Investigating the effectiveness of an efficient label placement method using eye movement data

This paper focuses on improving the efficiency and effectiveness of dynamic and interactive maps in relation to the user. A label placement method with an improved algorithmic efficiency is presented. Since this algorithm has an influence on the actual placement of the name labels on the map, it is tested if this efficient algorithms also creates more effective maps: how well is the information processed by the user. We tested 30 participants while they were working on a dynamic and interactive map display. Their task was to locate geographical names on each of the presented maps. Their eye movements were registered together with the time at which a given label was found. The gathered data reveal no difference in the user's response times, neither in the number and the duration of the fixations between both map designs. The results of this study show that the efficiency of label placement algorithms can be improved without disturbing the user's cognitive map. Consequently, we created a more efficient map without affecting its effectiveness towards the user

An Algorithmic Framework for Labeling Road Maps

Given an unlabeled road map, we consider, from an algorithmic perspective, the cartographic problem to place non-overlapping road labels embedded in their roads. We first decompose the road network into logically coherent road sections, e.g., parts of roads between two junctions. Based on this decomposition, we present and implement a new and versatile framework for placing labels in road maps such that the number of labeled road sections is maximized. In an experimental evaluation with road maps of 11 major cities we show that our proposed labeling algorithm is both fast in practice and that it reaches near-optimal solution quality, where optimal solutions are obtained by mixed-integer linear programming. In comparison to the standard OpenStreetMap renderer Mapnik, our algorithm labels 31% more road sections in average.Comment: extended version of a paper to appear at GIScience 201

Pictures in Your Mind: Using Interactive Gesture-Controlled Reliefs to Explore Art

Tactile reliefs offer many benefits over the more classic raised line drawings or tactile diagrams, as depth, 3D shape, and surface textures are directly perceivable. Although often created for blind and visually impaired (BVI) people, a wider range of people may benefit from such multimodal material. However, some reliefs are still difficult to understand without proper guidance or accompanying verbal descriptions, hindering autonomous exploration. In this work, we present a gesture-controlled interactive audio guide (IAG) based on recent low-cost depth cameras that can be operated directly with the hands on relief surfaces during tactile exploration. The interactively explorable, location-dependent verbal and captioned descriptions promise rapid tactile accessibility to 2.5D spatial information in a home or education setting, to online resources, or as a kiosk installation at public places. We present a working prototype, discuss design decisions, and present the results of two evaluation studies: the first with 13 BVI test users and the second follow-up study with 14 test users across a wide range of people with differences and difficulties associated with perception, memory, cognition, and communication. The participant-led research method of this latter study prompted new, significant and innovative developments

Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement

Over the recent years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains, including robotics, self-driving cars, and finance. In this paper, we are introducing Reinforcement Learning (RL) to label placement, a complex task in data visualization that seeks optimal positioning for labels to avoid overlap and ensure legibility. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning to learn the label placement strategy, the first machine-learning-driven labeling method, in contrast to the existing hand-crafted algorithms designed by human experts. To facilitate RL learning, we developed an environment where an agent acts as a proxy for a label, a short textual annotation that augments visualization. Our results show that the strategy trained by our method significantly outperforms the random strategy of an untrained agent and the compared methods designed by human experts in terms of completeness (i.e., the number of placed labels). The trade-off is increased computation time, making the proposed method slower than the compared methods. Nevertheless, our method is ideal for scenarios where the labeling can be computed in advance, and completeness is essential, such as cartographic maps, technical drawings, and medical atlases. Additionally, we conducted a user study to assess the perceived performance. The outcomes revealed that the participants considered the proposed method to be significantly better than the other examined methods. This indicates that the improved completeness is not just reflected in the quantitative metrics but also in the subjective evaluation by the participants

Algorithms for Automatic Label Placement

Práce popisuje problém automatického umísťování popisků do mapy. Jednotlivé bodové, čárové a plošné objekty v mapě je třeba označit odpovídajícími textovými či obrázkovými popisky. Tyto popisky je nutné rozmístit tak, aby se vzájemně nepřekrývaly a zároveň byly jasně přiřaditelné k odpovídajícím objektům. O problému je známo, že je NP-těžký a nalezení optimálního rozmístění všech popisků je výpočetně velmi náročné i pro nejjednodušší mapy. Pozornost je věnována umísťování popisků označujících bodové a čárové objekty, včetně prvního kroku obnášejícího přípravu možných pozic pro umístění těchto popisků, při dodržení běžných kartografických pravidel pro rozmísťování popisků. Následně jsou na problém aplikovány tři různé druhy algoritmů -- greedy ("hladové") algoritmy v kombinaci s lokálním prohledáváním, matematická optimalizace (v podobě 0-1 celočíselného programování) a genetické algoritmy. Popsané algoritmy jsou v softwarové části práce implementovány a na závěr porovnány na několika různých datových sadách, vycházejících z reálných geografických podkladů a z náhodně vygenerovaných map. Závěrečné srovnání se zaměřuje na kvalitu výsledného rozmístění (dle metrik definovaných v práci), času potřebnému k nalezení řešení a také na determinističnost daných algoritmů.Thesis describes the problem of automatic map label placement. Various point, line or area features in maps must be marked with matching text or graphic labels. These labels have to be placed so they do not overlap with each other and they are clearly associable with corresponding map features. The problem is known to be NP-hard and finding optimal positions of all map labels is highly computationally expensive, even for the simplest maps. Focus is given to the placement of labels describing point and line map features, including the initial phase of enumerating possible label positions, respecting the basic cartographic rules common for those labels. Afterwards, three different algorithm types are applied to the problem itself -- greedy algorithms (in combination with local search optimization), mathematical optimization (0-1 integer programming) and genetic algorithms. Ultimately, the described algorithms are implemented in the software part of the work and compared on various data sets, based on both real world geographical data and randomly generated maps. The final comparison focuses especially on the quality of the result (scored by the metrics defined in the thesis), time needed to find the solution and determinism of the given algorithms

Automatic Label Placement for Technical Drawings

A lot of research has been done to automatically label geographical maps. In the field of study, other types of images, like technical drawings, are almost completely disregarded. In enormous industrial projects, like shipbuilding or designing process plant facilities, there are countless of drawings produced. Labeling these drawings needs to be done manually by the designers and automating the labeling of these drawings would save countless of hours of the designers time. This thesis aims to develop a method to conduct automatic labeling in the context of technical drawings. The main target are drawings produced in process plant design and marine industry, but the final solution is general enough to be used for other types of technical drawings as well. To achieve this, a set of requirements for the task is collected and an implementation principle for automatic labeling of technical drawings is presented. The solution is based on prior research done regarding automatic labeling of geographical maps as well as a new label candidate generation heuristic. The developed solution is evaluated by conducting an empirical study on an implementation of the new principle. The empirical study indicates that the solution is practical enough to be used in real environments. However, it also reveals some improvements needed in the quality of the labeling as well as its performance.Maantieteellisten karttojen automaattinen labelointi on laajalti tutkittu aihe. Tutkimusalalla muun tyyppiset kuvat, kuten tekniset piirustukset, eivät ole juuri saaneet huomiota. Suurissa teollisissa projekteissa, kuten laivojen tai prosessiteollisuuslaitosten suunnittelussa, tuotetaan suuria määriä piirustuksia. Projekteissa suunnittelijat joutuvat itse labeloimaan kyseiset piirustukset, mikä vie merkittävästi aikaa. Näiden piirustusten automaattinen labelointi säästäisi lukemattomia työtunteja suunnittelijoilta. Tämä diplomityö pyrkii kehittämään automaattisen labelointimenetelmän teknisille piirustuksille. Päätavoite on laivanrakennuksessa ja prosessiteollisuuslaitosten suunnittelussa tuotettujen piirustusten labeloinnin automatisointi. Kehitetty menetelmä on kuitenkin yleiskäyttöinen myös muunkaltaisissa piirustuksissa. Työssä ensin kartoitetaan teknisten piirustusten automaattisen labeloinnin vaatimukset, minkä jälkeen kehitetään ko. vaatimukset täyttävä menetelmä. Menetelmä perustuu tutkimustietoon karttojen automaattisesta labeloinnista sekä tässä työssä kehitettyyn uuteen labeleiden kandidaattien generointi -heuristiikkaan. Kehitetty menetelmä arvioidaan empiirisellä tutkimuksella. Empiirinen tutkimus osoittaa, että kehitetty menetelmä on riittävän käyttökelpoinen todellisessa ympäristössä. Menetelmässä ilmeni kuitenkin vielä parannettavaa labeloinnin laadussa sekä menetelmän suoritusnopeudessa