Search Results for author: Dogancan Temel

Found 29 papers, 16 papers with code

On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption

1 code implementation2 Sep 2020 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.

Scene Understanding Semantic Segmentation

Novelty Detection Through Model-Based Characterization of Neural Networks

no code implementations13 Aug 2020 Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

To articulate the significance of the model perspective in novelty detection, we utilize backpropagated gradients.

Contrastive Explanations in Neural Networks

2 code implementations1 Aug 2020 Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib

Current modes of visual explanations answer questions of the form $`Why \text{ } P?'$.

Image Quality Assessment

Characterizing Missing Information in Deep Networks Using Backpropagated Gradients

no code implementations ICLR 2020 Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.

Anomaly Detection

Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics

2 code implementations29 Aug 2019 Dogancan Temel, Min-Hung Chen, Ghassan AlRegib

We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.

Traffic Sign Detection Traffic Sign Recognition

Distorted Representation Space Characterization Through Backpropagated Gradients

2 code implementations27 Aug 2019 Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms.

General Classification Image Quality Assessment

Relative Afferent Pupillary Defect Screening through Transfer Learning

no code implementations6 Aug 2019 Dogancan Temel, Melvin J. Mathew, Ghassan AlRegib, Yousuf M. Khalifa

Based on the conducted experiments, proposed algorithm RAPDNet can achieve a sensitivity and a specificity of 90. 6% over 64 test cases in a balanced set, which corresponds to an AUC of 0. 929 in ROC analysis.

Object Recognition Transfer Learning

Implicit Background Estimation for Semantic Segmentation

1 code implementation23 May 2019 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.

Scene Understanding Semantic Segmentation

Automated Pupillary Light Reflex Test on a Portable Platform

no code implementations21 May 2019 Dogancan Temel, Melvin J. Mathew, Ghassan AlRegib, Yousuf M. Khalifa

In this paper, we introduce a portable eye imaging device denoted as lab-on-a-headset, which can automatically perform a swinging flashlight test.

Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions

2 code implementations19 Feb 2019 Dogancan Temel, Tariq Alshawi, Min-Hung Chen, Ghassan AlRegib

Experimental results show that benchmarked algorithms are highly sensitive to tested challenging conditions, which result in an average performance drop of 0. 17 in terms of precision and a performance drop of 0. 28 in recall under severe conditions.

Traffic Sign Detection

Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation

no code implementations18 Feb 2019 Dogancan Temel, Jinsol Lee, Ghassan AlRegib

Experimental results show that deep learning-based image representations can estimate the recognition performance variation with a Spearman's rank-order correlation of 0. 94 under multifarious acquisition conditions.

Object Recognition

Semantically Interpretable and Controllable Filter Sets

no code implementations17 Feb 2019 Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib

In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion.

Image Quality Assessment

Image Quality Assessment and Color Difference

1 code implementation22 Nov 2018 Dogancan Temel, Ghassan AlRegib

In this work, we combine these approaches by extending CIEDE2000 formula with perceptual color difference to assess image quality.

Image Quality Assessment MS-SSIM +1

A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment

1 code implementation21 Nov 2018 Dogancan Temel, Ghassan AlRegib

In this work, we compare the state of the art quality and content-based spatial pooling strategies and show that although features are the key in any image quality assessment, pooling also matters.

Image Quality Assessment SSIM

Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework

no code implementations21 Nov 2018 Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib

While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version.

Image Quality Assessment

Boosting in Image Quality Assessment

1 code implementation21 Nov 2018 Dogancan Temel, Ghassan AlRegib

In addition to support vector machines that are commonly used in the multi-method fusion, we propose using neural networks in the boosting.

Image Quality Assessment

A Comparative Study of Computational Aesthetics

no code implementations19 Nov 2018 Dogancan Temel, Ghassan AlRegib

We show that generic descriptors can perform as well as state of the art hand-crafted aesthetics models that use global features.

BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment

1 code implementation16 Nov 2018 Dogancan Temel, Ghassan AlRegib

Moreover, BleSS significantly enhances the performance of SR-SIM and FSIM in the full TID 2013 database.

Image Quality Assessment

ReSIFT: Reliability-Weighted SIFT-based Image Quality Assessment

1 code implementation14 Nov 2018 Dogancan Temel, Ghassan AlRegib

In terms of the Pearson and the Spearman correlation, ReSIFT is the best performing quality estimator in the overall databases.

Image Quality Assessment

HeartBEAT: Heart Beat Estimation through Adaptive Tracking

1 code implementation19 Oct 2018 Huijie Pan, Dogancan Temel, Ghassan AlRegib

In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart rate from wrist-type photoplethysmography (PPG) signals and simultaneously recorded three-axis acceleration data.

Photoplethysmography (PPG)

CURE-OR: Challenging Unreal and Real Environments for Object Recognition

no code implementations18 Oct 2018 Dogancan Temel, Jinsol Lee, Ghassan AlRegib

Moreover, we investigate the relationship between object recognition and image quality and show that objective quality algorithms can estimate recognition performance under certain photometric challenging conditions.

Object Recognition

CSV: Image Quality Assessment Based on Color, Structure, and Visual System

no code implementations15 Oct 2018 Dogancan Temel, Ghassan AlRegib

This paper presents a full-reference image quality estimator based on color, structure, and visual system characteristics denoted as CSV.

Image Quality Assessment

Perceptual Image Quality Assessment through Spectral Analysis of Error Representations

no code implementations14 Oct 2018 Dogancan Temel, Ghassan AlRegib

To overcome these shortcomings, we introduce an image quality assessment algorithm based on the Spectral Understanding of Multi-scale and Multi-channel Error Representations, denoted as SUMMER.

Image Quality Assessment

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

1 code implementation7 Dec 2017 Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, Ghassan AlRegib

We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions.

Data Augmentation Traffic Sign Recognition

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