Search Results for author:
Found 11 papers, 4 papers with code
Edge Device Deployment of Multi-Tasking Network for Self-Driving Operations
A safe and robust autonomous driving system relies on accurate perception of the environment for application-oriented scenarios.
FV-UPatches: Enhancing Universality in Finger Vein Recognition
In the proposed framework, the domain mapper is an approximation to a specific extraction function thus the training is only a one-time effort with limited data.
DC2Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
In this paper, we propose a method for estimating lumbar spine MR images based on CT images using a novel objective function and a dual cycle-consistent adversarial network (DC2Anet) with semi-supervised learning.
Mixture separability loss in a deep convolutional network for image classification
MSL consists of between-class and within-class loss.
Blind Deconvolution Method using Omnidirectional Gabor Filter-based Edge Information
In the previous blind deconvolution methods, de-blurred images can be obtained by using the edge or pixel information.
Real-time and robust multiple-view gender classification using gait features in video surveillance
This paper proposes a novel method for classifying human gender in real time using gait information.
Deep CT to MR Synthesis using Paired and Unpaired Data
To improve the accuracy of CT-based radiotherapy planning, we propose a synthetic approach that translates a CT image into an MR image using paired and unpaired training data.
End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module
The proposed CNN structure uses the fire module as the base model and uses the gram module for texture extraction.
Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold
This study proposes a patch-based fake fingerprint detection method using a fully convolutional neural network with a small number of parameters and an optimal threshold to solve the above-mentioned problem.
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
The proposed approach achieved a mean average precision (mAP) of 76. 6% at the frame-based and 83. 5% at the video-based measurement on the new large-scale ICVL video surveillance dataset that the authors introduce and make available to the community with this paper.
Wide-Residual-Inception Networks for Real-time Object Detection
Since convolutional neural network(CNN)models emerged, several tasks in computer vision have actively deployed CNN models for feature extraction.
