Kehtarnavaz, Nasser
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Nasser Kehtarnavaz is a professor in the Department of Electrical Engineering and was made an Erik Jonsson Distinguished Professor in 2017. He also serves as head of the Signal and Image Processing (SIP) Lab. His research interests include:
- Signal and image processing
- Real-time signal and image processing
- Real-time embedded processing
- Biomedical image analysis
- Pattern recognition
ORCID page
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Recent Submissions
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A Real-Time Smartphone App for Unsupervised Noise Classification In Realistic Audio Environments
(IEEE, 2019-03-07)This paper presents a real-time unsupervised noise classifier smartphone app which is designed to operate in realistic audio environments. This app addresses the two limitations of a previously developed smartphone app for ... -
Convolutional Autoencoder-Based Multispectral Image Fusion
This paper presents a deep learning-based pansharpening method for fusion of panchromatic and multispectral images in remote sensing applications. This method can be categorized as a component substitution method in which ... -
A Computationally Efficient Pipeline for 3d Point Cloud Reconstruction from Video Sequences
This paper presents a computationally efficient pipeline to achieve 3D point cloud reconstruction from video sequences. This pipeline involves a key frame selection step to improve the computational efficiency by generating ... -
A Convolutional Neural Network-Based Sensor Fusion System for Monitoring Transition Movements in Healthcare Applications
This paper presents a convolutional neural network-based sensor fusion system to monitor six transition movements as well as falls in healthcare applications by simultaneously using a depth camera and a wearable inertial ... -
A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection
This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with ... -
Multi-Temporal Depth Motion Maps-Based Local Binary Patterns for 3-D Human Action Recognition
This paper presents a local spatio-temporal descriptor for action recognistion from depth video sequences, which is capable of distinguishing similar actions as well as coping with different speeds of actions. This descriptor ... -
Optimization Method to Reduce Blocking Artifacts in JPEG Images
This paper presents an optimization method to reduce blocking artifacts in JPEG images by utilizing the image gradient information. A closed-form solution is derived for the optimization method. To address the computational ... -
Comparison of Two Real-Time Hand Gesture Recognition Systems Involving Stereo Cameras, Depth Camera, and Inertial Sensor
This paper presents a comparison of two real-time hand gesture recognition systems. One system utilizes a binocular stereo camera set-up while the other system utilizes a combination of a depth camera and an inertial sensor. ...
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