986 citations · 1.1k across the 22 of their papers we have counts for
6 papers · 2 filters
Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification
Yilun Cao, Hyungtae Lee, Heesung Kwon
In this paper, we introduce a novel fusion method that can enhance object detection performance by fusing decisions from two different types of computer vision tasks: object detect…
Exploitation of Semantic Keywords for Malicious Event Classification
Hyungtae Lee, Sungmin Eum, Joel Levis +3
Learning an event classifier is challenging when the scenes are semantically different but visually similar. However, as humans, we typically handle such tasks painlessly by adding…
DTM: Deformable Template Matching
Hyungtae Lee, Heesung Kwon, Ryan M. Robinson +1
A novel template matching algorithm that can incorporate the concept of deformable parts, is presented in this paper. Unlike the deformable part model (DPM) employed in object reco…
Fast Object Localization Using a CNN Feature Map Based Multi-Scale Search
Hyungtae Lee, Heesung Kwon, Archith J. Bency +1
Object localization is an important task in computer vision but requires a large amount of computational power due mainly to an exhaustive multiscale search on the input image. In…
Going Deeper with Contextual CNN for Hyperspectral Image Classification
Hyungtae Lee, Heesung Kwon
In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike…
Weakly Supervised Localization using Deep Feature Maps
Archith J. Bency, Heesung Kwon, Hyungtae Lee +2
Object localization is an important computer vision problem with a variety of applications. The lack of large scale object-level annotations and the relative abundance of image-lev…