89 citations · 371 across the 28 of their papers we have counts for
8 papers · 1 filter
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
Dogancan Temel, Min-Hung Chen, Ghassan AlRegib
Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign…
Distorted Representation Space Characterization Through Backpropagated Gradients
Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel +1
In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms. We demonstrate the utility of such gra…
Relative Afferent Pupillary Defect Screening through Transfer Learning
Dogancan Temel, Melvin J. Mathew, Ghassan AlRegib +1
Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on rel…
Implicit Background Estimation for Semantic Segmentation
Charles Lehman, Dogancan Temel, Ghassan AlRegib
Scene understanding and semantic segmentation are at the core of many computer vision tasks, many of which, involve interacting with humans in potentially dangerous ways. It is the…
Automated Pupillary Light Reflex Test on a Portable Platform
Dogancan Temel, Melvin J. Mathew, Ghassan AlRegib +1
In this paper, we introduce a portable eye imaging device denoted as lab-on-a-headset, which can automatically perform a swinging flashlight test. We utilized this device in a clin…
Semantically Interpretable and Controllable Filter Sets
Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel +1
In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion. Each semantic filter learns a vi…
Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
Dogancan Temel, Tariq Alshawi, Min-Hung Chen +1
State-of-the-art algorithms successfully localize and recognize traffic signs over existing datasets, which are limited in terms of challenging condition type and severity. Therefo…
Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation
Dogancan Temel, Jinsol Lee, Ghassan AlRegib
In this paper, we investigate the reliability of online recognition platforms, Amazon Rekognition and Microsoft Azure, with respect to changes in background, acquisition device, an…