89 citations · 371 across the 28 of their papers we have counts for
31 papers
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…
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…
Fault Detection Using Color Blending and Color Transformations
Zhen Wang, Dogancan Temel, Ghassan AlRegib
In the field of seismic interpretation, univariate databased maps are commonly used by interpreters, especially for fault detection. In these maps, contrast between target regions…
Image Quality Assessment and Color Difference
Dogancan Temel, Ghassan AlRegib
An average healthy person does not perceive the world in just black and white. Moreover, the perceived world is not composed of pixels and through vision humans perceive structures…
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation
Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib
In this paper, we train independent linear decoder models to estimate the perceived quality of images. More specifically, we calculate the responses of individual non-overlapping i…
Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework
Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib
In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The applicability of these filter sets is demonstrated…
A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment
Dogancan Temel, Ghassan AlRegib
The process of quantifying image quality consists of engineering the quality features and pooling these features to obtain a value or a map. There has been a significant research i…
Coding of 3D Videos Based on Visual Discomfort
Dogancan Temel, Ghassan AlRegib
We propose a rate-distortion optimization method for 3D videos based on visual discomfort estimation. We calculate visual discomfort in the encoded depth maps using two indexes: te…
Effectiveness of 3VQM in Capturing Depth Inconsistencies
Dogancan Temel, Ghassan AlRegib
The 3D video quality metric (3VQM) was proposed to evaluate the temporal and spatial variation of the depth errors for the depth values that would lead to inconsistencies between l…
Boosting in Image Quality Assessment
Dogancan Temel, Ghassan AlRegib
In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. Existing multi-method studies focus on proposing a single quality estimato…
A Comparative Study of Computational Aesthetics
Dogancan Temel, Ghassan AlRegib
Objective metrics model image quality by quantifying image degradations or estimating perceived image quality. However, image quality metrics do not model what makes an image more…
PerSIM: Multi-resolution Image Quality Assessment in the Perceptually Uniform Color Domain
Dogancan Temel, Ghassan AlRegib
An average observer perceives the world in color instead of black and white. Moreover, the visual system focuses on structures and segments instead of individual pixels. Based on t…
BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment
Dogancan Temel, Ghassan AlRegib
This paper proposes a biologically-inspired low-level spatiochromatic-model-based similarity method (BLeSS) to assist full-reference image-quality estimators that originally oversi…
ReSIFT: Reliability-Weighted SIFT-based Image Quality Assessment
Dogancan Temel, Ghassan AlRegib
This paper presents a full-reference image quality estimator based on SIFT descriptor matching over reliability-weighted feature maps. Reliability assignment includes a smoothing o…