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Lolping deepfocus5/16/2023 “By making our DeepFocus source and training data available, we’ve provided a framework not just for engineers developing new VR systems, but also for vision scientists and other researchers studying long-standing perceptual questions,” say the researchers.Ī research paper presented at SIGGRAPH Asia 2018 explains that DeepFocus is a unified rendering and optimization framework based on convolutional neural networks that solve a full range of computational tasks. While varifocal VR headsets can deliver a crisp image anywhere the viewer looks, DeepFocus allows us to render the rest of the scene just the way it looks in the real world: naturally blurry,” mentions Marina Zannoli, a vision scientist at FRL.įacebook is also open-sourcing DeepFocus, making the system’s code and the data set used to train it available to help other VR researchers incorporate it into their work. Those blurry regions help our visual system make sense of the three-dimensional structure of the world and help us decide where to focus our eyes next. “Our eyes are like tiny cameras: When they focus on a given object, the parts of the scene that are at a different depth look blurry. However, HalfDome needs software to work in its full potential, that is where DeepFocus comes into the picture. This makes the VR experience a lot more comfortable, natural, and immersive. HalfDome is an example of a “varifocal” head-mounted display (HMD) that comprises eye-tracking camera systems, wide-field-of-view optics, and adjustable display lenses that move forward and backward to match your eye movements. 2011 6(1):S16.Facebook released a new “AI-powered rendering system”, called DeepFocus yesterday, that works with Half Dome, a special prototype headset that Facebook’s Reality Lab (FRL) team had been working on over the past three years. Distributed computing in image analysis using open source frameworks and application to image sharpness assessment of histological whole slide images. Referenceless image quality evaluation for whole slide imaging. Hashimoto N, Bautista PA, Yamaguchi M, Ohyama N, Yagi Y. Quality evaluation of virtual slides using methods based on comparing common image areas. Image quality metrics applied to digital pathology SPIE Photonics Europe 2016: International Society for Optics and Photonics. Jiménez A, Bueno G, Cristóbal G, Déniz O, Toomey D, Conway C, editors. An automated blur detection method for histological whole slide imaging. Moles Lopez X, D'Andrea E, Barbot P, Bridoux AS, Rorive S, Salmon I, et al. DeepFocus has the potential to be integrated with whole slide scanners to automatically re-scan problematic areas, hence improving the overall image quality for pathologists and image analysis algorithms. When trained and tested on two independent datasets, DeepFocus resulted in an average accuracy of 93.2% (± 9.6%), which is a 23.8% improvement over an existing method. DeepFocus was trained by using 16 different H&E and IHC-stained slides that were systematically scanned on nine different focal planes, generating 216,000 samples with varying amounts of blurriness. DeepFocus is built on TensorFlow, an open source library that exploits data flow graphs for efficient numerical computation. The aim of this study is to develop a deep learning based software called, DeepFocus, which can automatically detect and segment blurry areas in digital whole slide images to address these problems. Moreover, this process is both tedious, and time-consuming. These areas are typically identified by visual inspection, which leads to a subjective evaluation causing high intra- and inter-observer variability. Moreover, these artifacts hamper the performance of computerized image analysis systems. Unfortunately, whole slide scanners often produce images with out-of-focus/blurry areas that limit the amount of tissue available for a pathologist to make accurate diagnosis/prognosis. The development of whole slide scanners has revolutionized the field of digital pathology.
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