The urban fabric is a fundamental small-scale component of urban form. Its quantitative analysis has so far been limited either in its geographical extent or in the diversity of components analysed. Moreover, the planning approach has traditionally privileged an aerial perspective. A new approach integrating the pedestrian point of view is proposed. Spatial analysis procedures are implemented with a twofold objective: identifying urban fabrics and studying their spatial organization within a large metropolitan area. The former is achieved through multiple fabric assessment, a three-step protocol using a network-based partition of urban space: (i) a set of skeletal streetscape indicators is implemented on each spatial unit, considering different constituents of the urban fabric; (ii) spatial patterns on the street network are identified, applying geostatistical analysis to each indicator; and (iii) spatial patterns with Bayesian clustering are recombined, allowing the identification and characterization of urban fabric types and subspaces within the city. This methodology is tested on the French Riviera metropolitan area, where nine families of urban fabric are identified. Disentangling the spatial organization of urban fabrics represents the second objective of this paper: the geographical distribution of urban fabrics is investigated, applying mathematical morphology and variography while considering networkconstrained topological contiguities.

Currently, there are no quantitative studies of smooth pursuit, a behavior attributed to the fovea, in individuals with macular degeneration (MD). We hypothesize that pursuit in MD patients depends on the relative positions of the scotoma and target trajectory. We tested this hypothesis with a scanning laser ophthalmoscope (SLO), which allows for direct visualization of the target on the damaged retina. Monocular microperimetry and eye movements were assessed in eleven individuals with differing degrees of MD. Observers were asked to visually track a 1.7° target that moved in one of eight radial directions at 5°/s–6°/s. Consistent with our hypothesis, pursuit metrics depended on whether the target moved into or out of scotoma. Pursuit gains decreased with increasing scotoma extent in the target's heading direction (p = 0.017). Latencies were higher when the scotoma was present along the target trajectory (in either starting or heading directions, p < 0.001). Furthermore, an analysis of retinal position shows that targets fell on the fixational locus nearly 50% of the time. The results suggest that MD patients are capable of smooth pursuit eye movements, but are limited by target trajectory and scotoma characteristics.

Abstract. Indoor wayfinding is a major challenge for people with visual impairments, who are often unable to see visual cues such as informational signs, landmarks and structural features that people with normal vision rely on for wayfinding. We describe a novel indoor localization approach to facilitate wayfinding that uses a smartphone to combine computer vision and a dead reckoning technique known as visual-inertial odometry (VIO). The approach uses sign recognition to estimate the user's location on the map whenever a known sign is recognized, and VIO to track the user's movements when no sign is visible. The advantages of our approach are (a) that it runs on a standard smartphone and requires no new physical infrastructure, just a digital 2D map of the indoor environment that includes the locations of signs in it; and (b) it allows the user to walk freely without having to actively search for signs with the smartphone (which is challenging for people with severe visual impairments). We report a formative study with four blind users demonstrating the feasibility of the approach and suggesting areas for future improvement. State of the Art and Related TechnologyThe key to any wayfinding aid is localization -a means of estimating and tracking a person's location as they travel in an environment. The most widespread localization approach is GPS, which enables a variety of wayfinding tools such as Google Maps, Seeing Eye GPS 1 and BlindSquare, but it is only accurate outdoors. There are a range of indoor localization approaches, including Bluetooth beacons [1], Wi-Fi triangulation 2 , infrared light beacons [2] and RFIDs [3]. However, all of these approaches incur the cost of installing and maintaining physical infrastructure, or of updating the system as the existing infrastructure changes (e.g., whenever Wi-Fi access points change). Dead reckoning approaches such as step counting using inertial navigation [4] can estimate relative movements without any physical infrastructure, but this tracking estimate drifts over time unless it is augmented by absolute location estimates.

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Abstract. Nowadays the measurement of the nuchal translucency thickness is being used as part of routine ultrasound scanning during the end of the first trimester of pregnancy, for the screening of chromosomal defects, as trisomy 21. Currently, the measurement is being performed manually by physicians. The measurement can take a long time for being accomplished, needs to be performed by highly skilled operators, and is prone to errors. In this paper we present an algorithm that automatically detects the border of the nuchal translucency, once a region of interest has been manually identified. The algorithm is based on the minimisation of a cost function, and the optimisation is performed using the dynamic programming paradigm. The method we present overcomes several of the drawbacks present in the state of the art algorithms.