9 publications with the keyword "deep learning"


E. Ufuktepe, V. Ramtekkar, K. Gao, N. Al-Shakarji, J. Fraser, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

pyTAG: Python-based Interactive Training Data Generation for Visual Tracking Algorithms

Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing), Volume 11398, 2020

ground-truth generation, visual tracking, crowd sourcing, annotation, deep learning

Abstract, Bibtex, PlainText, Software, URL, DOI, Google Scholar
Y. M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, and K. Palaniappan

Deep U-Net Regression and Hand-Crafted Feature Fusion for Accurate Blood Vessel Segmentation

2019 IEEE International Conference on Image Processing (ICIP), pgs. 1445-1449, 2019

semantic vessel segmentation, deep learning, histogram equalization, random forests, u-net

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar
G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. E. Lever, and F. Bunyak

Facial expression recognition for monitoring neurological disorders based on convolutional neural network

Multimedia Tools and Applications, Volume 78, pgs. 31581-31603, 2019

facial component segmentation, facial expression recognition, convolutional neural network, deep learning

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar
Z. A. Oraibi, H. Yousif, A. Hafiane, G. Seetharaman, and K. Palaniappan

Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests

25th IEEE International Conference on Image Processing (ICIP), pgs. 2446-2450, 2018

feature extraction, random forests, local features, deep learning, image classification

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar
#196: Y. M. Kassim and K. Palaniappan

Extracting Retinal Vascular Networks Using Deep Learning Architecture

IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017

deep learning, vessel segmentation, fundoscopy images, patches, convolution neural network

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar
#192: G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. Lever, and F. Bunyak

Deep learning-based facial expression recognition for monitoring neurological disorders

EEE International Conference on Bioinformatics and Biomedicine (BIBM) , pgs. 1652-1657, 2017

convolutional neural networks, deep learning, facial component segmentation, facial expression recognition

Abstract, Bibtex, PlainText, URL, Google Scholar
#181: Y. M. Kassim, V. B. S. Prasath, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan

Microvasculature segmentation of arterioles using deep CNN

IEEE International Conference on Image Processing (ICIP), 2017

deep learning, segmentation, vasculature, biomedical

Abstract, Bibtex, PlainText, URL, Google Scholar
#176: Z. Liang, S. Jaeger, G. Thoma, J. Huang, P. Guo, A. Powell, K. Silamut, I. Ersoy, M. Poostchi, K. Palaniappan, and R. J. Maude

CNN - Based Image Analysis for Malaria Diagnosis

Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016

deep learning, malaira, cells, classification, biomedical

Abstract, Bibtex, PlainText, Google Scholar
#173: Z. Al-Milaji, I. Ersoy, K. Palaniappan, and F. Bunyak

Hybrid Deep Learning Framework for the Classification of Epithelial and Stromal Tissues in H&E Images

Proc. First International Workshop on Deep Learning for Pattern Recognition (DLPR), 2016

deep learning, histopathology, segmentation

Bibtex, PlainText, URL, Google Scholar