Bridging the Gap Between Computational Photography and Visual Recognition
| Autor: | Sudarshan Nagesh, Wenhan Yang, Junru Wu, Michael Albright, Zhangyang Wang, Wen-Chi Chin, Sreya Banerjee, Xiaoshuai Zhang, Mahmoud Lababidi, Rosaura G. VidalMata, Yi-Chun Li, Brandon RichardWebster, Sushobhan Ghosh, Scott McCloskey, Jiaying Liu, Asong Tambo, Hwann-Tzong Chen, Pedro Davalos, Ben Miller, Walter J. Scheirer, Tzu-Wei Huang, Ye Yuan, Yueyu Hu, Charles Otto |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Computational photography Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Image restoration Interpretability business.industry Applied Mathematics Deep learning Photography Cognitive neuroscience of visual object recognition Visual appearance Visualization Computational Theory and Mathematics 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Software |
| DOI: | 10.48550/arxiv.1901.09482 |
| Popis: | What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene content? While there have been important advances in the area of computational photography to restore or enhance the visual quality of an image, the capabilities of such techniques have not always translated in a useful way to visual recognition tasks. Consequently, there is a pressing need for the development of algorithms that are designed for the joint problem of improving visual appearance and recognition, which will be an enabling factor for the deployment of visual recognition tools in many real-world scenarios. To address this, we introduce the UG^2 dataset as a large-scale benchmark composed of video imagery captured under challenging conditions, and two enhancement tasks designed to test algorithmic impact on visual quality and automatic object recognition. Furthermore, we propose a set of metrics to evaluate the joint improvement of such tasks as well as individual algorithmic advances, including a novel psychophysics-based evaluation regime for human assessment and a realistic set of quantitative measures for object recognition performance. We introduce six new algorithms for image restoration or enhancement, which were created as part of the IARPA sponsored UG^2 Challenge workshop held at CVPR 2018. Under the proposed evaluation regime, we present an in-depth analysis of these algorithms and a host of deep learning-based and classic baseline approaches. From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area. Comment: CVPR Prize Challenge: http://www.ug2challenge.org |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|