Search Results for author:
Found 29 papers, 18 papers with code
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential.
Novelty Detection Through Model-Based Characterization of Neural Networks
To articulate the significance of the model perspective in novelty detection, we utilize backpropagated gradients.
Contrastive Explanations in Neural Networks
Current modes of visual explanations answer questions of the form $`Why \text{ } P?'$.
Backpropagated Gradient Representations for Anomaly Detection
Anomalies require more drastic model updates to fully represent them compared to normal data.
Characterizing Missing Information in Deep Networks Using Backpropagated Gradients
To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.
Leveraging Adversarial Examples to Obtain Robust Second-Order Representations
Such a positioning scheme is based on a data point’s second-order property.
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.
Distorted Representation Space Characterization Through Backpropagated Gradients
In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms.
Relative Afferent Pupillary Defect Screening through Transfer Learning
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.
Implicit Background Estimation for Semantic Segmentation
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.
Automated Pupillary Light Reflex Test on a Portable Platform
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
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.
Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation
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.
Semantically Interpretable and Controllable Filter Sets
In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion.
Image Quality Assessment and Color Difference
In this work, we combine these approaches by extending CIEDE2000 formula with perceptual color difference to assess image quality.
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation
We use multiple linear decoders to capture different abstraction levels of the image patches.
Boosting in Image Quality Assessment
In addition to support vector machines that are commonly used in the multi-method fusion, we propose using neural networks in the boosting.
A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment
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.
Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework
While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version.
A Comparative Study of Computational Aesthetics
We show that generic descriptors can perform as well as state of the art hand-crafted aesthetics models that use global features.
PerSIM: Multi-resolution Image Quality Assessment in the Perceptually Uniform Color Domain
An average observer perceives the world in color instead of black and white.
BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment
Moreover, BleSS significantly enhances the performance of SR-SIM and FSIM in the full TID 2013 database.
ReSIFT: Reliability-Weighted SIFT-based Image Quality Assessment
In terms of the Pearson and the Spearman correlation, ReSIFT is the best performing quality estimator in the overall databases.
HeartBEAT: Heart Beat Estimation through Adaptive Tracking
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.
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
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.
Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads.
CSV: Image Quality Assessment Based on Color, Structure, and Visual System
This paper presents a full-reference image quality estimator based on color, structure, and visual system characteristics denoted as CSV.
Perceptual Image Quality Assessment through Spectral Analysis of Error Representations
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.
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions.
