An approaches for noise induced object classifications accuracy improvement
| Autor: | Chris Capraro, Uttam Majumder, Josh Siddall, Chris Cicotta, Daniel Brown, Eric K. Davis |
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| Rok vydání: | 2019 |
| Předmět: |
Synthetic aperture radar
Contextual image classification business.industry Image quality Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition Pattern recognition Object (computer science) Object detection Noise Compressed sensing Computer Science::Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | Cyber Sensing 2019. |
| DOI: | 10.1117/12.2520618 |
| Popis: | Among various parameters, large scene object detection and classification accuracy depends on image quality. In general, deep neural networks (DNN) are trained to achieve a desired recognition accuracy on a set of targets. However, DNNs become tuned to the training data used and may not generalize to new unseen data artifacts. Classification accuracy of a previously trained DNN is significantly reduced when classification is run on an image altered with additive noise. In this research, we propose image pre-processing to reduce the impact of noise induced low classification accuracy. Our approach consists of applying compressive sensing inspired pre-processing techniques to noisy images. We then compare the object recognition accuracy of a pretrained model on pre-processed noisy images and unprocessed noisy images. We will present our technical method, results, and analysis on relevant synthetic aperture radar data. |
| Databáze: | OpenAIRE |
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