A new EU BON derived paper, publsihed recently in the journal Remote Sensing, introduces eHabitat+, a habitat modelling service supporting the European Commission’s Digital Observatory for Protected Areas.

Abstract:

Protected areas (PAs) need to be assessed systematically according to biodiversity values and threats in order to support decision-making processes. For this, PAs can be characterized according to their species, ecosystems and threats, but such information is often difficult to access and usually not comparable across regions. There are currently over 200,000 PAs in the world, and assessing these systematically according to their ecological values remains a huge challenge. However, linking remote sensing with ecological modelling can help to overcome some limitations of conservation studies, such as the sampling bias of biodiversity inventories. The aim of this paper is to introduce eHabitat+, a habitat modelling service supporting the European Commission’s Digital Observatory for Protected Areas, and specifically to discuss a component that systematically stratifies PAs into different habitat functional types based on remote sensing data. eHabitat+ uses an optimized procedure of automatic image segmentation based on several environmental variables to identify the main biophysical gradients in each PA. This allows a systematic production of key indicators on PAs that can be compared globally. Results from a few case studies are illustrated to show the benefits and limitations of this open-source tool.

Original Source: 

Martínez-López, J.; Bertzky, B.; Bonet-García, F.J.; Bastin, L.; Dubois, G. Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification. Remote Sens. 2016, 8, 780. DOI: 0.3390/rs8090780

The discipline of taxonomy is highly reliant on previously published photographs, drawings and other images as biodiversity data. Inspired by the uncertainty among taxonomists, a team, representing both taxonomists and experts in rights and copyright law, has traced the role and relevance of copyright when it comes to images with scientific value. Their discussion and conclusions are published in the latest paper added in the EU BON Collection in the open science journal Research Ideas and Outcomes (RIO).

Taxonomic papers, by definition, cite a large number of previous publications, for instance, when comparing a new species to closely related ones that have already been described. Often it is necessary to use images to demonstrate characteristic traits and morphological differences or similarities. In this role, the images are best seen as biodiversity data rather than artwork. According to the authors, this puts them outside the scope, purposes and principles of Copyright. Moreover, such images are most useful when they are presented in a standardized fashion, and lack the artistic creativity that would otherwise make them 'copyrightable works'.

"It follows that most images found in taxonomic literature can be re-used for research or many other purposes without seeking permission, regardless of any copyright declaration," says Prof. David J. Patterson, affiliated with both Plazi and the University of Sydney.

Nonetheless, the authors point out that, "in observance of ethical and scholarly standards, re-users are expected to cite the author and original source of any image that they use." Such practice is "demanded by the conventions of scholarship, not by legal obligation," they add.

However, the authors underline that there are actual copyrightable visuals, which might also make their way to a scientific paper. These include wildlife photographs, drawings and artwork produced in a distinctive individual form and intended for other than comparative purposes, as well as collections of images, qualifiable as databases in the sense of the European Protection of Databases directive.

In their paper, the scientists also provide an updated version of the Blue List, originally compiled in 2014 and comprising the copyright exemptions applicable to taxonomic works. In their Extended Blue List, the authors expand the list to include five extra items relating specifically to images.

"Egloff, Agosti, et al. make the compelling argument that taxonomic images, as highly standardized 'references for identification of known biodiversity,' by necessity, lack sufficient creativity to qualify for copyright. Their contention that 'parameters of lighting, optical and specimen orientation' in biological imaging must be consistent for comparative purposes underscores the relevance of the merger doctrine for photographic works created specifically as scientific data," comments on the publication Ms. Gail Clement, Head of Research Services at the Caltech Library.

"In these cases, the idea and expression are the same and the creator exercises no discretion in complying with an established convention. This paper is an important contribution to the literature on property interests in scientific research data - an essential framing question for legal interoperability of research data," she adds.

###

Original source:

Egloff W, Agosti D, Kishor P, Patterson D, Miller J (2017) Copyright and the Use of Images as Biodiversity Data. Research Ideas and Outcomes 3: e12502. https://doi.org/10.3897/rio.3.e12502

Rotten returns with a curious mixture of rage and nostalgia.

AXIS Image Health Analytics notifies users of any issues with image quality and ensures that the cameras being used are capturing the right images at all times.

As a writer I’m loath to admit it, but sometimes words just can’t paint as clear of a picture as, well, a picture.

Children's Geographies; 08/01/2021
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About the Role
As an Imaging R&D Graduate, you will be joining the ISP team within Arm, which develops and designs image processing technology that is used in a range of applications including automobiles, security cameras, and drones. The algorithm development team is tasked with solving a variety of image processing problems, from denoise to demosaic, auto-exposure to motion compensation. Our algorithms must satisfy the competing demands of high image quality, and efficient, low-power hardware implementation.

This is an opportunity to contribute towards the next generation of imaging systems, for both human viewing and autonomous driving applications.

Why should you apply?

• You want to work in leading digital imaging technology.
• You have a keen interest in imaging or image processing, which you would like to develop into a career.
• You want to see tangible results from your work.
• You want to have the opportunity to learn from the best engineers and start a career in a leading imaging and vision technology group.

What will I be accountable for?

• Working with image quality experts to determine requirements for processing.
• Developing new image processing algorithms, often from early concept phase and typically in a mathematical modelling environment.
• Implementing novel algorithms, starting from a floating-point model
• Testing and benchmarking of the results, working closely with our image quality experts.
• Collaborating with the wider engineering team to arrive at an architecture and fixed-point model of your algorithm, optimized for hardware or software implementation

Abstract Objectives Higher education students experience elevated levels of suicidal ideation, but often face barriers in accessing support. The Mental Imagery for Suicidality in Students Trial (MISST; ISRCTN13621293; NCT05296538) tested the feasibility and acceptability of a six-session imagery-based approach called Broad-Minded Affective Coping (BMAC). This qualitative evaluation explored the experiences of MISST participants and staff. […]

The post A qualitative study exploring participants experiences of the Mental Imagery for Suicidality in Students Trial was curated by information for practice.

The singer-songwriter's latest album, "Black Cowboys," chronicles the role played by African-Americans in settling the West after the Civil War. The album has been nominated for a Grammy.

imageHOLDERS’ bespoke kiosk technology has helped ViewPoint Feedback develop a new range of touchless self-service solutions ­- ensuring customers, employees, patients and students continue to leave vital real-time responses.

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Two U.S. experts who inspected North Korea’s uranium enrichment facility at the Yongbyon nuclear complex in 2010 have analyzed recent images of a similar facility in the North and pointed out differences.  Stanford University professor emeritus Siegfried Hecker and Robert Carlin, a scholar at the ...

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The structural and chemical evolution of battery electrodes at the nanoscale plays an important role in affecting the cell performance. Nano-resolution X-ray microscopy has been demonstrated as a powerful technique for characterizing the evolution of battery electrodes under operating conditions with sensitivity to their morphology, compositional distribution and redox heterogeneity. In real-world batteries, the electrode could deform upon battery operation, causing challenges for the image registration which is necessary for several experimental modalities, e.g. XANES imaging. To address this challenge, this work develops a deep-learning-based method for automatic particle identification and tracking. This approach was not only able to facilitate image registration with good robustness but also allowed quantification of the degree of sample deformation. The effectiveness of the method was first demonstrated using synthetic datasets with known ground truth. The method was then applied to an experimental dataset collected on an operating lithium battery cell, revealing a high degree of intra- and interparticle chemical complexity in operating batteries.

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In a photoinjector electron source, the initial transverse electron bunch properties are determined by the spatial properties of the laser beam on the photocathode. Spatial shaping of the laser is commonly achieved by relay imaging an illuminated circular mask onto the photocathode. However, the Gibbs phenomenon shows that recreating the sharp edge and discontinuity of the cut profile at the mask on the cathode is not possible with an optical relay of finite aperture. Furthermore, the practical injection of the laser into the photoinjector results in the beam passing through small or asymmetrically positioned apertures. This work uses wavefront propagation to show how the transport apertures cause ripple structures to appear in the transverse laser profile even when effectively the full laser power is transmitted. The impact of these structures on the propagated electron bunch has also been studied with electron bunches of high and low charge density. With high charge density, the ripples in the initial charge distribution rapidly wash-out through space charge effects. However, for bunches with low charge density, the ripples can persist through the bunch transport. Although statistical properties of the electron bunch in the cases studied are not greatly affected, there is the potential for the distorted electron bunch to negatively impact machine performance. Therefore, these effects should be considered in the design phase of accelerators using photoinjectors.

Signal-to-noise ratio and spatial resolution are quantitatively analysed in the context of in-line (propagation based) X-ray phase-contrast imaging. It is known that free-space propagation of a coherent X-ray beam from the imaged object to the detector plane, followed by phase retrieval in accordance with Paganin's method, can increase the signal-to-noise in the resultant images without deteriorating the spatial resolution. This results in violation of the noise-resolution uncertainty principle and demonstrates `unreasonable' effectiveness of the method. On the other hand, when the process of free-space propagation is performed in software, using the detected intensity distribution in the object plane, it cannot reproduce the same effectiveness, due to the amplification of photon shot noise. Here, it is shown that the performance of Paganin's method is determined by just two dimensionless parameters: the Fresnel number and the ratio of the real decrement to the imaginary part of the refractive index of the imaged object. The relevant theoretical analysis is performed first, followed by computer simulations and then by a brief test using experimental images collected at a synchrotron beamline. More extensive experimental tests will be presented in the second part of this paper.

The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.

The Keck-PAD (pixel array detector) was developed at Cornell as a burst-rate imager capable of recording images from successive electron bunches (153 ns period) from the Advanced Photon Source (APS). Both Si and hole-collecting Schottky CdTe have been successfully bonded to this ASIC (application-specific integrated circuit) and used with this frame rate. The facility upgrades at the APS will lower the bunch period to 77 ns, which will require modifications to the Keck-PAD electronics to image properly at this reduced period. In addition, operation at high X-ray energies will require a different sensor material having a shorter charge collection time. For the target energy of 40 keV for this project, simulations have shown that electron-collecting CdTe should allow >90% charge collection within 35 ns. This collection time will be sufficient to sample the signal from one frame and prepare for the next. 750 µm-thick electron-collecting Schottky CdTe has been obtained from Acrorad and bonded to two different charge-integrating ASICs developed at Cornell, the Keck-PAD and the CU-APS-PAD. Carrier mobility has been investigated using the detector response to single X-ray bunches at the Cornell High Energy Synchrotron Source and to a pulsed optical laser. The tests indicate that the collection time will meet the requirements for 77 ns imaging.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

Here, a morphologically based approach is used for the in situ characterization of 3D growth rates of facetted crystals from the solution phase. Crystal images of single crystals of the β-form of l-glutamic acid are captured in situ during their growth at a relative supersaturation of 1.05 using transmission optical microscopy. The crystal growth rates estimated for both the {101} capping and {021} prismatic faces through image processing are consistent with those determined using reflection light mode [Jiang, Ma, Hazlehurst, Ilett, Jackson, Hogg & Roberts (2024). Cryst. Growth Des. 24, 3277–3288]. The growth rate in the {010} face is, for the first time, estimated from the shadow widths of the {021} prismatic faces and found to be typically about half that of the {021} prismatic faces. Analysis of the 3D shape during growth reveals that the initial needle-like crystal morphology develops during the growth process to become more tabular, associated with the Zingg factor evolving from 2.9 to 1.7 (>1). The change in relative solution supersaturation during the growth process is estimated from calculations of the crystal volume, offering an alternative approach to determine this dynamically from visual observations.

In a new NAE Perspective, Idalis Villanueva Alarcón argues that engineering must recognize and permanently remove negative hidden curriculum. It is only then that engineering can create a new image, one where everyone is equitably welcomed, valued, invested in, and sustained for generations to come.

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