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Nasser Kehtarnavaz
University of Texas at Dallas
360Publications
32H-index
4,050Citations
Publications 360
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#2Nasser Kehtarnavaz (UTD: University of Texas at Dallas)H-Index: 32
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#1Meysam TavakoliH-Index: 9
Last.Nasser KehtarnavazH-Index: 32
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This paper presents a computationally efficient method for the detection of optic nerve head in both color fundus and fluorescein angiography images. It involves a combination of Radon transformation and multi-overlapping windows within an optimization framework in order to achieve a robust detection in the presence of various structural, color, and intensity variations in such images. Three databases have been examined and it is shown that the introduced method provides high detection rates whi...
#1Haoran WeiH-Index: 1
#2Roozbeh JafariH-Index: 32
Last.Nasser KehtarnavazH-Index: 32
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This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D co...
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#1Haoran WeiH-Index: 1
#2Nasser KehtarnavazH-Index: 32
This paper presents a semi-supervised faster region-based convolutional neural network (SF-RCNN) approach to detect persons and to classify the load carried by them in video data captured from distances several miles away via high-power lens video cameras. For detection, a set of computationally efficient image processing steps are considered to identify moving areas that may contain a person. These areas are then passed onto a faster RCNN classifier whose convolutional layers consist of ResNet5...
1 CitationsSource
#1Arian Azarang (UTD: University of Texas at Dallas)H-Index: 3
#2John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
Last.Nasser Kehtarnavaz (UTD: University of Texas at Dallas)H-Index: 32
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This paper presents combining two data augmentation methods involving speed perturbation and room impulse response reverberation for the purpose of improving the generalization capability of convolutional neural networks when used for voice command recognition. Speed perturbation generates voice command variations caused by shorter or longer time durations of commands spoken by different speakers. Room impulse response reverberation generates voice command variations caused by reflected sound pa...
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#1Haoran WeiH-Index: 1
#2Abhishek SehgalH-Index: 5
Last.Nasser KehtarnavazH-Index: 32
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May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Nasim AlamdariH-Index: 4
#2Edward Lobarinas (UTD: University of Texas at Dallas)H-Index: 21
Last.Nasser KehtarnavazH-Index: 32
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This paper presents an educational tool to learn about how hearing aid compression fitting is prescribed from a signal processing perspective. An interactive web-based program has been developed based on the widely used DSL-v5 fitting rationale. This program can be accessed and used from any internet browser to generate the parameters of compression curves that correspond to the nine frequency bands used in DSL-v5. These parameters are then transferred to a smartphone in the form of a datafile t...
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#1Abhishek SehgalH-Index: 5
#2Nasser KehtarnavazH-Index: 32
Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uni...
3 CitationsSource
This paper presents a self-supervised deep neural network solution to speech denoising by easing the requirement that clean speech signals need to be available for network training. This self-supervised approach is based on training a Fully Convolutional Neutral Network to map a noisy speech signal to another noisy version of the speech signal. To show the effectiveness of the developed approach, four commonly used objective performance measures are used to compare the self-supervised approach t...
#1Arian Azarang (UTD: University of Texas at Dallas)H-Index: 3
#2Hafez Eslami Manoochehri (UTD: University of Texas at Dallas)H-Index: 2
Last.Nasser Kehtarnavaz (UTD: University of Texas at Dallas)H-Index: 32
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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 convolutional autoencoder network is trained to generate original panchromatic images from their spatially degraded versions. Low resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high resolution multispectra...
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