- San Francisco CA, US Peter N. Belhumeur - San Francisco CA, US Ying Xiong - Sunnyvale CA, US Jongmin Baek - San Jose CA, US Simon Kozlov - San Francisco CA, US Thomas Berg - San Francisco CA, US David J. Kriegman - San Diego CA, US
The present disclosure is directed toward systems and methods to quickly and accurately identify boundaries of a displayed document in a live camera image feed, and provide a document boundary indicator within the live camera image feed. For example, systems and methods described herein utilize different display document detection processes in parallel to generate and provide a document boundary indicator that accurately corresponds with a displayed document within a live camera image feed. Thus, a user of the mobile computing device can easily see whether the document identification system has correctly identified the displayed document within the camera viewfinder feed.
Data Storage Scheme Switching In A Distributed Data Storage System
- San Francisco CA, US Daniel R. Horn - Mountain View CA, US Andraz Kavalar - San Francisco CA, US David Lichtenberg - San Francisco CA, US Austin Sung - San Francisco CA, US Shi Feng - Millbrae CA, US Jongmin Baek - Foster City CA, US
International Classification:
G06F 3/06 H03M 13/15 G06N 20/00 G06N 7/00
Abstract:
Systems and methods for dynamic and automatic data storage scheme switching in a distributed data storage system. A machine learning-based policy for computing probable future content item access patterns based on historical content item access patterns is employed to dynamically and automatically switch the storage of content items (e.g., files, digital data, photos, text, audio, video, streaming content, cloud documents, etc.) between different data storage schemes. The different data storage schemes may have different data storage cost and different data access cost characteristics. For example, the different data storage schemes may encompass different types of data storage devices, different data compression schemes, and/or different data redundancy schemes.
- San Francisco CA, US Jongmin Baek - Millbrae CA, US
International Classification:
G06N 20/00 G06N 5/04 G06F 16/93 G06F 16/16
Abstract:
Computer-implemented techniques encompass using distinct machine learning sub-models to score respective types of candidate content for the purpose of providing personalized content suggestions to end-users of a content management system. The relevancy scores generated by the distinct sub-models are mapped to expected end-user interaction scores of the candidate content scored. Content suggestions are provided at end-users' computing devices where the suggested content is selected from the candidate content based on the expected end-user interaction scores of the candidate content. For each distinct sub-model, a normalizing mapping function is solved using an optimizer that maps the relevancy scores generated by the sub-model for the candidate content to expected end-user interaction scores for the candidate content. The expected end-user interaction scores are comparable across the distinct sub-models and can be used to rank content suggestions across the distinct sub-models.
One or more embodiments of an image enhancement system enable a computing device to generate an enhanced digital image. In particular, a computing device can enhance a digital image including, for example, a photograph of a whiteboard, document, chalkboard, or other object having a uniform background. The computing device can determine modifications to apply to the digital image by minimizing an energy heuristic that both causes pixels of the digital image to change to a uniform color (e.g., white) and preserves gradients from the digital image. The computing device can further generate an enhanced digital image by applying the determined modifications to the digital image.
- San Francisco CA, US Peter N. Belhumeur - San Francisco CA, US Ying Xiong - Sunnyvale CA, US Jongmin Baek - San Jose CA, US Simon Kozlov - San Francisco CA, US Thomas Berg - San Francisco CA, US David J. Kriegman - San Diego CA, US
The present disclosure is directed toward systems and methods that efficiently and effectively generate an enhanced document image of a displayed document in an image frame captured from a live image feed. For example, systems and methods described herein apply a document enhancement process to a displayed document in an image frame that result in an enhanced document image that is cropped, rectified, un-shadowed, and with dark text against a mostly white background. Additionally, systems and method described herein determine whether a stored digital content item includes a displayed document. In response to determining that a stored digital content item does include a displayed document, systems and methods described herein generate an enhanced document image of a displayed document included in the stored digital content item.
- San Francisco CA, US Peter N. Belhumeur - San Francisco CA, US Ying Xiong - Sunnyvale CA, US Jongmin Baek - San Jose CA, US Simon Kozlov - San Francisco CA, US Thomas Berg - San Francisco CA, US David J. Kriegman - San Diego CA, US
The present disclosure is directed toward systems and methods that efficiently and effectively generate an enhanced document image of a displayed document in an image frame captured from a live image feed. For example, systems and methods described herein apply a document enhancement process to a displayed document in an image frame that result in an enhanced document image that is cropped, rectified, un-shadowed, and with dark text against a mostly white background. Additionally, systems and method described herein determine whether a stored digital content item includes a displayed document. In response to determining that a stored digital content item does include a displayed document, systems and methods described herein generate an enhanced document image of a displayed document included in the stored digital content item.
Live Document Detection In A Captured Video Stream
- San Francisco CA, US Peter N. Belhumeur - San Francisco CA, US Ying Xiong - Sunnyvale CA, US Jongmin Baek - San Jose CA, US Simon Kozlov - San Francisco CA, US Thomas Berg - San Francisco CA, US David J. Kriegman - San Diego CA, US
The present disclosure is directed toward systems and methods to quickly and accurately identify boundaries of a displayed document in a live camera image feed, and provide a document boundary indicator within the live camera image feed. For example, systems and methods described herein utilize different display document detection processes in parallel to generate and provide a document boundary indicator that accurately corresponds with a displayed document within a live camera image feed. Thus, a user of the mobile computing device can easily see whether the document identification system has correctly identified the displayed document within the camera viewfinder feed.
One or more embodiments of an image enhancement system enable a computing device to generate an enhanced digital image. In particular, a computing device can enhance a digital image including, for example, a photograph of a whiteboard, document, chalkboard, or other object having a uniform background. The computing device can determine modifications to apply to the digital image by minimizing an energy heuristic that both causes pixels of the digital image to change to a uniform color (e.g., white) and preserves gradients from the digital image. The computing device can further generate an enhanced digital image by applying the determined modifications to the digital image.
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