no code implementations • 25 May 2023 • Akash Awasthi, Son Ly, Jaer Nizam, Samira Zare, Videet Mehta, Safwan Ahmed, Keshav Shah, Ramakrishna Nemani, Saurabh Prasad, Hien Van Nguyen
The definition of anomaly detection is the identification of an unexpected event.
no code implementations • 10 Mar 2022 • Hadi Abdullah, Aditya Karlekar, Saurabh Prasad, Muhammad Sajidur Rahman, Logan Blue, Luke A. Bauer, Vincent Bindschaedler, Patrick Traynor
We begin by comparing 20 recent attack papers, classifying and measuring their suitability to serve as the basis of new "robust to transcription" but "easy for humans to understand" CAPTCHAs.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 16 Jul 2020 • Xiong Zhou, Saurabh Prasad
Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery and video.
no code implementations • 18 Jul 2016 • Minshan Cui, Saurabh Prasad
Based on these developments, a sparse representation-based classification (SRC) has been proposed for a variety of classification and related tasks, including face recognition.
no code implementations • 18 Jul 2016 • Saurabh Prasad, Minshan Cui, Lifeng Yan
With the emergence of passive and active optical sensors available for geospatial imaging, information fusion across sensors is becoming ever more important.
no code implementations • 15 Jul 2016 • Saurabh Prasad, Tanu Priya, Minshan Cui, Shishir Shah
Specifically, we assert that by accurately characterizing the unique spectral signature for each person's skin, hyperspectral imagery can provide very useful descriptors (e. g. spectral signatures from skin pixels) for re-identification.
no code implementations • 15 Jul 2016 • Minshan Cui, Saurabh Prasad
Additionally, we also propose a sparse representation based classifier which is optimized to exploit spatial information during classification - we hence assert that our proposed projection is particularly suitable for classifiers where local similarity and spatial context are both important.