• MeSH
• Humans MeSH
• Task Performance and Analysis MeSH
• Check Tag
• Humans MeSH

• MeSH
• Time MeSH
• Behavior MeSH
• Humanities MeSH
• Humans MeSH
• Psychophysiology MeSH
• Check Tag
• Humans MeSH

• MeSH
• Motor Skills MeSH
• Task Performance and Analysis MeSH
• Computers utilization   MeSH
• Visual Perception MeSH

Příručka, která se zaměřuje na eye-tracking při hodnocení a optimalizaci map, zejména na praktickou stránku výzkumu. Určeno odborné veřejnosti.; Publikace nabízí komplexní pohled na využití sledování pohybu očí při hodnocení a optimalizaci map. Čtenáři se v ní dozvědí teoretické základy, na kterých technologie eye-tracking funguje, seznámí se s různými způsoby měření pohybu očí a rovněž získají základní přehled o oblastech, ve kterých je eye-tracking využíván, samozřejmě s důrazem na kartografii. Značná část knihy je zaměřena prakticky. Nejprve je popsáno doporučené vybavení eye-tracking laboratoře a jsou představeny tři typy eye-trackerů. Následují kapitoly zaměřené na přípravu, design a průběh experimentu, pre-processing, validaci a čištění dat, a konečně na samotné vyhodnocení naměřených pohybů očí. V těchto kapitolách autor vycházel z vlastních zkušeností, jež se snažil prostřednictvím této publikace předat dalším výzkumníkům.

• MeSH
• Electronic Data Processing MeSH
• Geographic Mapping MeSH
• Cognitive Science MeSH
• Eye Movements MeSH
• Eye-Tracking Technology MeSH
• Research MeSH
• Vision, Ocular MeSH
• Publication type
• Handbook MeSH

• Conspectus
• Geodezie. Kartografie
• NML Fields
• věda a výzkum
• neurovědy

• MeSH
• Electrocardiography MeSH
• Humans MeSH
• Higher Nervous Activity MeSH
• Check Tag
• Humans MeSH

Tato eye-trackingová studie byla navržena tak, aby byla schopna zachytit efekt umělecké transformace fotografických předloh do maleb a její vliv na okulomotorické chování divákova pohledu. Unikátní set stimulů sestával z maleb a fotografií, které sloužily autorům obrazů jako obrazový zdroj pro jejich tvorbu. Mezi zdrojovými fotografiemi a malbou byly pomocí statistické analýzy naměřených dat nalezeny signifikantní rozdíly v bazálních očních pohybech (BOP) a celkově prostudované ploše stimulu. U maleb byla rychlost očních pohybů vyšší a celkový pohled diváka byl rozptýlenější. Na modulaci BOP se kromě celkové komplexity malby podílela i míra divákovy umělecké expertízy. Dále byly nalezeny signifikantní rozdíly ve srovnání fixačních map maleb oproti fotografiím. Tyto rozdíly mezi fixačními mapami mohou být interpretovány jako index umělecké transformace fotografie do malby, obsahující i složku umělecké intence. Ve světle teoretického pozadí umělecké intence, která v sobě zahrnuje i motorické akty umělce, je představena možnost použít záznam okulomotorického chování jako ukazatele divákovy inference umělecké intence. Tato možnost je ilustrována na třech případových studiích. Článek je v anglickém jazyce.

This eye-tracking study was designed to analyze the effect of artistic transformation of a photographic image to a painting on oculomotor behavior. The study employed unique set of stimuli, consisting of paintings and photographs used by authors of the paintings as direct source images. First, the differences in basic eye movement measurements and the explored area were investigated. In paintings, viewers moved their eyes quicker, and viewing was more dispersive. Second, relationship between the basic eye movement measurements and stylistic features (complexity, style expressivity and dynamism) as well as the top-down factor of expertise were analyzed. Furthermore, fixation maps of paintings versus photographs were compared and significant shifts were identified. The difference in fixation maps can be interpreted as a behavioral index of artistic transformations, including artist’s intention (AI). In the light of the theoretical background of AI that includes even motor acts, the idea of using oculomotor behavior as an index of viewer’s inference of AI was introduced.

• Keywords
• fixace, fixační mapy, transformace, modelování, styl, hodnocení,
• MeSH
• Photography * MeSH
• Humans MeSH
• Paintings * psychology   MeSH
• Eye Movements physiology   MeSH
• Eye-Tracking Technology instrumentation   statistics & numerical data   MeSH
• Art MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Case Reports MeSH
• Observational Study MeSH

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

• MeSH
• Algorithms MeSH
• Benchmarking * MeSH
• Cell Tracking * methods   MeSH
• Machine Learning MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH

Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.

• MeSH
• Algorithms MeSH
• Cell Line MeSH
• Cell Tracking methods   MeSH
• Time-Lapse Imaging methods   MeSH
• Microscopy, Fluorescence MeSH
• Humans MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH

Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualisations called Fix-images, which clearly depict reading difficulties. The second phase utilises the ResNet18 convolutional neural network for classifying these images. The INSIGHT method was tested on 35 child participants (13 dyslexic and 22 control readers) using three text-reading tasks, achieving a highest accuracy of 86.65%. Additionally, we cross-tested the method on an independent dataset of Danish readers, confirming the robustness and generalizability of our approach with a notable accuracy of 86.11%. This innovative approach not only provides detailed insight into eye movement patterns when reading but also offers a robust framework for the early and accurate diagnosis of dyslexia, supporting the potential for more personalised and effective interventions.

• MeSH
• Reading MeSH
• Child MeSH
• Dyslexia * physiopathology   diagnosis   classification   MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Fixation, Ocular * physiology   MeSH
• Eye Movements physiology   MeSH
• Eye-Tracking Technology * MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

• MeSH
• Humans MeSH
• Motor Activity MeSH
• Eye Movements MeSH
• Visual Perception MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH