Academic literature on the topic 'Multi-Light Image Collections'

The digitisation of natural science specimens is a shared ambition of many of the largest collections, but the scale of these collections, estimated at at least 1.1 billion specimens (Johnson et al. 2023), continues to challenge even the most resource-rich organisations.The Natural History Museum, London (NHM) has been pioneering work to accelerate the digitisation of its 80 million specimens. Since the inception of the NHM Digital Collection Programme in 2014, more than 5.5 million specimen records have been made digitally accessible. This has enabled the museum to deliver a tenfold increase in digitisation, compared to when rates were first measured by the NHM in 2008. Even with this investment, it will take circa 150 years to digitise its remaining collections, leading the museum to pursue technology-led solutions alongside increased funding to deliver the next increase in digitisation rate. Insects comprise approximately half of all described species and, at the NHM, represent more than one-third (c. 30 million specimens) of the NHM's overall collection. Their most common preservation method, attached to a pin alongside a series of labels with metadata, makes insect specimens challenging to digitise. Early Artificial Intelligence (AI)-led innovations (Price et al. 2018) resulted in the development of ALICE, the museum's Angled Label Image Capture Equipment, in which a pinned specimen is placed inside a multi-camera setup, which captures a series of partial views of a specimen and its labels. Centred around the pin, these images can be digitally combined and reconstructed, using the accompanying ALICE software, to provide a clean image of each label. To do this, a Convolutional Neural Network (CNN) model is incorporated, to locate all labels within the images. This is followed by various image processing tools to transform the labels into a two-dimensional viewpoint, align the associated label images together, and merge them into one label. This allows users to manually, or computationally (e.g., using Optical Character Recognition [OCR] tools) extract label data from the processed label images (Salili-James et al. 2022). With the ALICE setup, a user might average imaging 800 digitised specimens per day, and exceptionally, up to 1,300. This compares with an average of 250 specimens or fewer daily, using more traditional methods involving separating the labels and photographing them off of the pin. Despite this, our original version of ALICE was only suited to a small subset of the collection. In situations when the specimen is very large, there are too many labels, or these labels are too close together, ALICE fails (Dupont and Price 2019).Using a combination of updated AI processing tools, we hereby present ALICE version 2. This new version of ALICE provides faster rates, improved software accuracy, and a more streamlined pipeline. It includes the following updates:Hardware: after conducting various tests, we have optimised the camera setup. Further hardware updates include a Light-Emitting Diode (LED) ring light, as well as modifications to the camera mounting.Software: our latest software incorporates machine learning and other computer vision tools to segment labels from ALICE images and stitch them together more quickly and with a higher level of accuracy, significantly reducing the image processing failure rate. These processed label images can be combined with the latest OCR tools for automatic transcription and data segmentation.Buildkit: we aim to provide a toolkit that any individual or institution can incorporate into their digitisation pipeline. This includes hardware instructions, an extensive guide detailing the pipeline, and new software code accessible via Github.We provide test data and workflows to demonstrate the potential of ALICE version 2 as an effective, accessible, and cost-saving solution to digitising pinned insect specimens. We also describe potential modifications, enabling it to work with other types of specimens.

Digital technology presents us with new and compelling opportunities for discovery when focused on the world's natural history collections. The outstanding barrier to applying existing and forthcoming computational methods for large-scale study of this important resource is that it is (largely) not yet in the digital realm. Without development of new and much faster methods for digitizing objects in these collections, it will be a long time before these data are available in digital form. For example, methods that are currently employed for capturing, cataloguing, and indexing pinned insect specimen data will require many tens of years or more to process collections with millions of dry specimens, and so we need to develop a much faster pipeline. In this paper we describe a capture system capable of collecting and archiving the imagery necessary to digitize a collection of circa 4.5 million specimens in one or two years of production operation. To minimize the time required to digitize each specimen, we have proposed (Hereld et al. 2017) developing multi-camera systems to capture the pinned insect and its accompanying labels from many angles in a single exposure. Using a sampling (21 randomly drawn drawers, totalling 5178 insects) of the 4.5 million specimens in the collection at the Field Museum of Natural History, we estimated that a large fraction of that collection (97.6% +/- 2.2%) consists of pinned insects with labels that are visible from one angle or another without requiring adjustment or removal of elements on the pin. In this situation a multi-camera system with enough angular coverage could provide imagery for reconstructing virtual labels from fragmentary views taken from different directions. Agarwal et al. (2018) demonstrated a method for combining these multiple views into a virtual label that could be transcribed by automated optical character recognition software. We have now designed, built and tested a prototype snapshot 3D digitization station to allow rapid capture of multi-view imagery for automated capture of pinned insect specimens and labels. It consists of twelve very small and light 8-megapixel cameras (Fig. 1), each controlled by a small dedicated computer. The cameras are arrayed around the target volume, six on each side of the sample feed path. Their positions and orientations are fixed by a 3D-printed scaffolding designed for the purpose. The twelve camera controllers and a master computer are connected to a dedicated high-speed data network over which all of the coordinating control signals and returning images and metadata are passed. The system is integrated with a high-performance object store that includes a database for metadata and the archived images comprising each snapshot. The system is designed so that it can be readily extended to include additional or different sensors. The station is meant to be fed with specimens by a conveyor belt whose motion is coordinated with the exposure of the multi-view snapshots. In order to test the performance of the system we added a recirculating specimen feeder designed expressly for this experiment. With it integrated into the system in place of a conventional conveyor belt we are able to provide a continuous stream of targets for the digitization system to facilitate long tests of its performance and robustness. We demonstrated the ability to capture data at a peak rate of 1400 specimens per hour and an average rate of 1000 specimens per hour over the course of a sustained 6 hour run. The dataset (Hereld and Ferrier 2018) collected in this experiment provides fodder for the further development of algorithms for the offline reconstruction and automatic transcription of the label contents.

To overcome the challenges during the crop growth process, e.g., pest infestation, inadequate environmental monitoring, and poor intelligence, this study proposes a crop growth information collection system based on a solar insecticidal lamp. The system comprises two primary modules: (1) an environmental information collection module, and (2) a multi-view image collection module. The environmental information collection module acquires crucial parameters, e.g., temperature, relative humidity, light intensity, soil conductivity, nitrogen, phosphorus, potassium content, and pH, by means of various sensors. Simultaneously, the multi-view image collection module employs three industrial cameras to capture images of the crop from the top, left, and right perspectives. The system is developed on the ESP32-S3 platform. WiFi-Mesh wireless communication technology is adopted to achieve high-frequency, real-time data transmission. Additionally, visualization software has been developed for real-time data display, data storage, and dynamic curve plotting. Field verification indicates that the proposed system effectively meets the requirements of pest control and crop growth information collection, which provides substantial support for the advancement of smart agriculture.

Abstract We present a combination of image processing operations, which improve the quality of OCT images. The sequence is: (1) Gaussian blur, (2) Enhance Local Contrast (CLAHE), (3) FFT based band-pass filter, (4) Background Subtraction, (5) adjustment of the contrast of the image with light saturation (Enhance Contrast) and finally (6) reassignment of the palette such as to enhance the bright part of the images. The multi-step treatment reduces pixel-to-pixel noise, improves the homogeneity of the contrast, supresses vertical strips, reduces cosmetic distractions, equalizes contrast across a collection of images, and uses the pallet to emphasise areas of greater intensity. The enhanced images show a significant improvement in their quality, with details that do not appear in the original OCT image. The processing of these images and the comparison with untreated OCT images are shown throughout the paper.