Adaptive sampling for scanning pixel cameras
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Abstract (arXiv)
A scanning pixel camera is a novel low-cost, low-power sensor that is not diffraction limited. It produces data as a sequence of samples extracted from various parts of the scene during the course of a scan. It can provide very detailed images at the expense of samplerates and slow image acquisition time. This paper proposes a new algorithm which allows the sensor to adapt the samplerate over the course of this sequence. This makes it possible to overcome some of these limitations by minimising the bandwidth and time required to image and transmit a scene, while maintaining image quality. We examine applications to image classification and semantic segmentation and are able to achieve similar results compared to a fully sampled input, while using 80% fewer samples
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 27 Jul 2022.
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11 of 22 references.
References · 11
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- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
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- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2014Striving for Simplicity: The All Convolutional Netcited by 983
- 2016Virtual Worlds as Proxy for Multi-Object Tracking Analysiscited by 340
- 2021Vision Transformer with Progressive Samplingcited by 44
- 2019Saccader: Improving Accuracy of Hard Attention Models for Visioncited by 35
- 2015Reconstruction-free action inference from compressive imagerscited by 7
- 2018SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networkscited by 3
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