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1. SaIL: saliency-driven injection of ARIA landmarks

   https://doi.org/10.1145/3377325.3377540  

   Aydin, Ali Selman; Feiz, Shirin; Ashok, Vikas; Ramakrishnan, IV (January 2020, 25th International Conference on Intelligent User Interfaces)

   Navigating webpages with screen readers is a challenge even with recent improvements in screen reader technologies and the increased adoption of web standards for accessibility, namely ARIA. ARIA landmarks, an important aspect of ARIA, lets screen reader users access different sections of the webpage quickly, by enabling them to skip over blocks of irrelevant or redundant content. However, these landmarks are sporadically and inconsistently used by web developers, and in many cases, even absent in numerous web pages. Therefore,we propose SaIL, a scalable approach that automatically detects the important sections of a web page, and then injects ARIA landmarks into the corresponding HTML markup to facilitate quick access to these sections. The central concept underlying SaIL is visual saliency, which is determined using a state-of-the-art deep learning model that was trained on gaze-tracking data collected from sighted users in the context of web browsing. We present the findings of a pilot study that demonstrated the potential of SaIL in reducing both the time and effort spent in navigating webpages with screen readers. 

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2. Simple Distances for Trajectories via Landmarks

   Jeff, Phillips; Pingfan, Tang (January 2019, Advances in geographic information science)
3. Analysis of shape data: From landmarks to elastic curves

   https://doi.org/10.1002/wics.1495  

   Bharath, Karthik; Kurtek, Sebastian (January 2020, WIREs Computational Statistics)

   Abstract Proliferation of high‐resolution imaging data in recent years has led to substantial improvements in the two popular approaches for analyzing shapes of data objects based on landmarks and/or continuous curves. We provide an expository account of elastic shape analysis of parametric planar curves representing shapes of two‐dimensional (2D) objects by discussing its differences, and its commonalities, to the landmark‐based approach. Particular attention is accorded to the role of reparameterization of a curve, which in addition to rotation, scaling and translation, represents an important shape‐preserving transformation of a curve. The transition to the curve‐based approach moves the mathematical setting of shape analysis from finite‐dimensional non‐Euclidean spaces to infinite‐dimensional ones. We discuss some of the challenges associated with the infinite‐dimensionality of the shape space, and illustrate the use of geometry‐based methods in the computation of intrinsic statistical summaries and in the definition of statistical models on a 2D imaging dataset consisting of mouse vertebrae. We conclude with an overview of the current state‐of‐the‐art in the field. This article is categorized under: Image and Spatial Data < Data: Types and StructureComputational Mathematics < Applications of Computational Statistics 

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4. A coding approach to localization using landmarks

   https://doi.org/10.1109/GLOBECOM42002.2020.9347982  

   Rebanal, Juan Carlo; Ezzeldin, Yahya H.; Fragouli, Christina; Tabuada, Paulo (December 2020, GLOBECOM)

   Fully autonomous vehicles need the ability to localize without external help, for instance by using visual sensors together with a pre-loaded map of landmarks. In this paper we connect self-localization using landmarks with coding theory. This connection enables to translate Hamming distance properties to probabilistic localization guarantees given a certain number of errors in landmark identification; it also enables to leverage existing polynomial time decoding algorithms for localization. We present promising numerical evaluation results by simulating vehicle traveling paths along a road network generated from real data of a region in Washington D.C. 

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5. Automatic Detection and Uncertainty Quantification of Landmarks on Elastic Curves

   https://doi.org/10.1080/01621459.2018.1527224  

   Strait, Justin; Chkrebtii, Oksana; Kurtek, Sebastian (December 2018, Journal of the American Statistical Association)