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The acquisition technologies developed by Haltadefinizione allow you to obtain very high resolution digital images of the paintings. Thanks to specially designed algorithms, in fact, it is possible to combine a large quantity of individual photographic shots of small portions of the portraits to obtain what is called a gigapixel image.

The acquisition technologies developed by Haltadefinizione allow you to obtain very high resolution digital images of the paintings. Thanks to specially designed algorithms, in fact, it is possible to combine a large quantity of individual photographic shots of small portions of the portraits to obtain what is called a gigapixel image.

The acquisition technologies developed by Haltadefinizione allow you to obtain very high resolution digital images of the paintings. Thanks to specially designed algorithms, in fact, it is possible to combine a large quantity of individual photographic shots of small portions of the portraits to obtain what is called a gigapixel image.

Optical imaging has served as a primary method to collect information about biosystems across scales—from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV). The problem originates from constituent refractive optics—the larger the aperture, the more challenging the correction of lens aberrations. Therefore, it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of millions. To address this unmet need, a variety of high-SBP imagers have emerged over the past decade, enabling an unprecedented resolution and FOV beyond the limit of conventional optics. We provide a comprehensive survey of high-SBP imaging techniques, exploring their underlying principles and applications in bioimaging.

The article analyses the complex digitisation project of Lucrezia Borgia's letters and registers recently carried out by the tech company Haltadefinizione at the State Archive of Modena. In addition to gigapixel acquisition, this case also involved the use of transmitted light digitisation in ultra-high resolution, allowing the unveiling of the watermarks in the documents. The article explores various methodologies used in the digitisation campaign and emphasises how these advanced high-resolution imaging techniques are a fundamental tool for preserving and making these valuable historical documents accessible.

Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition tasks, as well as exceeding human capabilities in both time and accuracy. Histopathology diagnostics is one of the more popular challenges at the intersection of artificial intelligence, computer vision, and medicine. Developing methods to automatically detect and identify pathologies in digitized histology slides imposes unique challenges due to the large size of these images and the complexity of the features present in biological tissue. Most methods that are capable of human-level recognition in histopathology are tuned to a specific problem since the computational complexity exceeds that of traditional image classification problems. In the current study, a deep learning approach is developed and presented that can be trained to locate and accurately classify different types of pathologies in gigapixel digitized histology slides along with completing the binary disease classification for the entire image. The approach uses a novel pyramid tiling approach to take advantage of spatial awareness around the area to be classified, while maintaining efficiency and scalability for gigapixel images. The approach is trained and validated on a wide variety of tissue types (i.e., testis, ovary, prostate, kidney) and pathologies taken from an epigenetically altered histology study at Washington State University. The newly developed procedure was optimized and validated along with comparison and validation on public histology datasets. The current developed procedure was found to be optimal and more reproducible when compared to manual procedures, and optimal to previous protocols that used fragmented tissue or slide analysis. Observations demonstrate that the deep learning histopathology analysis is significantly more efficient and accurate than standard manual histopathology analysis.

Nowadays the use of remote monitoring sensors is a standard practice in landslide characterization and monitoring. In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment while limited studies and applications are still available for ArcSAR Interferometry, Gigapixel imaging and Acoustic sensing. Taking advantage of the facilities located at the Poggio Baldi Landslide Natural Laboratory, an intensive monitoring campaign was carried out on May 2019 using simultaneously the HYDRA-G ArcSAR for radar monitoring, the Gigapan robotic system equipped with a DSLR camera for photo-monitoring purposes and the DUO Smart Noise Monitor for acoustic measurements. The aim of this study was to evaluate the potential of each monitoring sensor and to investigate the ongoing gravitational processes at the Poggio Baldi landslide. Analysis of multi-temporal Gigapixel-images revealed the occurrence of 84 failures of various sizes between 14–17 May 2019. This allowed us to understand the short-term evolution of the rock cliff that is characterized by several impulsive rockfall events and continuous debris production. Radar displacement maps revealed a constant movement of the debris talus at the toe of the main rock scarp, while acoustic records proved the capability of this technique to identify rockfall events as well as their spectral content in a narrow range of frequencies between 200 Hz to 1000 Hz. This work demonstrates the great potential of the combined use of a variety of remote sensors to achieve high spatial and temporal resolution data in the field of landslide characterization and monitoring.

Capturing paintings with gigapixel resolution (resolution around 1000 megapixels or greater) is an innovative technique that is starting to be used by some important international museums for documenting, analysing, and disseminating their masterpieces.This line of research is extremely interesting, not only for art curators and scholars, but also for the general public. The results can be disseminated through online virtual tours, offering a detailed interactive visualization. These virtual tours allow the viewer to delve into the artwork, in such a way, that it is possible to zoom in and observe those details, which would be negligible to the naked eye in a real visit. Therefore, this kind of virtual visualization using gigapixel images becomes an essential tool to enhance this cultural heritage and to make it accessible to everyone.This article will describe an affordable methodology, based on SfM photogrammetry techniques, with which it will be possible to achieve a very high level of detail and chromatic fidelity, when documenting and disseminating pictorial artworks. As a practical example, there will be shown a case study of the altarpiece, from the Museo de Bellas Artes de Valencia (Spain), entitled Virgen de las fiebres, painted around 1500 by Bernardino di Benedetto di Biagio, nicknamed ‘Il Pinturicchio' (Perugia, ca. 1454 – Siena, 1513).

In this paper, we propose a pipeline to investigate the performance of semantic segmentation model that employs an encoder-decoder architecture with atrous separable convolution and spatial pyramid pooling, trained on multi-resolution whole slide breast pathological images with different patch sizes. Our segmentation model obtains the best performance on zoom level 2 (10 × magnification) with AUC score 0.974 in terms of slide-level classification. This outperforms both the performance of the pathologist and other semantic segmentation models on the Camelyon16 dataset. By offering a larger field of view and reducing noise and detail, training a semantic segmentation model on the properly selected lower resolution pathology images can further improve the precision of pixel-wise cancer region segmentation. By contrast, the corresponding inference time is 14 times shorter than the inference time trained on the highest resolution patches, and it is also shorter than the time required by a pathologist with time constraints. Moreover, we prove that the model trained on lower resolution patches can still generate refined external polygons of cancer region on the highest resolution image. This study provides new insights into efficient gigapixel histopathology analysis that will make clinical adoption more likely.

Throughout their lifetime, trees store valuable environmental information within their wood. Unlocking this information requires quantitative analysis, in most cases of the surface of wood. The conventional pathway for high-resolution digitization of wood surfaces and segmentation of wood features requires several manual and time consuming steps. We present a semi-automated high-throughput pipeline for sample preparation, gigapixel imaging, and analysis of the anatomy of the end-grain surfaces of discs and increment cores. The pipeline consists of a collaborative robot (Cobot) with sander for surface preparation, a custom-built open-source robot for gigapixel imaging (Gigapixel Woodbot), and a Python routine for deep-learning analysis of gigapixel images. The robotic sander allows to obtain high-quality surfaces with minimal sanding or polishing artefacts. It is designed for precise and consistent sanding and polishing of wood surfaces, revealing detailed wood anatomical structures by applying consecutively finer grits of sandpaper. Multiple samples can be processed autonomously at once. The custom-built open-source Gigapixel Woodbot is a modular imaging system that enables automated scanning of large wood surfaces. The frame of the robot is a CNC (Computer Numerical Control) machine to position a camera above the objects. Images are taken at different focus points, with a small overlap between consecutive images in the X-Y plane, and merged by mosaic stitching, into a gigapixel image. Multiple scans can be initiated through the graphical application, allowing the system to autonomously image several objects and large surfaces. Finally, a Python routine using a trained YOLOv8 deep learning network allows for fully automated analysis of the gigapixel images, here shown as a proof-of-concept for the quantification of vessels and rays on full disc surfaces and increment cores. We present fully digitized beech discs of 30–35 cm diameter at a resolution of 2.25  μ m, for which we automatically quantified the number of vessels (up to 13 million) and rays. We showcase the same process for five 30 cm length beech increment cores also digitized at a resolution of 2.25  μ m, and generated pith-to-bark profiles of vessel density. This pipeline allows researchers to perform high-detail analysis of anatomical features on large surfaces, test fundamental hypotheses in ecophysiology, ecology, dendroclimatology, and many more with sufficient sample replication.