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REVIEW 3 major objections 3 minor 48 references

A Step towards Automated and Generalizable Tactile Map Generation using Generative Adversarial Networks

T0 review · 3 major / 3 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that a conditional GAN can turn a street-level visual map into a simplified tactile map in one pass, reaching median F1 above 0.97 at a single zoom and retaining IoU above 87% when trained on two zooms and tested on…

desk verdict Honest proof-of-concept: the GAN learns Google's tactile styling well, but the proxy ground truth means usability remains untested. read the letter →

arxiv 2412.07191 v1 pith:BUAQPMQM submitted 2024-12-10 cs.CV

David G Hobson , Majid Komeili This is my paper · ORCID
classification cs.CV
keywords tactilemapsgenerativeadversarialnetworksPix2Piximage-to-imagetranslationmapsimplificationinpaintingaccessibilityvisualimpairment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that tactile map generation from street-level visual maps can be treated as an image-to-image translation problem and solved with a conditional generative adversarial network. The authors build a first-of-its-kind dataset of 6500 Google Maps locations at zooms 15–18, paired with simplified “tactile” renderings in which six line- and area-like features are colored and everything else is removed. They show that Pix2Pix models trained at a single zoom identify the six features, drop icons and text, and inpaint the leftovers, with median F1 and IoU above 0.97 across all features. Training the model on two zooms instead costs only a few percentage points and still generalizes to an unseen zoom and to world cities with non-English text. If the approach is valid, it would make automated tactile map production scale-invariant, region-independent, and independent of any particular tactile texture standard.

What carries the argument

The engine of the method is the Pix2Pix conditional GAN, an image-to-image translation architecture in which a generator learns to map an input RGB image to a target RGB image under an $\ell^1$ reconstruction loss and an adversarial loss from a PatchGAN discriminator. The generator is a UNet++ network, chosen here because of its success on segmentation-style tasks; the discriminator judges local image patches, which encourages the output to look realistic at the feature level. The task is set up so that the single output image simultaneously encodes a semantic segmentation (each of six features gets one color), a simplification (icons, labels, and other noise are omitted), and an inpainting (the holes left by removed elements are filled). Because the output is a color-labeled image rather than a physical texture map, the representation is agnostic to tactile standards, and textures can be assigned later during printing.

What would settle it

Feed the trained models the same source maps but pair them with tactile maps produced by professional tactile designers or validated by blind users; if the per-class IoU or F1 against those ground truths falls well below the reported 0.94–0.97, or if users cannot follow the raised output, the claim that the model generates usable tactile maps is refuted.

Watch

Extended reading notes

Core claim

The paper's claim is that a conditional GAN can perform the three visual tasks at the heart of tactile map creation—feature recognition, simplification, and inpainting—in a single forward pass. Using source-tactile pairs with streets, highways, parks, water, buildings, and medical facilities as the six target classes, the trained models convert a full Google street map into a clean, class-colored map with labels and icons removed. On the English test sets, the single-zoom models exceed 94% median classwise IoU and 98% median F1, and the two-zoom model does nearly as well while also interpolating to zoom 17 and extrapolating to zoom 15 with IoU still above 87% on every reported class. The same two-zoom model handles maps of cities in India, Brazil, Germany, Japan, and other unseen countries, removing text in non-Latin scripts as readily as English text. The paper frames this as a proof of concept, not a finished product, and states that the RGB class-colored output can be post-processed into any tactile texture scheme.

Load-bearing premise

The ground truth tactile maps are Google-Maps-API renderings with six features recolored and labels and icons removed, not maps designed by tactile specialists, and the paper does not test whether any output is legible by touch.

Editorial extensions

If this is right

  • At the trained zoom, the models identify all six target features and remove extraneous icons and text with median F1 above 0.98 and IoU above 0.94 on English test sets.
  • A model trained on zooms 16 and 18 keeps IoU above 0.87 when applied to zoom 15 and to unseen world cities, indicating the translation generalizes across scales and languages without retraining.
  • Because the output is a class-colored RGB image, the same model output can be rendered under different national or institutional tactile guidelines by choosing textures at print time.
  • The released dataset of 6500 locations across four zoom levels gives the research community a benchmark for street-view tactile map generation.
  • The three subtasks (segment, simplify, inpaint) are not map-specific, so the same architecture could be retrained for other tactile graphics such as plots, diagrams, or building interiors.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extension: If these pixel-overlap numbers survive comparison against designer-made tactile maps, automated production could remove the main cost of tactile maps—the manual simplification step—making on-demand printing practical in schools and transit agencies.
  • Extension: The reported scores measure agreement with Google's own styling, not usability; a legibility study with tactile readers would be the decisive next experiment, and until that is done the practical claim should be read as promising rather than established.
  • Extension: The model's success at erasing text in scripts it never saw suggests the learned 'remove everything that is not a mapped feature' rule is language-agnostic, which would make the approach portable to OpenStreetMap or Bing renderings if equivalent paired data were created.
  • Extension: A directly testable follow-up is to train the same two-zoom recipe on maps with point features (bus stops, traffic lights) and measure whether the model's scaling behavior degrades gracefully when the class count grows.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The paper introduces a source-tactile map pair dataset built by applying a Google Maps Static API style to street-view maps at four zoom levels across 6500 locations, and trains Pix2Pix-GAN models (with a UNet++ generator) to convert ordinary Google Maps images into simplified RGB 'tactile' representations. It evaluates single-zoom models at zooms 16 and 18 and a double-zoom model at zooms 15 through 18 on English and non-English city test sets, reporting median IoU and F1 values above 0.97 at the trained zooms and generally high, though lower, scores on extrapolated zooms and unseen world cities. The paper positions this as a proof-of-concept first step toward automated tactile map generation, explicitly deferring legibility analysis and consultation with people with visual impairments to future work.

Significance. If the contribution is read narrowly as 'GANs can learn Google's map-styling transformation and inpaint removed labels', the paper is a useful proof of concept: it releases a public dataset, documents architecture and training details transparently, and tests transfer across zoom levels and world regions. The strengths include the dataset DOI, the candid limitations section, and the systematic metric tables. However, the significance for actual tactile map generation rests on an unvalidated proxy target: the ground-truth 'tactile' images are Google Maps API stylings of the same source maps, and the paper explicitly disclaims legibility analysis in Appendix A.5. I therefore agree with the stress-test concern that the high pixel-overlap metrics mainly establish fidelity to Google's styling, not usability as tactile maps; this gap must be addressed in the framing or with additional validation.

major comments (3)
  1. [Section 3.2.2, Appendix A.1, Appendix A.5] The ground-truth tactile maps are generated by applying the Google Maps Static API style specification in Table A.6 to the same source maps; they are not produced by tactile designers, and Appendix A.5 states that no legibility analysis was performed and that some maps may not be legible without additional processing. Tables 1-5 therefore measure how well the models reproduce a deterministic style transformation of the input, and the abstract's claim that the models identify key map elements for tactile maps is under-supported. The paper should either add a small validation study with tactile designers or PVI users, or explicitly reframe the contribution as learning a map-simplification operator that is a first step toward, rather than an instance of, tactile map generation.
  2. [Section 3.4, Tables 1-5] The evaluation converts predicted RGB images to class maps using an L1 nearest-color assignment with a manually fixed threshold of 230, and all reported numbers are point estimates with no confidence intervals or random-seed variation. Because the six class colors are fixed and deliberately distinct, this assignment step makes the pixel metrics close to a color-fidelity check and likely overstates geometric and semantic quality; the authors should report variance across training seeds and ideally separate color errors from geometric/semantic errors by evaluating on the underlying class masks rather than on RGB closeness.
  3. [Section 4.2 and Appendix C] The claim that performance on the World test sets has 'All IoU metrics above 87%' is contradicted by the paper's own appendix: the World zoom-15 hospital class has median IoU 79.8 and mean 73.3 (Table C.10), and the World zoom-17 street and building medians fall below 85% (Table 5). This discrepancy affects the paper's generalization claim and should be corrected; the cross-region conclusion should be qualified to specific classes and zoom levels, with the main text reporting the full range of observed IoU values.
minor comments (3)
  1. [Table C.11] The Buildings row for the World zoom-16 test set reports median IoU 0.1 and median F1 0.2, but zoom-16 ground truth contains no buildings; this row should be marked N/A or explicitly explained as expected behavior, otherwise it reads as a model failure.
  2. [Conclusion vs. Abstract] The conclusion states that models trained at two zoom levels achieve IoU and F1 scores 'better than 0.95 across all classes', while the abstract claims better than 0.97; Table 1 shows a double-zoom street IoU of 94.8, so the two statements should be reconciled with a consistent qualifier (for example, restricting the 0.97 claim to trained zooms or using a specific threshold).
  3. [Reproducibility] The paper states that all code is written in Python but does not provide a code repository link; adding one would improve reproducibility beyond the already-released dataset.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the model is trained and evaluated against externally generated Google-API-style ground truth, so the reported metrics are self-contained image-translation results; legibility validity is a separate concern.

full rationale

The claimed derivation chain is: (1) source maps are downloaded from Google Maps; (2) ground-truth 'tactile' maps are produced by applying a fixed Google Maps Static API style to the same map tiles (Section 3.2.2, Table A.6); (3) Pix2Pix-GAN models are trained to translate source to ground truth; (4) IoU/F1 are computed on held-out English and World test sets at trained and untrained zooms (Tables 1-5). No step in this chain is equivalent to the model output by construction: the ground truth is generated by an external, fixed API style that does not depend on the trained model, evaluation is on held-out locations and zooms, and the metrics compare predictions to external targets. The paper's acknowledged weakness (Appendix A.5: no legibility analysis; Section 5: PVI consultation essential) means the targets may not be valid tactile maps, and the high pixel-overlap scores may only demonstrate reproduction of Google's styling. That is a construct-validity limitation of the benchmark, not a circularity in the derivation. The only self-references are to the released dataset (Hobson and Komeili, 2023) and to the same-laboratory thesis (Heydari, 2022) whose architecture is adopted; neither is used to justify the empirical result in a load-bearing way. Therefore no circular step is exhibited, and the appropriate finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The main uncharged premise is that the API-styled rendering is a sufficient proxy for a tactile map. The model itself uses standard GAN training with default hyperparameters; no scientific constants are fitted.

free parameters (1)
  • Pixel classification distance threshold = 230 (L1 distance)
    Used in evaluation (Section 3.4) to assign predicted pixels to one of the six tactile classes; changing this threshold changes reported IoU/F1.
assumptions (3)
  • domain assumption Google Maps API styling produces valid ground truth tactile maps
    The target images are generated by applying a color and label-removal style to the same source maps (Section 3.2.2, Appendix A.1); no tactile designer or user validation is involved.
  • domain assumption Pixel-level IoU/F1 against the API-styled target measures performance for tactile map generation
    Evaluation uses only segmentation metrics; Appendix A.5 explicitly disclaims legibility analysis.
  • domain assumption The six selected features (streets, highways, buildings, parks, water, hospitals) are the relevant set
    Features chosen based on literature and visual distinguishability (Section 3.2.1), not on a user study in this paper.

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Cite this review

Pith. "Pith review of A Step towards Automated and Generalizable Tactile Map Generation using Generative Adversarial Networks." pith.science (2026). https://pith.science/paper/BUAQPMQM

@misc{pith2026241207191,
  author       = {Pith},
  title        = {Pith review of: A Step towards Automated and Generalizable Tactile Map Generation using Generative Adversarial Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BUAQPMQM}},
  note         = {Machine review of arXiv:2412.07191}
}
read the original abstract

Blindness and visual impairments affect many people worldwide. For help with navigation, people with visual impairments often rely on tactile maps that utilize raised surfaces and edges to convey information through touch. Although these maps are helpful, they are often not widely available and current tools to automate their production have similar limitations including only working at certain scales, for particular world regions, or adhering to specific tactile map standards. To address these shortcomings, we train a proof-of-concept model as a first step towards applying computer vision techniques to help automate the generation of tactile maps. We create a first-of-its-kind tactile maps dataset of street-views from Google Maps spanning 6500 locations and including different tactile line- and area-like features. Generative adversarial network (GAN) models trained on a single zoom successfully identify key map elements, remove extraneous ones, and perform inpainting with median F1 and intersection-over-union (IoU) scores of better than 0.97 across all features. Models trained on two zooms experience only minor drops in performance, and generalize well both to unseen map scales and world regions. Finally, we discuss future directions towards a full implementation of a tactile map solution that builds on our results.

Figures

Figures reproduced from arXiv: 2412.07191 by the authors.

Figure 1
Figure 1. Source (left), tactile (middle), and a possible textured tactile map (right). These [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Sample model outputs from the zoom 16 and 18 [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Sample model outputs from the zoom 15 and 17 [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Sample model outputs illustrating limitations of the double and single zoom [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 34 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  2. [2]

    , author Aghdam, E.K

    author Azad, R. , author Aghdam, E.K. , author Rauland, A. , author Jia, Y. , author Avval, A.H. , author Bozorgpour, A. , author Karimijafarbigloo, S. , author Cohen, J.P. , author Adeli, E. , author Merhof, D. , year 2022 . title Medical image segmentation review: The success of u-net . http://arxiv.org/abs/2211.14830 arXiv:2211.14830

    arXiv 2022

  3. [3]

    title Guidelines and standards for tactile graphics

    author BANA , year 2010 . title Guidelines and standards for tactile graphics . http://www.brailleauthority.org/tg/

    work page 2010

  4. [4]

    , author Selvam, A

    author Bollepalli, S.C. , author Selvam, A. , author Patel, M. , author Kuchhal, A. , author Sahel, J.A. , author Chhablani, J. , author Vupparaboina, K.K. , year 2024 . title Evaluating gan-based synthesis for image segmentation: Pigment epithelial detachment segmentation in oct images . journal Investigative Ophthalmology & Visual Science volume 65 , pa...

    work page 2024

  5. [5]

    , author Bauer, M.A

    author Bose, R. , author Bauer, M.A. , author Jürgensen, H. , year 2020 . title Utilizing machine learning models for developing a comprehensive accessibility system for visually impaired people , in: booktitle Proceedings of the International Conference on Computers Helping People with Special Needs , p. pages 83

    work page 2020

  6. [6]

    , author Flaxman, S.R

    author Bourne, R.R.A. , author Flaxman, S.R. , author Braithwaite, T. , author Cicinelli, M.V. , author Das, A. , author Jonas, J.B. , author Keeffe, J. , author Kempen, J.H. , author Leasher, J. , author Limburg, H. , author Naidoo, K. , author Pesudovs, K. , author Resnikoff, S. , author Silvester, A. , author Stevens, G.A. , author Tahhan, N. , author ...

    work page 2017

  7. [7]

    , author Choi, M

    author Choi, Y. , author Choi, M. , author Kim, M. , author Ha, J.W. , author Kim, S. , author Choo, J. , year 2018 . title Stargan: Unified generative adversarial networks for multi-domain image-to-image translation , in: booktitle 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. pages 8789--8797 . :10.1109/CVPR.2018.00916

    arXiv 2018

  8. [8]

    , author Malaier, S

    author Engelstad, C. , author Malaier, S. , author O’Day, B. , author Sheffield, R. , author Schaller, E. , author Snow, D. , author Turner, L. , author Walker, S. , year 2019 . title Accessible communities analysis . journal Report. American Printing House for the Blind

    work page 2019

Show all 48 references
  1. [9]

    , author Jansson, G

    author Eriksson, Y. , author Jansson, G. , author Strucel, M. , year 2003 . title Tactile maps--guidelines for the production of maps for the visually impaired enskede . journal Sweden: The Swedish Braille Authority

    2003

  2. [10]

    , author Ungar, S

    author Espinosa, M. , author Ungar, S. , author Ochaita, E. , author Blades, M. , author Spencer, C. , year 1998 . title Comparing methods for introducing blind and visually impaired people to unfamiliar urban environments . journal Journal of Environmental Psychology volume 1...

    1998

  3. [11]

    , author Azar, A.T

    author Ganesan, J. , author Azar, A.T. , author Alsenan, S. , author Kamal, N.A. , author Qureshi, B. , author Hassanien, A.E. , year 2022 . title Deep learning reader for visually impaired . journal Electronics volume 11 . https://www.mdpi.com/2079-9292/11/20/3335, :10.3390/e...

    2022 doi

  4. [12]

    , author Gonzalez, C

    author Gonzalez, R. , author Gonzalez, C. , author Guerra-Gomez, J.A. , year 2019 . title Tactiled: Towards more and better tactile graphics using machine learning , in: booktitle Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility , p...

    2019

  5. [13]

    , year 2022

    author Heydari, M. , year 2022 . title Towards End-to-end Semantically aware 2D Plot Tactile Generation . Master's thesis. Carleton University

    2022

  6. [14]

    , author Komeili, M

    author Hobson, D.G. , author Komeili, M. , year 2023 . title Street-view tactile maps from google maps . howpublished https://doi.org/10.20383/103.0797

    2023 doi

  7. [15]

    , author Kabata, T

    author Ishibashi, K. , author Kabata, T. , author Oda, T. , author Watanabe, K. , author Watanabe, T. , author Takaoka, Y. , author Kita, S. , year 2013 . title Easy detectable symbol’s size in tactile maps: A study with persons familiar and unfamiliar with braille . journal J...

    2013

  8. [16]

    , author Zhu, J.Y

    author Isola, P. , author Zhu, J.Y. , author Zhou, T. , author Efros, A.A. , year 2017 . title Image-to-image translation with conditional adversarial networks , in: booktitle 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. pages 5967--5976 . :10.1...

    2017 doi

  9. [17]

    , year 1998

    author Jacobson, R.D. , year 1998 . title Navigating maps with little or no sight: An audio-tactile approach , in: booktitle Content Visualization and Intermedia Representations ( CVIR ' 98) , pp. pages 95--102 . https://aclanthology.org/W98-0214

    1998

  10. [18]

    title Handbook for producing pedestrian tactile maps

    author Japan Committee of Social Welfare Facilities for the Blind , year 1984 . title Handbook for producing pedestrian tactile maps

    1984

  11. [19]

    , author Pyayt, A

    author Kaplan, H. , author Pyayt, A. , year 2022 . title Development of the tactile map creator ( TMC ) application . journal Disabilities volume 2 , pages 19--27 . :10.3390/disabilities2010002

    2022 doi

  12. [20]

    , author Lee, J

    author Kim, S. , author Lee, J. , author Jeong, K. , author Lee, J. , author Hong, T. , author An, J. , year 2024 . title Automated door placement in architectural plans through combined deep-learning networks of resnet-50 and pix2pix-gan . journal Expert Systems with Applicat...

    2024

  13. [21]

    , author Ba, J

    author Kingma, D.P. , author Ba, J. , year 2017 . title Adam: A method for stochastic optimization . http://arxiv.org/abs/1412.6980 arXiv:1412.6980

    2017 arXiv

  14. [22]

    , author Wand, M

    author Li, C. , author Wand, M. , year 2016 . title Precomputed real-time texture synthesis with markovian generative adversarial networks , in: booktitle Computer Vision -- ECCV 2016 , publisher Springer International Publishing , address Cham . pp. pages 702--716

    2016

  15. [23]

    , author Wang, K

    author Lin, Y. , author Wang, K. , author Yi, W. , author Lian, S. , year 2019 . title Deep learning based wearable assistive system for visually impaired people , in: booktitle 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) , pp. pages 2549--2557 ....

    2019

  16. [24]

    , year 2015

    author Lobben, A.K. , year 2015 . title Tactile maps and mapping , in: booktitle Journal of Blindness Innovation and Research

    2015

  17. [25]

    , year 2018

    author Martínez Piazuelo, F. , year 2018 . title ML to categorize and find tactile graphics . type Technical Report . Fundación Universitaria de Ciencias de la Salud. http://hdl.handle.net/1992/40157

    2018

  18. [26]

    , year 2024

    author Metlek, S. , year 2024 . title Cellsegunet: an improved deep segmentation model for the cell segmentation based on unet++ and residual unet models . journal Neural Computing and Applications https://doi.org/10.1007/s00521-023-09374-3, :10.1007/s00521-023-09374-3

    2024 doi

  19. [27]

    , author Landau, S

    author Miele, J.A. , author Landau, S. , author Gilden, D. , year 2006 . title Talking tmap: Automated generation of audio-tactile maps using smith-kettlewell's tmap software . journal British Journal of Visual Impairment volume 24 , pages 93--100 . https://doi.org/10.1177/026...

    2006 doi

  20. [28]

    Tactual and Bold Print Mapping Committee , year 1987

    author N.S.W. Tactual and Bold Print Mapping Committee , year 1987 . title A guide for the production of tactual and bold print maps . edition 3rd ed

    1987

  21. [29]

    , author Charitakis, K

    author Papadopoulos, K. , author Charitakis, K. , author Kartasidou, L. , author Kouroupetroglou, G. , author Sakalli Gumus, S. , author Stylianidis, E. , author Stiefelhagen, R. , author M \"u ller, K. , author Yilmaz, E. , author Jaworek, G. , author Polimeras, C. , author S...

    2016

  22. [30]

    , author Ungar, S

    author Rowell, J. , author Ungar, S. , year 2003 a. title Feeling your way : A tactile map user survey , pp. pages 652--659 . https://api.semanticscholar.org/CorpusID:55885039

    2003

  23. [31]

    , author Ungar, S

    author Rowell, J. , author Ungar, S. , year 2003 b. title The world of touch: an international survey of tactile maps. part 1: production . journal British Journal of Visual Impairment volume 21 , pages 98--104 . https://doi.org/10.1177/026461960302100303, :10.1177/02646196030...

    2003 doi

  24. [32]

    , author Nogueira, R

    author Régis, T. , author Nogueira, R. , year 2019 . title Contribuição para o ensino-aprendizagem de geografia: a padronização de mapas táteis

    2019

  25. [33]

    , author Hasan, K

    author Shabiralyani, G. , author Hasan, K. , author Hamad, N. , author Iqbal, N. , year 2015 . title Impact of visual aids in enhancing the learning process case research: District dera ghazi khan. journal Journal of Education and Practice volume 6 , pages 226--233 . https://a...

    2015

  26. [34]

    , author Pathak, K

    author Surana, S. , author Pathak, K. , author Gagnani, M. , author Shrivastava, V. , author R, M.T. , author Madhuri G, S. , year 2022 . title Text extraction and detection from images using machine learning techniques: A research review , in: booktitle 2022 International Con...

    2022

  27. [35]

    , author Stangl, A

    author Suzuki, R. , author Stangl, A. , author Gross, M.D. , author Yeh, T. , year 2017 . title Fluxmarker: Enhancing tactile graphics with dynamic tactile markers . http://arxiv.org/abs/1708.03783 arXiv:1708.03783

    2017 arXiv

  28. [36]

    title TMAP FAQ and troubleshooting

    author TMAP , year 2022 . title TMAP FAQ and troubleshooting . https://lighthouse-sf.org/tmap/faq/

    2022

  29. [37]

    , author Mościcka, A

    author Wabiński, J. , author Mościcka, A. , author Touya, G. , year 2022 . title Guidelines for standardizing the design of tactile maps: A review of research and best practice . journal The Cartographic Journal volume 59 , pages 239--258 . https://doi.org/10.1080/00087041.202...

    2022

  30. [38]

    , author Watanabe, T

    author Watanabe, K. , author Watanabe, T. , author Yamaguchi, T. , author Akiyama, J. , author Minatani, K. , author Miyagi, M. , author Kita, S. , year 2012 . title Development of an embossed map automated creation system and evaluation of the legibility of the maps produced ...

    2012

  31. [39]

    , author Yamaguchi, T

    author Watanabe, T. , author Yamaguchi, T. , author Koda, S. , author Minatani, K. , year 2014 . title Tactile map automated creation system using OpenStreetMap , in: booktitle Computers Helping People with Special Needs , publisher Springer International Publishing , address ...

    2014

  32. [40]

    , author Barner, K

    author Way, T. , author Barner, K. , year 1997 . title Automatic visual to tactile translation. i. human factors, access methods and image manipulation . journal IEEE Transactions on Rehabilitation Engineering volume 5 , pages 81--94 . :10.1109/86.559353

    1997 doi

  33. [41]

    , author Cheng, H.C

    author Wijanto, E. , author Cheng, H.C. , author Liao, B.H. , author Huang, C.M. , author Chang, Y.T. , year 2024 . title Research on dispersion compensation of fd-oct system via pix2pix gan technique . journal IEEE Access volume 12 , pages 30976--30988 . :10.1109/ACCESS.2024.3368051

    2024

  34. [42]

    , author Zhang, Z

    author Yang, L. , author Zhang, Z. , author Song, Y. , author Hong, S. , author Xu, R. , author Zhao, Y. , author Zhang, W. , author Cui, B. , author Yang, M.H. , year 2023 . title Diffusion models: A comprehensive survey of methods and applications . journal ACM Comput. Surv....

    2023 doi

  35. [43]

    , author Fazli, P

    author Yuksel, B.F. , author Fazli, P. , author Mathur, U. , author Bisht, V. , author Kim, S.J. , author Lee, J.J. , author Jin, S.J. , author Siu, Y.T. , author Miele, J.A. , author Yoon, I. , year 2020 a. title Human-in-the-loop machine learning to increase video accessibil...

    2020

  36. [44]

    , author Kim, S.J

    author Yuksel, B.F. , author Kim, S.J. , author Jin, S.J. , author Lee, J.J. , author Fazli, P. , author Mathur, U. , author Bisht, V. , author Yoon, I. , author Siu, Y.T. , author Miele, J.A. , year 2020 b. title Increasing video accessibility for visually impaired users with...

    2020

  37. [45]

    , author Wilton, A.P

    author Zebehazy, K.T. , author Wilton, A.P. , year 2014 . title Straight from the source: Perceptions of students with visual impairments about graphic use . journal Journal of Visual Impairment & Blindness volume 108 , pages 275--286 . https://doi.org/10.1177/0145482X14108004...

    2014 doi

  38. [46]

    , author Rahman Siddiquee, M.M

    author Zhou, Z. , author Rahman Siddiquee, M.M. , author Tajbakhsh, N. , author Liang, J. , year 2018 . title Unet++: A nested u-net architecture for medical image segmentation , in: booktitle Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decisio...

    2018

  39. [47]

    , author Brinda, K

    author Červenka, P. , author Brinda, K. , author Hanousková, M. , author Hofman, P. , author Seifert, R. , year 2016 . title Blind friendly maps: Tactile maps for the blind as a part of the public map portal (mapy.cz) . journal CoRR volume abs/1603.09520 . http://arxiv.org/abs...

    2016 arXiv

  40. [48]

    , author Mulíčková, E

    author Štampach, R. , author Mulíčková, E. , year 2016 . title Automated generation of tactile maps . journal Journal of Maps volume 12 , pages 532--540 . https://doi.org/10.1080/17445647.2016.1196622, :10.1080/17445647.2016.1196622, http://arxiv.org/abs/https://doi.org/10.108...

    2016

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.