226 citations · 729 across the 62 of their papers we have counts for
18 papers · 1 filter
Deep learning powered real-time identification of insects using citizen science data
Shivani Chiranjeevi, Mojdeh Sadaati, Zi K Deng +10
Insect-pests significantly impact global agricultural productivity and quality. Effective management involves identifying the full insect community, including beneficial insects an…
Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos
Md Zahid Hasan, Jiajing Chen, Jiyang Wang +6
Recognizing the activities causing distraction in real-world driving scenarios is critical for ensuring the safety and reliability of both drivers and pedestrians on the roadways.…
Out-of-distribution detection algorithms for robust insect classification
Mojdeh Saadati, Aditya Balu, Shivani Chiranjeevi +5
Deep learning-based approaches have produced models with good insect classification accuracy; Most of these models are conducive for application in controlled environmental conditi…
Concept Activation Vectors for Generating User-Defined 3D Shapes
Stefan Druc, Aditya Balu, Peter Wooldridge +2
We explore the interpretability of 3D geometric deep learning models in the context of Computer-Aided Design (CAD). The field of parametric CAD can be limited by the difficulty of…
A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment
Homagni Saha, Fateme Fotouhif, Qisai Liu +1
In this paper we propose a new framework - MoViLan (Modular Vision and Language) for execution of visually grounded natural language instructions for day to day indoor household ta…
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
Luis G Riera, Matthew E. Carroll, Zhisheng Zhang +8
Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop…
Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras
Homagni Saha, Sin Yong Tan, Ali Saffari +3
Reliable detection of human occupancy in indoor environments is critical for various energy efficiency, security, and safety applications. We consider this challenge of occupancy d…
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping
Koushik Nagasubramanian, Asheesh K. Singh, Arti Singh +2
Deep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training mo…
How useful is Active Learning for Image-based Plant Phenotyping?
Koushik Nagasubramanian, Talukder Z. Jubery, Fateme Fotouhi Ardakani +5
Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, succ…
Driver Behavior Analysis Using Lane Departure Detection Under Challenging Conditions
Luis Riera, Koray Ozcan, Jennifer Merickel +3
In this paper, we present a novel model to detect lane regions and extract lane departure events (changes and incursions) from challenging, lower-resolution videos recorded with mo…
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar +1
Deep neural networks have been shown to exhibit an intriguing vulnerability to adversarial input images corrupted with imperceptible perturbations. However, the majority of adversa…
Multi-level 3D CNN for Learning Multi-scale Spatial Features
Sambit Ghadai, Xian Lee, Aditya Balu +2
3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, su…
Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps
Koushik Nagasubramanian, Sarah Jones, Asheesh K. Singh +3
Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that aff…
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean
Koushik Nagasubramanian, Sarah Jones, Soumik Sarkar +3
Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, au…
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
Aditya Balu, Sambit Ghadai, Kin Gwn Lore +3
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn…
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection
Adedotun Akintayo, Nigel Lee, Vikas Chawla +7
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to…
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar
In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredi…
Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks
Soumik Sarkar, Vivek Venugopalan, Kishore Reddy +3
Occlusion edges in images which correspond to range discontinuity in the scene from the point of view of the observer are an important prerequisite for many vision and mobile robot…