Search Results for author:
Found 9 papers, 2 papers with code
VisIRNet: Deep Image Alignment for UAV-taken Visible and Infrared Image Pairs
We propose two variants of our approach, where in the first variant (ModelA), we directly predict the new coordinates of only the four corners of the image to be aligned; and in the second one (ModelB), we predict the homography matrix directly.
GRJointNET: Synergistic Completion and Part Segmentation on 3D Incomplete Point Clouds
On the other hand, our proposed solution, GRJointNet, is an architecture that can perform joint completion and segmentation on point clouds as a successor of GRNet.
A Hybrid 3D Eddy Detection Technique Based on Sea Surface Height and Velocity Field
In this paper, we introduce a hybrid eddy detection approach that combines sea surface height (SSH) and velocity fields with geometric criteria defining eddy behavior.
SyNet: An Ensemble Network for Object Detection in UAV Images
Recent advances in camera equipped drone applications and their widespread use increased the demand on vision based object detection algorithms for aerial images.
Deep Receiver Design for Multi-carrier Waveforms Using CNNs
In this paper, a deep learning based receiver is proposed for a collection of multi-carrier wave-forms including both current and next-generation wireless communication systems.
Parametric Shape Modeling and Skeleton Extraction with Radial Basis Functions using Similarity Domains Network
In this paper, we demonstrate how using SDN can first help us model a pixel-based image in terms of SDs and then demonstrate how those learned SDs can be used to extract the skeleton of a shape.
Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks
A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions.
Coresets for Vector Summarization with Applications to Network Graphs
We provide a deterministic data summarization algorithm that approximates the mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in $\REAL^d$, by a weighted mean $\tilde{p}$ of a \emph{subset} of $O(1/\eps)$ vectors, i. e., independent of both $n$ and $d$.
