89 citations · 371 across the 28 of their papers we have counts for
17 papers · 1 filter
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
Charles Lehman, Dogancan Temel, Ghassan AlRegib
Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential. Implicit Background Estimation (…
Novelty Detection Through Model-Based Characterization of Neural Networks
Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel +1
In this paper, we propose a model-based characterization of neural networks to detect novel input types and conditions. Novelty detection is crucial to identify abnormal inputs tha…
Contrastive Explanations in Neural Networks
Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel +1
Visual explanations are logical arguments based on visual features that justify the predictions made by neural networks. Current modes of visual explanations answer questions of th…
Backpropagated Gradient Representations for Anomaly Detection
Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel +1
Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use act…
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…
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…
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
Dogancan Temel, Jinsol Lee, Ghassan AlRegib
In this paper, we introduce a large-scale, controlled, and multi-platform object recognition dataset denoted as Challenging Unreal and Real Environments for Object Recognition (CUR…
UNIQUE: Unsupervised Image Quality Estimation
D. Temel, M. Prabhushankar, G. AlRegib
In this paper, we estimate perceived image quality using sparse representations obtained from generic image databases through an unsupervised learning approach. A color space trans…
CSV: Image Quality Assessment Based on Color, Structure, and Visual System
Dogancan Temel, Ghassan AlRegib
This paper presents a full-reference image quality estimator based on color, structure, and visual system characteristics denoted as CSV. In contrast to the majority of existing me…
Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
Dogancan Temel, Ghassan AlRegib
Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads. State-of-the-art algorithms successfully perform traffic sign detection over ex…
Perceptual Image Quality Assessment through Spectral Analysis of Error Representations
Dogancan Temel, Ghassan AlRegib
In this paper, we analyze the statistics of error signals to assess the perceived quality of images. Specifically, we focus on the magnitude spectrum of error images obtained from…
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar +1
In this paper, we investigate the robustness of traffic sign recognition algorithms under challenging conditions. Existing datasets are limited in terms of their size and challengi…