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Alejandro J. Cortese

Also published under: Alejandro Javier Cortese, Alejandro Cortese

Publications

Citations

111

Publications by Year

20182021

Co-Authors:

Elizabeth Rose AggerThomas A. ClelandShelley FriedAasta Parin GandhiShahaboddin Ghajari

Show All Co-Authors (22)

Alejandro J. Cortese

Also published under: Alejandro Javier Cortese, Alejandro Cortese

Affiliation

Department of Electrical and Computer Engineering

Cornell University

Ithaca, NY, USA

Publication Topics

Optical Power,
Atomic Layer Deposition,
Heterogeneous Integration,
Clock Cycles,
Light Pulses,
Neural Recordings,
Optical Pulse,
Power Consumption,
Record Neural Activity,
Attenuation Length,
Autonomic System,
Base Pressure

Biography

Alejandro Javier Cortese received the B.S. degree (Hons.) in physics and the B.S. degree in mathematics from Duke University, Durham, NC, USA, in 2012, and the M.S and Ph.D. degrees in physics from Cornell University, Ithaca, NY, USA, in 2015 and 2019, respectively. He is currently a Presidential Postdoctoral Fellow with Cornell University, working on wireless optoelectronic sensors.(Based on document published on 12 June 2020).

Publications

Citations

111

Publications by Year

20182021

Co-Authors:

Elizabeth Rose Agger
Thomas A. Cleland
Shelley Fried
Aasta Parin Gandhi
Shahaboddin Ghajari

Show All Co-Authors (22)

Author's Published Works

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Showing 1-3 of 3 results

Conferences (3)

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Shanmuganathan Raman(3)
Zeeshan Khan(3)
Mukul Khanna(2)
Parth Shettiwar(1)
Aditya Vora(1)
Georgia Tech(1)
CVIT, IIIT Hyderabad, India(1)
CVIG Lab, IIT Gandhinagar, India(1)
Honeywell Technology Solutions, Bangalore, India(1)
Indian Institute of Technology, Gandhinagar, India(1)
2022 26th International Conference on Pattern Recognition (ICPR)(2)
2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)(1)
IEEE(3)
Media(2)
Montreal, QC, Canada(2)
Ottawa, ON, Canada(1)
Convolutional Neural Network(2)
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High Dynamic Range(2)
High Dynamic Range Image(2)
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DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction

Zeeshan Khan;Parth Shettiwar;Mukul Khanna;Shanmuganathan Raman

2022 26th International Conference on Pattern Recognition (ICPR)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Due to hardware constraints, standard off-the-shelf digital cameras suffers from low dynamic range (LDR) and low frame per second (FPS) outputs. Previous works in high dynamic range (HDR) video reconstruction uses sequence of alternating exposure LDR frames as input, and align the neighbouring frames using optical flow based networks. However, these methods often result in motion artifacts in chal...Show More

DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction

Zeeshan Khan;Parth Shettiwar;Mukul Khanna;Shanmuganathan Raman

2022 26th International Conference on Pattern Recognition (ICPR)

Year: 2022 | Conference Paper |

Deep Appearance Consistent Human Pose Transfer

Ashish Tiwari;Zeeshan Khan;Aditya Vora;Manjuprakash Rama Rao;Shanmuganathan Raman

2022 26th International Conference on Pattern Recognition (ICPR)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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The fidelity of a pose transfer system depends on its ability to generate realistic images of a person under novel poses while preserving the desired human attributes (like face, hairstyle, and clothes). However, the visual fidelity is often compromised as the existing methods fail to extract rich appearance and pose features since they propagate the pose and the appearance information through the...Show More

Deep Appearance Consistent Human Pose Transfer

Ashish Tiwari;Zeeshan Khan;Aditya Vora;Manjuprakash Rama Rao;Shanmuganathan Raman

2022 26th International Conference on Pattern Recognition (ICPR)

Year: 2022 | Conference Paper |

FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network

Zeeshan Khan;Mukul Khanna;Shanmuganathan Raman

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Year: 2019 | Conference Paper |

Cited by: Papers (40) | Patents (1)

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High dynamic range (HDR) image generation from a single exposure low dynamic range (LDR) image has been made possible due to the recent advances in Deep Learning. Various feed-forward Convolutional Neural Networks (CNNs) have been proposed for learning LDR to HDR representations. To better utilize the power of CNNs, we exploit the idea of feedback, where the initial low level features are guided b...Show More

FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network

Zeeshan Khan;Mukul Khanna;Shanmuganathan Raman

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Year: 2019 | Conference Paper |

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