Browsing by Subject "Surface materials"
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Item Open Access Effects of surface reflectance on local second order shape estimation in dynamic scenes(Elsevier Ltd, 2015) Dövencioğlu, D.N.; Wijntjes, M.W.A.; Ben-Shahar O.; Doerschner, K.In dynamic scenes, relative motion between the object, the observer, and/or the environment projects as dynamic visual information onto the retina (optic flow) that facilitates 3D shape perception. When the object is diffusely reflective, e.g. a matte painted surface, this optic flow is directly linked to object shape, a property found at the foundations of most traditional shape-from-motion (SfM) schemes. When the object is specular, the corresponding specular flow is related to shape curvature, a regime change that challenges the visual system to determine concurrently both the shape and the distortions of the (sometimes unknown) environment reflected from its surface. While human observers are able to judge the global 3D shape of most specular objects, shape-from-specular-flow (SFSF) is not veridical. In fact, recent studies have also shown systematic biases in the perceived motion of such objects. Here we focus on the perception of local shape from specular flow and compare it to that of matte-textured rotating objects. Observers judged local surface shape by adjusting a rotation and scale invariant shape index probe. Compared to shape judgments of static objects we find that object motion decreases intra-observer variability in local shape estimation. Moreover, object motion introduces systematic changes in perceived shape between matte-textured and specular conditions. Taken together, this study provides a new insight toward the contribution of motion and surface material to local shape perception. © 2015 The Authors.Item Open Access Human visual cortical responses to specular and matte motion flows(Frontiers Media S. A, 2015) Kam, T.-E.; Mannion, D.J.; Lee, S.-W.; Doerschner, K.; Kersten, D.J.Determining the compositional properties of surfaces in the environment is an important visual capacity. One such property is specular reflectance, which encompasses the range from matte to shiny surfaces. Visual estimation of specular reflectance can be informed by characteristic motion profiles; a surface with a specular reflectance that is difficult to determine while static can be confidently disambiguated when set in motion. Here, we used fMRI to trace the sensitivity of human visual cortex to such motion cues, both with and without photometric cues to specular reflectance. Participants viewed rotating blob-like objects that were rendered as images (photometric) or dots (kinematic) with either matte-consistent or shiny-consistent specular reflectance profiles. We were unable to identify any areas in low and mid-level human visual cortex that responded preferentially to surface specular reflectance from motion. However, univariate and multivariate analyses identified several visual areas; V1, V2, V3, V3A/B, and hMT+, capable of differentiating shiny from matte surface flows. These results indicate that the machinery for extracting kinematic cues is present in human visual cortex, but the areas involved in integrating such information with the photometric cues necessary for surface specular reflectance remain unclear. © 2015 Kam, Mannion, Lee, Doerschner and Kersten.Item Open Access Target classification with simple infrared sensors using artificial neural networks(IEEE, 2008-10) Aytaç, T.; Barshan, BillurThis study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE.