Filiz Bunyak - IEEE Xplore Author Profile

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Author details

Filiz Bunyak

Also published under: F. Bunyak

Publications

71

Citations

972

Publications by Year

19982024

Co-Authors:

Oliver AcataySanjeev AgarwalH. AkbarpourRumana AktarZahraa Al-Milaji

Show All Co-Authors (226)

Filiz Bunyak

Also published under: F. Bunyak

Affiliation

University of Missouri, Columbia, USA

Publication Topics

Scanning Electron Microscopy,
Deep Learning,
Synthetic Images,
Carbon Nanotubes,
Deep Network,
Generative Adversarial Networks,
Image Segmentation,
Machine Learning,
Neural Network,
Scanning Electron Microscopy Images,
Training Set,
Computer Vision

Biography

Filiz Bunyak received the Ph.D. from the University of Missouri at Rolla. She is currently an Assistant Research Professor with the Computer Science Department, University of Missouri. Her research interests include image processing, computer vision, and pattern recognition with emphasis on biomedical image analysis, aerial and wide-area surveillance, visual tracking, data fusion, segmentation, and level set methods.(Based on document published on 7 December 2020).

Publications

71

Citations

972

Publications by Year

19982024

Co-Authors:

Oliver Acatay
Sanjeev Agarwal
H. Akbarpour
Rumana Aktar
Zahraa Al-Milaji

Show All Co-Authors (226)

Author's Published Works

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Showing 1-25 of 71 results

Conferences (70)

Journals (1)

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Filiz Bunyak(59)
Kannappan Palaniappan(44)
Guna Seetharaman(19)
F. Bunyak(12)
K. Palaniappan(9)
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA(11)
Department of Computer Science, University of Missouri, Columbia, MO, USA(7)
Advanced Computing Concepts, U.S. Naval Research Laboratory, Washington, DC, USA(4)
Department of Computer Science, University of Missouri-Columbia, MO, USA(4)
Department of Computer Science, University of Missouri, Columbia, Columbia, MO, USA(4)
2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)(3)
2020 25th International Conference on Pattern Recognition (ICPR)(3)
2020 IEEE International Conference on Image Processing (ICIP)(3)
2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)(3)
2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)(3)
IEEE(71)
Media(1)
Washington, DC, USA(12)
Abu Dhabi, United Arab Emirates(3)
Milan, Italy(3)
St. Louis, MO, USA(3)
Auckland, New Zealand(2)
Deep Learning(19)
Object Detection(14)
Video Analysis(11)
Active Contour(10)
Bounding Box(10)

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Results

Learning-based Image Analytics in User-AI Agent Interactions for Cyber-enabled Manufacturing

Mauro Lemus Alarcon;Upasana Roy;Anirudh Kambhampati;Nguyen Nguyen;Minasadat Attari;Ramakrishna Surya;Filiz Bunyak;Matthew Maschmann;Kannappan Palaniappan;Prasad Calyam

2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)

Year: 2024 | Conference Paper |

Cited by: Papers (4)

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Manual trial-and-error methods are employed for image parameter selection decisions in the processes that control cyber-enabled scientific instruments. Particularly in materials manufacturing use cases, where image analytics can be iterative, time-consuming and prone to errors, there is a need to enhance existing processes by using agents featuring learning algorithms that recommend image analytic...Show More

Learning-based Image Analytics in User-AI Agent Interactions for Cyber-enabled Manufacturing

Mauro Lemus Alarcon;Upasana Roy;Anirudh Kambhampati;Nguyen Nguyen;Minasadat Attari;Ramakrishna Surya;Filiz Bunyak;Matthew Maschmann;Kannappan Palaniappan;Prasad Calyam

2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)

Year: 2024 | Conference Paper |

Creating semi-Quanta multi-layer synthetic CNT images using CycleGAN

Kaveh Safavigerdini;Ramakrishna Surya;Andrew Reinhard;Zach Quinlan;Filiz Bunyak;Matthew R. Maschmann;Kannappan Palaniappan

2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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We address the challenge of creating semi-Quanta images of vertically-oriented carbon nanotubes (CNT). Our previous method, CNTNeXt, successfully predicted the mechanical properties for semi-Scanning Electron Microscopy (SEM) images of CNT forests. However, for Quanta images, a more sophisticated approach is required. We propose a novel pipeline that modifies multi-layer synthetic (MLS) CNT images...Show More

Creating semi-Quanta multi-layer synthetic CNT images using CycleGAN

Kaveh Safavigerdini;Ramakrishna Surya;Andrew Reinhard;Zach Quinlan;Filiz Bunyak;Matthew R. Maschmann;Kannappan Palaniappan

2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2023 | Conference Paper |

Multi-Loss Topology-Aware Deep Learning Network for Segmentation of Vessels in Microscopy Images

Minasadat Attari;Nguyen P. Nguyen;Kannappan Palaniappan;Filiz Bunyak

2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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Accurate segmentation of microvasculature is vital for the analysis of vascular networks. Common deep learning networks using pixel-level loss functions like binary cross-entropy (BCE) or dice are effective for general segmentation but face challenges with thin curvilinear structures that are prone to topology errors. Accurate segmentation of the retinal vessels is essential for precise morphologi...Show More

Multi-Loss Topology-Aware Deep Learning Network for Segmentation of Vessels in Microscopy Images

Minasadat Attari;Nguyen P. Nguyen;Kannappan Palaniappan;Filiz Bunyak

2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2023 | Conference Paper |

Deep Style Transfer for Generation of Photo-realistic Synthetic Images of CNT Forests

Prashanth Reddy Kotha;Minasadat Attari;Matthew Maschmann;Filiz Bunyak

2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2023 | Conference Paper |

Cited by: Papers (6)

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Carbon nanotubes (CNTs) are promising nano-materials with diverse applications in various fields, ranging from electronics and energy storage to biomedical applications. Characterization of CNT forest structures and prediction of material properties through image analytics are critical for new material design and discovery. Artificial intelligence and machine learning (AI/ML) driven approaches off...Show More

Deep Style Transfer for Generation of Photo-realistic Synthetic Images of CNT Forests

Prashanth Reddy Kotha;Minasadat Attari;Matthew Maschmann;Filiz Bunyak

2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2023 | Conference Paper |

DFT-CAM: Discrete Fourier Transform Driven Class Activation Map

Yang Yang Wang;Filiz Bunyak

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Class activation map methods (CAMs) serve one of the key roles in explainable artificial intelligence (XAI) and are recently being applied to weakly-supervised object localization, showing great potential in many applications. However, the current CAM methods still have room for improvement regarding the weakly-supervised object localization task. In this paper, we proposed DFT-CAM, a novel CAM me...Show More

DFT-CAM: Discrete Fourier Transform Driven Class Activation Map

Yang Yang Wang;Filiz Bunyak

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

Predicting Mechanical Properties of Carbon Nanotube (CNT) Images Using Multi-Layer Synthetic Finite Element Model Simulations

Kaveh Safavigerdini;Koundinya Nouduri;Ramakrishna Surya;Andrew Reinhard;Zach Quinlan;Filiz Bunyak;Matthew R. Maschmann;Kannappan Palaniappan

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

Cited by: Papers (12)

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We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. Our approach incorporates an innovative data augmentation technique that involves the use of multi-layer synthetic (MLS) or quasi-2.5D images which are generated by blending 2D synthetic images....Show More

Predicting Mechanical Properties of Carbon Nanotube (CNT) Images Using Multi-Layer Synthetic Finite Element Model Simulations

Kaveh Safavigerdini;Koundinya Nouduri;Ramakrishna Surya;Andrew Reinhard;Zach Quinlan;Filiz Bunyak;Matthew R. Maschmann;Kannappan Palaniappan

2023 IEEE International Conference on Image Processing (ICIP)

Year: 2023 | Conference Paper |

CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition in SEM Imagery using Generative Adversarial Networks

Nguyen P. Nguyen;Ramakrishna Surya;Prasad Calyam;Kannappan Palaniappan;Matthew Maschmann;Filiz Bunyak

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further...Show More

CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition in SEM Imagery using Generative Adversarial Networks

Nguyen P. Nguyen;Ramakrishna Surya;Prasad Calyam;Kannappan Palaniappan;Matthew Maschmann;Filiz Bunyak

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Axon and Myelin Sheath Segmentation in Electron Microscopy Images using Meta Learning

Nguyen P. Nguyen;Stephanie Lopez;Catherine L. Smith;Teresa E. Lever;Nicole L. Nichols;Filiz Bunyak

2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2022 | Conference Paper |

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Various neurological diseases affect the morphology of myelinated axons. Quantitative analysis of these structures and changes occurring due to neurodegeneration or neuroregeneration is of great importance for characterization of disease state and treatment response. This paper proposes a robust, meta-learning based pipeline for segmentation of axons and surrounding myelin sheaths in electron micr...Show More

Axon and Myelin Sheath Segmentation in Electron Microscopy Images using Meta Learning

Nguyen P. Nguyen;Stephanie Lopez;Catherine L. Smith;Teresa E. Lever;Nicole L. Nichols;Filiz Bunyak

2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2022 | Conference Paper |

Remote Instrumentation Science Environment for Intelligent Image Analytics

Mauro Lemus Alarcon;Songjie Wang;Nguyen Nguyen;Ashish Pandey;Filiz Bunyak;Matthew Maschmann;Kannappan Palaniappan;Prasad Calyam

2022 IEEE 18th International Conference on e-Science (e-Science)

Year: 2022 | Conference Paper |

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Current scientific experiments frequently involve control of specialized instruments (e.g., scanning electron microscopes), image data collection from those instruments, and transfer of the data for processing at simulation centers. This process requires a “human-in-the-loop” to perform those tasks manually, which besides requiring a lot of effort and time, could lead to inconsistencies or errors....Show More

Remote Instrumentation Science Environment for Intelligent Image Analytics

Mauro Lemus Alarcon;Songjie Wang;Nguyen Nguyen;Ashish Pandey;Filiz Bunyak;Matthew Maschmann;Kannappan Palaniappan;Prasad Calyam

2022 IEEE 18th International Conference on e-Science (e-Science)

Year: 2022 | Conference Paper |

Multi-Modal and Multi-Scale Oral Diadochokinesis Analysis using Deep Learning

Yang Yang Wang;Ke Gaol;Ali Hamad;Brianna McCarthy;Ashley M. Kloepper;Teresa E. Lever;Filiz Bunyak

2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2021 | Conference Paper |

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Various neurological disorders such as Parkinson's disease (PD), stroke, amyotrophic lateral sclerosis (ALS), etc. cause oromotor dysfunctions resulting in significant speech and swallowing impairments. Assessment and monitoring of speech disorders offer effective and non-invasive opportunities for dif-ferential diagnosis and treatment monitoring of neurological disorders. Oral diadochokinesis (or...Show More

Multi-Modal and Multi-Scale Oral Diadochokinesis Analysis using Deep Learning

Yang Yang Wang;Ke Gaol;Ali Hamad;Brianna McCarthy;Ashley M. Kloepper;Teresa E. Lever;Filiz Bunyak

2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2021 | Conference Paper |

Mitochondria Instance Segmentation in Electron Microscopy Image Volumes using 3D Deep Learning Networks

Nguyen P. Nguyen;Tommi A. White;Filiz Bunyak

2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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Segmentation of mitochondria in electron mi-croscopy (EM) images is a challenging task due to complex shapes of mitochondria and other sub-cellular structures, background clutter, weak boundaries, low contrast, low signal-to-noise ratio, touching mitochondria, and large data size. For robust and accu-rate segmentation of individual mitochondria within an electron microscopy image volume, we propos...Show More

Mitochondria Instance Segmentation in Electron Microscopy Image Volumes using 3D Deep Learning Networks

Nguyen P. Nguyen;Tommi A. White;Filiz Bunyak

2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2021 | Conference Paper |

Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images

Yang Yang Wang;O. V. Glinskii;Filiz Bunyak;Kannappan Palaniappan

2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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Detection, segmentation, and quantification of microvascular structures are the main steps towards studying microvascular remodeling. Combined with appropriate staining, confocal microscopy imaging enables exploration of the full 3D anatomical characteristics of microvascular systems. Segmentation of confocal microscopy images is a challenging task due to complexity of anatomical structures, stain...Show More

Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images

Yang Yang Wang;O. V. Glinskii;Filiz Bunyak;Kannappan Palaniappan

2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2021 | Conference Paper |

Impact of Georegistration Accuracy on Wide Area Motion Imagery Object Detection and Tracking

Noor Al-Shakarji;Ke Gao;Filiz Bunyak;Hadi Aliakbarpour;Erik Blasch;Priya Narayaran;Guna Seetharaman;Kannappan Palaniappan

2021 IEEE 24th International Conference on Information Fusion (FUSION)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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Advances in sensor technologies and embedded low-power processing provide new opportunities for using Wide Area Motion Imagery (WAMI) across a spectrum of mapping and monitoring applications covering large geospatial areas for extended time periods. While significant developments have been made in video analytics for ground or low-altitude aerial videos, methods for WAMI have been limited due to l...Show More

Impact of Georegistration Accuracy on Wide Area Motion Imagery Object Detection and Tracking

Noor Al-Shakarji;Ke Gao;Filiz Bunyak;Hadi Aliakbarpour;Erik Blasch;Priya Narayaran;Guna Seetharaman;Kannappan Palaniappan

2021 IEEE 24th International Conference on Information Fusion (FUSION)

Year: 2021 | Conference Paper |

Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans

Nguyen P. Nguyen;Youngjin Yoo;Andrei Chekkoury;Eva Eibenberger;Thomas J. Re;Jyotipriya Das;Abishek Balachandran;Yvonne W. Lui;Pina C. Sanelli;Thomas J. Schroeppel;Uttam Bodanapally;Savvas Nicolaou;Tommi A. White;Filiz Bunyak;Dorin Comaniciu;Eli Gibson

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2021 | Conference Paper |

Cited by: Papers (3)

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Brain midline shift (MLS), demonstrated by imaging, is a qualitative and quantitative radiological feature which measures the extent of lateral shift of brain midline structures in response to mass effect caused by hematomas, tumors, abscesses or any other space occupying intracranial lesions. It can be used, with other parameters, to determine the urgency of neurosurgical interventions and to pre...Show More

Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans

Nguyen P. Nguyen;Youngjin Yoo;Andrei Chekkoury;Eva Eibenberger;Thomas J. Re;Jyotipriya Das;Abishek Balachandran;Yvonne W. Lui;Pina C. Sanelli;Thomas J. Schroeppel;Uttam Bodanapally;Savvas Nicolaou;Tommi A. White;Filiz Bunyak;Dorin Comaniciu;Eli Gibson

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2021 | Conference Paper |

DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking

Rina Bao;Noor M. Al-Shakarji;Filiz Bunyak;Kannappan Palaniappan

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2021 | Conference Paper |

Cited by: Papers (11)

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Accurate segmentation and tracking of cells in microscopy image sequences is extremely beneficial in clinical diagnostic applications and biomedical research. A continuing challenge is the segmentation of dense touching cells and deforming cells with indistinct boundaries, in low signal-to-noise-ratio images. In this paper, we present a dual-stream marker-guided network (DMNet) for segmentation of...Show More

DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking

Rina Bao;Noor M. Al-Shakarji;Filiz Bunyak;Kannappan Palaniappan

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Year: 2021 | Conference Paper |

Deep Realistic Novel View Generation for City-Scale Aerial Images

Koundinya Nouduri;Ke Gao;Joshua Fraser;Shizeng Yao;Hadi AliAkbarpour;Filiz Bunyak;Kannappan Palaniappan

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Cited by: Papers (4)

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In this paper we introduce a novel end-to-end framework for generation of large, aerial, city-scale, realistic synthetic image sequences with associated accurate and precise camera metadata. The two main purposes for this data are (i) to enable objective, quantitative evaluation of computer vision algorithms and methods such as feature detection, description, and matching or full computer vision p...Show More

Deep Realistic Novel View Generation for City-Scale Aerial Images

Koundinya Nouduri;Ke Gao;Joshua Fraser;Shizeng Yao;Hadi AliAkbarpour;Filiz Bunyak;Kannappan Palaniappan

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation

Gani Rahmon;Filiz Bunyak;Guna Seetharaman;Kannappan Palaniappan

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Cited by: Papers (35)

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Detection of moving objects is a critical component of many computer vision tasks. Recently, deep learning architectures have been developed for supervised learning based moving object change detection. Some top performing architectures, like FgSegNet are single frame spatial appearance cue-based detection and tend to overfit to the training videos. We propose a novel compact multi-cue autoencoder...Show More

Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation

Gani Rahmon;Filiz Bunyak;Guna Seetharaman;Kannappan Palaniappan

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map

Md Maruf Hossain Shuvo;Yasmin M. Kassim;Filiz Bunyak;Olga V. Glinskii;Leike Xie;Vladislav V. Glinsky;Virginia H. Huxley;Mahesh M. Thakkar;Kannappan Palaniappan

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Cited by: Papers (4)

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Characterizing the spatial relationship between blood vessel and lymphatic vascular structures, in the mice dura mater tissue, is useful for modeling fluid flows and changes in dynamics in various disease processes. We propose a new deep learning-based approach to fuse a set of multi-channel single-focus microscopy images within each volumetric z-stack into a single fused image that accurately cap...Show More

Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map

Md Maruf Hossain Shuvo;Yasmin M. Kassim;Filiz Bunyak;Olga V. Glinskii;Leike Xie;Vladislav V. Glinsky;Virginia H. Huxley;Mahesh M. Thakkar;Kannappan Palaniappan

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Local Feature Performance Evaluation for Structure-From-Motion and Multi-View Stereo Using Simulated City-Scale Aerial Imagery

Ke Gao;Hadi Aliakbarpour;Joshua Fraser;Koundinya Nouduri;Filiz Bunyak;Ricky Massaro;Guna Seetharaman;Kannappan Palaniappan

IEEE Sensors Journal

Year: 2021 | Volume: 21, Issue: 10 | Journal Article |

Cited by: Papers (15)

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Ubiquitous low cost multi-rotor and fixed wing drones or unmanned aerial vehicles (UAVs) have accelerated the need for reliable, robust, and scalable Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipelines suitable for a variety of flightpath trajectories especially in degraded environments. Feature tracking being a core part of SfM and MVS, is essential for multiview scene modeling and ...Show More

Local Feature Performance Evaluation for Structure-From-Motion and Multi-View Stereo Using Simulated City-Scale Aerial Imagery

Ke Gao;Hadi Aliakbarpour;Joshua Fraser;Koundinya Nouduri;Filiz Bunyak;Ricky Massaro;Guna Seetharaman;Kannappan Palaniappan

IEEE Sensors Journal

Year: 2021 | Volume: 21, Issue: 10 | Journal Article |

Evaluation of Different Decision Fusion Mechanisms for Robust Moving Object Detection

Gani Rahmon;Filiz Bunyak;Guna Seetharaman;Kannappan Palaniappan

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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A fundamental processing step in a wide variety of video processing pipelines is detection of moving objects. Moving object detection is a challenging task due to various environmental conditions such as illumination changes, shadows, glare, background clutter; foreground complexities such as occlusion, camouflage, complex motion behavior of the foreground objects; and imaging conditions such as l...Show More

Evaluation of Different Decision Fusion Mechanisms for Robust Moving Object Detection

Gani Rahmon;Filiz Bunyak;Guna Seetharaman;Kannappan Palaniappan

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2020 | Conference Paper |

Semi-automatic System for Rapid Annotation of Moving Objects in Surveillance Videos using Deep Detection and Multi-object Tracking Techniques

Noor M. AL-Shakarji;Ekincan Ufuktepe;Filiz Bunyak;Hadi Aliakbarpour;Guna Seetharaman;Kannappan Palaniappan

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

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With the recent advances in video sensor technologies, emergence of new applications associated with these technologies, and demand for automated video analytics have increased the need for ground-truth annotations. Researchers attempt to explore different methodologies and algorithms on different challenging datasets. Ground-truth annotations are needed for quantitative evaluation and comparison ...Show More

Semi-automatic System for Rapid Annotation of Moving Objects in Surveillance Videos using Deep Detection and Multi-object Tracking Techniques

Noor M. AL-Shakarji;Ekincan Ufuktepe;Filiz Bunyak;Hadi Aliakbarpour;Guna Seetharaman;Kannappan Palaniappan

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Year: 2020 | Conference Paper |

Mosaicing of Dynamic Mesentery Video with Gradient Blending

Rumana Aktar;V. H. Huxley;G. Guidoboni;H. AliAkbarpour;F. Bunyak;K. Palaniappan

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

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In the aspect of medical imaging, it is crucial to obtain certain structures or even cellular features in microscopic resolution. Unfortunately, the field of view is inherently limited by the capability of capturing instruments. Thus, mosaicing of such microstructure is of utmost importance in order to restore original visual information for establishing broad structure morphology. Large panoramic...Show More

Mosaicing of Dynamic Mesentery Video with Gradient Blending

Rumana Aktar;V. H. Huxley;G. Guidoboni;H. AliAkbarpour;F. Bunyak;K. Palaniappan

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Deep Learning Based Landmark Matching For Aerial Geolocalization

Koundinya Nouduri;Filiz Bunyak;Shizeng Yao;Hadi Aliakbarpour;Sanjeev Agarwal;Raghuveer Rao;Kannappan Palaniappan

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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Visual odometry has gained increasing attention due to the proliferation of unmanned aerial vehicles, self-driving cars, and other autonomous robotics systems. Landmark detection and matching are critical for visual localization. While current methods rely upon point-based image features or descriptor mappings we consider landmarks at the object level. In this paper, we propose LMNet a deep learni...Show More

Deep Learning Based Landmark Matching For Aerial Geolocalization

Koundinya Nouduri;Filiz Bunyak;Shizeng Yao;Hadi Aliakbarpour;Sanjeev Agarwal;Raghuveer Rao;Kannappan Palaniappan

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Ensemble Of Deep Cascades For Detection Of Laryngeal Adductor Reflex Events In Endoscopy Videos

Ali S. Hamad;Yang Y. Wang;Teresa E. Lever;Filiz Bunyak

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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Vocal fold motion plays a critical role in life-sustaining functions of breathing and swallowing. Multiple muscles, nerves, and other anatomical structures are involved in coordinated motion of the vocal folds. Vocal fold function is affected by a wide range of disorders. Laryngeal endoscopy is used in clinical practice to inspect the larynx and to assess vocal fold function. In this paper we prop...Show More

Ensemble Of Deep Cascades For Detection Of Laryngeal Adductor Reflex Events In Endoscopy Videos

Ali S. Hamad;Yang Y. Wang;Teresa E. Lever;Filiz Bunyak

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Orthogonal Region Selection Network for Laryngeal Closure Detection in Laryngoscopy Videos

Yang Yang Wang;Ali S. Hamad;Teresa E. Lever;Filiz Bunyak

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2020 | Conference Paper |

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Vocal folds (VFs) play a critical role in breathing, swallowing, and speech production. VF dysfunctions caused by various medical conditions can significantly reduce patients' quality of life and lead to life-threatening conditions such as aspiration pneumonia, caused by food and/or liquid "invasion" into the windpipe. Laryngeal endoscopy is routinely used in clinical practice to inspect the laryn...Show More

Orthogonal Region Selection Network for Laryngeal Closure Detection in Laryngoscopy Videos

Yang Yang Wang;Ali S. Hamad;Teresa E. Lever;Filiz Bunyak

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2020 | Conference Paper |

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