no code implementations • 12 Feb 2024 • Sabyasachi Ghosh, Ajit Rajwade
This impedes recovery of signals which may have sparse representations in the GFT bases of the ground truth graph.
no code implementations • 5 Nov 2023 • Kashish Mittal, Harsh Shah, Ajit Rajwade
Unlike other methods, it does not assume a large difference between the cosine similarity of the query vector with the least related neighbor and that with the least unrelated non-neighbor.
no code implementations • 4 Sep 2023 • Garweet Sresth, Ajit Rajwade, Satish Mulleti
We present an estimator to solve for the unknown vector.
no code implementations • 12 May 2023 • Sabyasachi Ghosh, Sanyam Saxena, Ajit Rajwade
The computational cost of running the QMPNN and the CS algorithms is significantly lower than the cost of using a neural network with the same number of parameters separately on each image to classify the images, which we demonstrate via extensive experiments.
no code implementations • 18 Apr 2023 • Pranava Singhal, Waqar Mirza, Ajit Rajwade, Karthik S. Gurumoorthy
In this paper, we describe a method for estimating the joint probability density from data samples by assuming that the underlying distribution can be decomposed as a mixture of product densities with few mixture components.
no code implementations • 13 Apr 2023 • Sheel Shah, Karthik S. Gurumoorthy, Ajit Rajwade
More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given just the distribution of the sample locations and their ordering in 1D.
no code implementations • 7 Nov 2022 • Shu-Jie Cao, Ritesh Goenka, Chau-Wai Wong, Ajit Rajwade, Dror Baron
These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples.
1 code implementation • 25 Oct 2022 • Jerin Geo James, Devansh Jain, Ajit Rajwade
However, in this work, we introduce a more general representation scheme, which adapts any existing optical flow network to ignore the moving objects and obtain a spatially smooth approximation of the global motion between video frames.
no code implementations • 3 Mar 2022 • Shaan ul Haque, Ajit Rajwade, Karthik S. Gurumoorthy
We create a dictionary of various families of distributions by inspecting the data, and use it to approximate each decomposed factor of the product in the mixture.
no code implementations • 6 Oct 2021 • Ameya Anjarlekar, Ajit Rajwade
As compared to using randomly generated sensing matrices, optimizing the sensing matrix w. r. t.
no code implementations • 22 Mar 2021 • Jian Vora, Karthik S. Gurumoorthy, Ajit Rajwade
Joint probability mass function (PMF) estimation is a fundamental machine learning problem.
no code implementations • 8 Feb 2021 • Shuvayan Banerjee, Radhe Srivastava, Ajit Rajwade
Most compressed sensing algorithms do not account for the effect of saturation in noisy compressed measurements, though saturation is an important consequence of the limited dynamic range of existing sensors.
1 code implementation • 16 May 2020 • Sabyasachi Ghosh, Rishi Agarwal, Mohammad Ali Rehan, Shreya Pathak, Pratyush Agrawal, Yash Gupta, Sarthak Consul, Nimay Gupta, Ritika, Ritesh Goenka, Ajit Rajwade, Manoj Gopalkrishnan
Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data.
no code implementations • 23 Dec 2019 • Preeti Gopal, Sharat Chandran, Imants Svalbe, Ajit Rajwade
Our results on 3D data show that prior information can be used to compensate for the low-dose artefacts, and we demonstrate that it is possible to simultaneously prevent the prior from adversely biasing the reconstructions of new changes in the test object, via a method called ``re-irradiation''.
no code implementations • 11 Sep 2019 • Preeti Gopal, Sharat Chandran, Imants Svalbe, Ajit Rajwade
While this is easily feasible when measurements are acquired from a large number of projection views, it is challenging when the number of views is limited.
1 code implementation • ICCV 2019 • Jerin Geo James, Pranay Agrawal, Ajit Rajwade
Surprisingly, we demonstrate that PEOF is more efficient and often outperforms all the state of the art methods in terms of numerical measures.
no code implementations • 23 Dec 2018 • Preeti Gopal, Sharat Chandran, Imants Svalbe, Ajit Rajwade
The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical.
no code implementations • 6 Dec 2017 • Preeti Gopal, Ritwick Chaudhry, Sharat Chandran, Imants Svalbe, Ajit Rajwade
Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements.
no code implementations • 7 Sep 2016 • Alankar Kotwal, Ajit Rajwade
There exist several applications in image processing (eg: video compressed sensing [Hitomi, Y. et al, "Video from a single coded exposure photograph using a learned overcomplete dictionary"] and color image demosaicing [Moghadam, A.
no code implementations • CVPR 2013 • Yu-Tseh Chi, Mohsen Ali, Ajit Rajwade, Jeffrey Ho
This paper proposes a dictionary learning framework that combines the proposed block/group (BGSC) or reconstructed block/group (R-BGSC) sparse coding schemes with the novel Intra-block Coherence Suppression Dictionary Learning (ICS-DL) algorithm.