Zoom Level Research Articles

Zoom behavior during visual search modulates pupil diameter and reflects adaptive control states.

Adaptive gain theory proposes that the dynamic shifts between exploration and exploitation control states are modulated by the locus coeruleus-norepinephrine system and reflected in tonic and phasic pupil diameter. This study tested predictions of this theory in the context of a societally important visual search task: the review and interpretation of digital whole slide images of breast biopsies by physicians (pathologists). As these medical images are searched, pathologists encounter difficult visual features and intermittently zoom in to examine features of interest. We propose that tonic and phasic pupil diameter changes during image review may correspond to perceived difficulty and dynamic shifts between exploration and exploitation control states. To examine this possibility, we monitored visual search behavior and tonic and phasic pupil diameter while pathologists (N = 89) interpreted 14 digital images of breast biopsy tissue (1,246 total images reviewed). After viewing the images, pathologists provided a diagnosis and rated the level of difficulty of the image. Analyses of tonic pupil diameter examined whether pupil dilation was associated with pathologists' difficulty ratings, diagnostic accuracy, and experience level. To examine phasic pupil diameter, we parsed continuous visual search data into discrete zoom-in and zoom-out events, including shifts from low to high magnification (e.g., 1× to 10×) and the reverse. Analyses examined whether zoom-in and zoom-out events were associated with phasic pupil diameter change. Results demonstrated that tonic pupil diameter was associated with image difficulty ratings and zoom level, and phasic pupil diameter showed constriction upon zoom-in events, and dilation immediately preceding a zoom-out event. Results are interpreted in the context of adaptive gain theory, information gain theory, and the monitoring and assessment of physicians' diagnostic interpretive processes.

Multitemporal and Multiscale Applications of Geomatic Techniques to Medium-Sized Archaeological Sites—Case Study of Marroquíes Bajos (Jaén, Spain)

This study describes a methodology for obtaining a geometric documentation of a medium-sized archaeological area by applying various geomatic techniques. The procedure considers the obtainment of products at several scales, from the entire site to small artifacts, and at several dates, in order to model the evolution of the archaeological work. The methodology includes both LiDAR and photogrammetry, using the LiDAR point clouds to support the geometry obtained using photogrammetry and adding texture from this source. The technique used was adapted to the circumstances of the scene by considering the scale level (resolution and accuracy), complexity, and other requirements of the project. In the case of LiDAR, terrestrial laser scanning and structured-light scanning were used, and the aerial photogrammetry used two types of RPAS (medium and low flight height), close range photogrammetry with a conventional camera, and very close-range photogrammetry with a conventional camera mounted with a macro lens. The methodology demonstrated its feasibility for performing these types of studies, providing products adapted to the required scale level. All results were integrated into a website, including a map that allows user interaction and displays products at a selected zoom level, according to their scale level. The website also displays 3D models of the scenes and objects studied.

From simplex to complex: designing for wellbeing at scale,

In this article, we propose a theoretical framework of reference for the design for wellbeing, mainly in large techno ecosystems like smart cities and smart learning ecosystems taken here as examples. Starting from a people/human-centered vision of such ecosystems and the identification of their smartness with the ASLERD pyramids of needs, the mapping on the levels of this latter of the wellbeing – either defined by the living conditions generated by the context and perceived at the individual level due to the involvement in the technologically augmented processes taking place within the ecosystem – is discussed. It is also shown that the multidimensional smartness-wellbeing construct incorporates the perspective of the design for the experience, whatever the focus and the zoom level applied by the designers to the ecosystem under consideration. Finally, we show that the proposed theoretical framework allows the adoption of a bottom-up participatory approach to evaluate the smartness of the ecosystems of interest and, thus, the possibility of comparing ecosystems with similar characteristics and identifying their peculiarities with respect to the proposed framework.

Segmentation and Classification of Zn-Al-Mg-Sn SEM BSE Microstructure

The microstructure of materials is shaped not only by their chemical composition, but also by the thermomechanical processes used during the processing of a specific piece. The correct interpretation of the microstructure gives a rich source of information. This consists of several related steps, such as segmentation. Successful segmentation enables the qualitative as well as quantitative analysis of the individual microstructure components. The current paper deals with the segmentation and classification of four basic microstructure components of the Zn-Al-Mg-Sn alloy system. This is attempted with the help of several image processing techniques, where thresholding is the main one used. The investigated samples are the cast and annealed Zn-Al-Mg-Sn alloy bulks. The input data for this analysis are the SEM BSE images. These were taken for all alloys with a varying Sn content, covering a significant area of each investigated sample at different zoom levels. A semiautomatic algorithm running under Matlab is introduced. It addresses several tasks, such as preprocessing, noise filtering and decision methods. For the individual procedures, the time requirements for their execution are also indicated.

Missing the city for buildings? A critical review of pan-scalar map generalization and design in contemporary zoomable maps

ABSTRACT Most of the maps used today are what we call pan-scalar maps, i.e. interactive zoomable applications comprised of numerous maps of a particular area at different zoom levels (i.e. scales). We argue that such maps require a pan-scalar map design, which may differ significantly from established map design axioms and standards. This review is twofold. First, it reviews current practices in pan-scalar map design. Second, it summarizes and synthesizes literature about pan-scalar map design, as well as human-computer interaction (HCI) best practices for pan-scalar maps. The review of practices is based on a ScaleMaster analysis of the design of three popular pan-scalar maps: Google Maps, OpenStreetMap, and France's IGN Classic. Discussion centers on both stellar and subpar contemporary pan-scalar map design practices to help guide future practical pan-scalar designs and research on pan-scalar maps broadly.

Computationally efficient adaptive decompression for whole slide image processing.

Whole slide image (WSI) analysis is increasingly being adopted as an important tool in modern pathology. Recent deep learning-based methods have achieved state-of-the-art performance on WSI analysis tasks such as WSI classification, segmentation, and retrieval. However, WSI analysis requires a significant amount of computation resources and computation time due to the large dimensions of WSIs. Most of the existing analysis approaches require the complete decompression of the whole image exhaustively, which limits the practical usage of these methods, especially for deep learning-based workflows. In this paper, we present compression domain processing-based computation efficient analysis workflows for WSIs classification that can be applied to state-of-the-art WSI classification models. The approaches leverage the pyramidal magnification structure of WSI files and compression domain features that are available from the raw code stream. The methods assign different decompression depths to the patches of WSIs based on the features directly retained from compressed patches or partially decompressed patches. Patches from the low-magnification level are screened by attention-based clustering, resulting in different decompression depths assigned to the high-magnification level patches at different locations. A finer-grained selection based on compression domain features from the file code stream is applied to select further a subset of the high-magnification patches that undergo a full decompression. The resulting patches are fed to the downstream attention network for final classification. Computation efficiency is achieved by reducing unnecessary access to the high zoom level and expensive full decompression. With the number of decompressed patches reduced, the time and memory costs of downstream training and inference procedures are also significantly reduced. Our approach achieves a 7.2× overall speedup, and the memory cost is reduced by 1.1 orders of magnitudes, while the resulting model accuracy is comparable to the original workflow.

CNN Based Image Classification of Malicious UAVs

Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes. One way to manage this issue is to allow reporting of malicious UAVs (e.g., through a smartphone application) with the report including a photo of the UAV. It would be useful to able to automatically identify the type of UAV within the image in terms of the manufacturer and specific product identification using a trained image classification model. In this paper, we discuss the collection of images for three popular UAVs at different elevations and different distances from the observer, and using different camera zoom levels. We then train 4 image classification models based upon Convolutional Neural Networks (CNNs) using this UAV image dataset and the concept of transfer learning from the well-known ImageNet database. The trained models can classify the type of UAV contained in unseen test images with up to approximately 81% accuracy (for the Resnet-18 model), even though 2 of the UAVs represented in the UAV image dataset are visually similar, and the fact that the UAV image dataset contains images of UAVs that are a significant distance from the observer. This provides a motivation to expand the study in the future to include more UAV types and other usage scenarios (e.g., UAVs carrying loads).

Heuristic Evaluation of Microsoft Teams as an Online Teaching Platform: An Educators’ Perspective

The way that education is delivered changed significantly during the COVID-19 pandemic to be completely online in many countries for many institutions. Despite the fact that they are not online teaching platforms, virtual meeting platforms were utilized to deal with this transformation. One of the platforms Philadelphia University utilized for the unplanned shift to online teaching was Microsoft Teams. This paper examines how heuristic evaluation may be used to guide the evaluation of online meeting platforms for teaching and focuses on the use of heuristic evaluation to assess the level of usability of Microsoft Teams. The level of Zoom’s usability is also evaluated using heuristic evaluation in order to compare it to that of Microsoft Teams and to assess Microsoft Teams’ overall usability in comparison to other platforms being used for the same purpose. Microsoft Teams was identified as having a few issues that need to be addressed. Additionally, strengths, weaknesses, opportunities, and threats to Microsoft Teams’ usability were assessed.

Front Cover: A Theoretical Investigation of Cu+, Ni2+ and Co2+‐Exchanged Zeolites for Hydrogen Storage (ChemPhysChem 20/2022)

The Front Cover illustrates the application of ion-exchanged zeolites for hydrogen storage in the fuel cell vehicles. The different zoom levels show hydrogen molecule adsorbed on the extra-framework cations (Cu+, Co2+, or Ni2+) in CHA and AEI frameworks. Different levels of detail also represent our atomic level understanding and solution to a macro level problem. Cover design by Olus Ozbek. More information can be found in the Research Article by Olus Ozbek and Bahar Ipek.

Tidy tree: A New Layout for Phylogenetic Trees.

Many layouts exist for visualizing phylogenetic trees, allowing to display the same information (evolutionary relationships) in different ways. For large phylogenies, the choice of the layout is a key element, because the printable area is limited, and because interactive on-screen visualizers can lead to unreadable phylogenetic relationships at high zoom levels. A visual inspection of available layouts for rooted trees reveals large empty areas that one may want to fill in order to use less drawing space and eventually gain readability. This can be achieved by using the nonlayered tidy tree layout algorithm that was proposed earlier but was never used in a phylogenetic context so far. Here, we present its implementation, and we demonstrate its advantages on simulated and biological data (the measles virus phylogeny). Our results call for the integration of this new layout in phylogenetic software. We implemented the nonlayered tidy tree layout in R language as a stand-alone function (available at https://github.com/damiendevienne/non-layered-tidy-trees), as an option in the tree plotting function of the R package ape, and in the recent tool for visualizing reconciled phylogenetic trees thirdkind (https://github.com/simonpenel/thirdkind/wiki).

Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience.

The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma.

Task Level Decrease Commitment Due to Zoom Fatigue In Nursing Faculty, Padjadjaran University

The Covid-19 pandemic that hit the world, Indonesia is no exception, has had a huge impact on various sectors, one of which is the education sector. Many activities that must be done online cause students to experience fatigue commonly called zoom fatigue and affect the task commitment level of students. The purpose of this study was to determine the relationship between Zoom fatigue and level tasks commitment on the students-level I Faculty of Nursing, University of Padjadjaran. This type of research is quantitative with a descriptive correlation design. The data collection technique used in this study is a questionnaire using google forms. The instrument used in this study is the Zoom fatigue & Fatigue Scale to measure zoom fatigue and the Task Commitment level Scale to measure task commitment levels. Data analysis conducted in this study is to use Rank spearman to test the hypothesis test. The results of data analysis conducted using the Spearman correlation technique obtained a sig value of 0.119 > 0.05 then it was concluded that there is no significant relationship between Zoom fatigue and task commitment level in Level I Students of the Faculty of Nursing, University of Padjadjaran. Researchers are further advised to identify also related to other variables that affect the level of task commitment such as punishment, reward, and praise, as well as environmental conditions.

Task Level Decrease Commitment Due to Zoom Fatigue In Nursing Faculty, Padjadjaran University

The Covid-19 pandemic that hit the world, Indonesia is no exception, has had a huge impact on various sectors, one of which is the education sector. Many activities that must be done online cause students to experience fatigue commonly called zoom fatigue and affect the task commitment level of students. The purpose of this study was to determine the relationship between Zoom fatigue and level tasks commitment on the students-level I Faculty of Nursing, University of Padjadjaran. This type of research is quantitative with a descriptive correlation design. The data collection technique used in this study is a questionnaire using google forms. The instrument used in this study is the Zoom fatigue & Fatigue Scale to measure zoom fatigue and the Task Commitment level Scale to measure task commitment levels. Data analysis conducted in this study is to use Rank spearman to test the hypothesis test. The results of data analysis conducted using the Spearman correlation technique obtained a sig value of 0.119 > 0.05 then it was concluded that there is no significant relationship between Zoom fatigue and task commitment level in Level I Students of the Faculty of Nursing, University of Padjadjaran. Researchers are further advised to identify also related to other variables that affect the level of task commitment such as punishment, reward, and praise, as well as environmental conditions.

Generation of Istanbul road data set using Google Map API for deep learning-based segmentation

ABSTRACT Deep learning architectures are widely used for road segmentation studies. Data sets reflect the characteristics of the study region in which they are generated. The models that are trained with the data sets are unable to accurately forecast the region outside the area covered by the data sets. That is why local data sets must be generated to meet the requirements of navigation, military activities, urban planning and health management applications where road data are indispensable. In this study, satellite images and corresponding mask images of Istanbul city at different zoom levels were generated via Google Map Platform. Trained models were produced using Deep Residual U-Net. This study consists of three parts. The first part includes training each zoom level, from 14 to 17, separately. The recall statistics of models were found as 95%, 94%, 92% and 86%, and corresponding F1-Score statistics were calculated as 95%, 95%, 92% and 86%, respectively. Next, consecutive training with priory weight and regularization on satellite images were tested to increase the prediction results. The consecutive model and regularization methods resulted in an increase in both recall and F1-Score by 2−3%. In the third part, a new model was generated by combining all images which were produced at different levels. The combined model was found successful for the prediction of roads in all zoom levels. Finally, the performance of these models trained with the Istanbul data set was compared with well-known DeepGlobe and Massachusetts road data sets. Hence, this study illustrated that generating all data sets specifically for the area of interest is crucial.

Design decisions for multidimensional digital and interactive basemaps of urban environments

ABSTRACT Multidimensional digital and interactive basemaps are of high interest for many different user groups coming from various domains. There are already some design principles available for creating digital 3D maps, many of them coming from classical 2D map design, but there is still a need for new design strategies and principles for digital and interactive 3D and 4D basemaps for urban environments. In this article, we present the results of a comprehensive online survey with 175 participants. The aim of this conducted survey was to find out more about the preferences of different user groups regarding the content of digital 3D and 4D basemaps for urban environments, the zoom levels at which different content (such as buildings, landmarks, vegetation, labels, live data) should be visible, the highest shown level of detail as well the overall design (with regard to color, classification and symbology) of these contents. In addition, we also deal with questions about desired map elements and functionalities for an interactive multidimensional basemap and present first assessments of a developed 4D basemap demonstrator. As a final result, we conclude with two tables that provide advice for specific design decisions for creating digital and interactive 3D and 4D basemaps of urban environments for orientation purposes.

Pioneering easy-to-use forestry data with Forest Explorer

Forest Explorer is a web tool that can be used to easily browse the contents of the Cross-Forest dataset, a Linked Open Data resource containing the forestry inventory and land cover map from Spain. The tool is purposed for domain experts and lay users to facilitate the exploration of forestry data. Since these two groups are not knowledgable on Semantic Web, the user interface is designed to hide the complexity of RDF, OWL or SPARQL. An interactive map is provided for this purpose, allowing users to navigate to the area of interest and presenting forestry data with different levels of detail according to the zoom level. Forest Explorer offers different filter controls and is localized to English and Spanish. All the data is retrieved from the Cross-Forest and DBpedia endpoints through the Data manager. This component feeds the different Feature managers with the data needed to be displayed in the map. The Data manager uses a reduced set of SPARQL templates to accommodate any data request of the Feature managers. Caching and smart geographic querying are employed to limit data exchanges with the endpoint. A live version of the tool is freely available for everybody that wants to try it – any device with a modern browser should be sufficient to test it. Since December 2019, more than 3,200 users have employed Forest Explorer and it has appeared 12 times in the Spanish media. Results from a user study with 28 participants (mainly domain experts) show that Forest Explorer can be used to easily navigate the contents of the Cross-Forest dataset. No important limitations were found, only feature requests such as the integration of new datasets from other countries that are part of our future work.

Prioritization‐based subsampling quality assessment methodology for mobility‐related information systems

Mobility-related information systems, such as on-street parking information (OSPI) systems have become more popular in the original equipment manufacturer (OEM) industry over the last decade. However, there is a lack of methods to assess their quality at a large scale. This paper introduces a data-driven methodology to measure the true quality by fleet data prioritization-based subsampling strategies (PSSs). It is applied to the use case of OSPI using parking events (PE), but is applicable to other mobility-related information systems utilizing their respective fleet data. PSSs are defined based on neighbourhoods and time periods. Each PSS generates a unique set of spatio-temporally important areas at different quadkey zoom levels over 168 week-hours, called slices. The importance weight in each slice depends on the volume of PE within them. The algorithm for each PSS automatically selects important areas and time frames that are vital to be observed. Sample prediction models are used for the benefits assessment of the methodology by comparing it against non-prioritized randomized selection of ground truth. It is proven that the methodology can lessen the effort of ground truth collection, while maintaining the amount of information necessary to assess the true quality of a prediction model.

Consistency-Aware Map Generation at Multiple Zoom Levels Using Aerial Image

The multilevel tiled map service is widely used and serves as a kind of digital infrastructure. These map tiles are usually rendered from vector data, whose update needs to walk or drive with professional equipment to check every point of interest. This leads to inconvenience and expensive cost in timely updating maps. Compared with vector data, aerial images are much easier and cheaper to obtain. In this article, we propose a novel multilevel map (MLM) generation framework that can automatically generate accurate and consistent maps with multiple zoom levels from aerial images. It consists of a level-aware map generator and a consistency-aware map generator. The level-aware map generator is able to generate accurate initial maps with realistic details for each zoom level. The consistency-aware map generator regards the initial maps at each zoom level as a sequence and builds the connection between them, so as to guarantee content consistency between maps at different zoom levels. Furthermore, we collect a large-scale high-quality dataset called MLM for map generation at multiple zoom levels. Experiments on our MLM dataset show that our method outperforms the previous state-of-the-art map generation methods on both comprehensive quantitative metrics and perceptual quality.

An analysis of pathologists’ viewing processes as they diagnose whole slide digital images

Although pathologists have their own viewing habits while diagnosing, viewing behaviors leading to the most accurate diagnoses are under-investigated. Digital whole slide imaging has enabled investigators to analyze pathologists’ visual interpretation of histopathological features using mouse and viewport tracking techniques. In this study, we provide definitions for basic viewing behavior variables and investigate the association of pathologists' characteristics and viewing behaviors, and how they relate to diagnostic accuracy when interpreting whole slide images. We use recordings of 32 pathologists’ actions while interpreting a set of 36 digital whole slide skin biopsy images (5 sets of 36 cases; 180 cases total). These viewport tracking data include the coordinates of a viewport scene on pathologists’ screens, the magnification level at which that viewport was viewed, as well as a timestamp. We define a set of variables to quantify pathologists' viewing behaviors such as zooming, panning, and interacting with a consensus reference panel’s selected region of interest (ROI). We examine the association of these viewing behaviors with pathologists’ demographics, clinical characteristics, and diagnostic accuracy using cross-classified multilevel models. Viewing behaviors differ based on clinical experience of the pathologists. Pathologists with a higher caseload of melanocytic skin biopsy cases and pathologists with board certification and/or fellowship training in dermatopathology have lower average zoom and lower variance of zoom levels. Viewing behaviors associated with higher diagnostic accuracy include higher average and variance of zoom levels, a lower magnification percentage (a measure of consecutive zooming behavior), higher total interpretation time, and higher amount of time spent viewing ROIs. Scanning behavior, which refers to panning with a fixed zoom level, has marginally significant positive association with accuracy. Pathologists’ training, clinical experience, and their exposure to a range of cases are associated with their viewing behaviors, which may contribute to their diagnostic accuracy. Research in computational pathology integrating digital imaging and clinical informatics opens up new avenues for leveraging viewing behaviors in medical education and training, potentially improving patient care and the effectiveness of clinical workflow.

Generating Multiscale Maps From Satellite Images via Series Generative Adversarial Networks

Considering the success of generative adversarial networks (GANs) for image-to-image translation, researchers have attempted to translate remote sensing images (RSIs) to maps (rs2map) through GAN for cartography. However, these studies involved limited scales, which hinders multi-scale map creation. By extending their method, multi-scale RSIs can be trivially translated to multi-scale maps (multi-scale rs2map translation) through scale-wise rs2map models trained for certain scales (parallel strategy). However, this strategy has two theoretical limitations. First, inconsistency between various spatial resolutions of multi-scale RSIs and object generalization on multi-scale maps (RS-m inconsistency) increasingly complicate the extraction of geographical information from RSIs for rs2map models with decreasing scale. Second, as rs2map translation is cross-domain, generators incur high computation costs to transform the RSI pixel distribution to that on maps. Thus, we designed a series strategy of generators for multi-scale rs2map translation to address these limitations. In this strategy, high-resolution RSIs are inputted to an rs2map model to output large-scale maps, which are translated to multi-scale maps through series multi-scale map translation models. The series strategy avoids RS-m inconsistency as inputs are high-resolution large-scale RSIs, and reduces the distribution gap in multi-scale map generation through similar pixel distributions among multi-scale maps. Our experimental results showed better quality multi-scale map generation with the series strategy, as shown by average increases of 11.69%, 53.78%, 55.42%, and 72.34% in the structural similarity index, edge structural similarity index, intersection over union (road), and intersection over union (water) for data from Mexico City and Tokyo at zoom level 17-13.