Ashok Veeraraghavan
Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope.
no code implementations • 2 Sep 2015 • Suren Jayasuriya, Adithya Pediredla, Sriram Sivaramakrishnan, Alyosha Molnar, Ashok Veeraraghavan
In this paper, we explore the strengths and weaknesses of combining light field and time-of-flight imaging, particularly the feasibility of an on-chip implementation as a single hybrid depth sensor.
2 code implementations • 1 Sep 2015 • M. Salman Asif, Ali Ayremlou, Aswin Sankaranarayanan, Ashok Veeraraghavan, Richard Baraniuk
FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array.
no code implementations • 5 Aug 2015 • Qiong Huang, Ashok Veeraraghavan, Ashutosh Sabharwal
We study gaze estimation on tablets, our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet is not constrained.
no code implementations • CVPR 2015 • Huaijin Chen, M. Salman Asif, Aswin C. Sankaranarayanan, Ashok Veeraraghavan
Unfortunately, the measurement rate of a SPC is insufficient to enable imaging at high spatial and temporal resolutions.
1 code implementation • 27 Feb 2015 • Mayank Kumar, Ashok Veeraraghavan, Ashutosh Sabharval
The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios.
no code implementations • 1 Dec 2014 • Ali Ayremlou, Thomas Goldstein, Ashok Veeraraghavan, Richard Baraniuk
Sparse approximations using highly over-complete dictionaries is a state-of-the-art tool for many imaging applications including denoising, super-resolution, compressive sensing, light-field analysis, and object recognition.
no code implementations • 8 Aug 2013 • Kaushik Mitra, Oliver Cossairt, Ashok Veeraraghavan
Unfortunately, a detailed analysis of CI has proven to be a challenging problem because performance depends equally on three components: (1) the optical multiplexing, (2) the noise characteristics of the sensor, and (3) the reconstruction algorithm.