The 2D shape structure dataset: A user annotated open access database

International audienceIn this paper we present the 2D Shape Structure database, a public, user-generated dataset of 2D shape decompositions into a hierarchy of shape parts with geometric relationships retained. It is the outcome of a large-scale user study obtained by crowdsourcing, involving over 1200 shapes in 70 shape classes, and 2861 participants. A total of 41953 annotations has been collected with at least 24 annotations per shape. For each shape, user decompositions into main shape, one or more levels of parts, and a level of details are available. This database reinforces a philosophy that understanding shape structure as a whole, rather than in the separated categories of parts decomposition, parts hierarchy, and analysis of relationships between parts, is crucial for full shape understanding. We provide initial statistical explorations of the data to determine representative (" mean ") shape annotations and to determine the number of modes in the annotations. The primary goal of the paper is to make this rich and complex database openly available (through the website http://2dshapesstructure.github.io/index.html), providing the shape community with a ground truth of human perception of holistic shape structure

Logic and phenomenology of incompleteness in illusory figures: new cases and hypotheses

Why is it relevant to analyze the role of incompleteness in illusory figure formation? Incompleteness probes the general problems of organization of the visual world and object segregation. The organization problem is one of the most important problems in visual neuroscience; namely: How and why are a very large numebr of unorganized elements of the retinal image combined, reduced, grouped and segregated to create visual objects? Within the problem of organizaiton, illusory figures are often considered to be one of the best examples to understand how and why the visual system segregates objects with a particular shape, color, and depth stratification. Understanding the role played by incompleteness in inducing illusory figures can thus be useful for understanding the principles of organization (the How) of perceptual forms and the more general logic of perception (the Why). To this purpose, incompletenss is here studied by analyzing its underlying organization principles and its inner logic

Neural Models of Seeing and Thinking

Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Coding shape inside the shape

The shape of an object lies at the interface between vision and cognition, yet the field of statistical shape analysis is far from developing a general mathematical model to represent shapes that would allow computational descriptions to express some simple tasks that are carried out robustly and e↵ortlessly by humans. In this thesis, novel perspectives on shape characterization are presented where the shape information is encoded inside the shape. The representation is free from the dimensions of the shape, hence the model is readily extendable to any shape embedding dimensions (i.e 2D, 3D, 4D). A very desirable property is that the representation possesses the possibility to fuse shape information with other types of information available inside the shape domain, an example would be reflectance information from an optical camera. Three novel fields are proposed within the scope of the thesis, namely ‘Scalable Fluctuating Distance Fields’, ‘Screened Poisson Hyperfields’, ‘Local Convexity Encoding Fields’, which are smooth fields that are obtained by encoding desired shape information. ‘Scalable Fluctuating Distance Fields’, that encode parts explicitly, is presented as an interactive tool for tumor protrusion segmentation and as an underlying representation for tumor follow-up analysis. Secondly, ‘Screened Poisson Hyper-Fields’, provide a rich characterization of the shape that encodes global, local, interior and boundary interactions. Low-dimensional embeddings of the hyper-fields are employed to address problems of shape partitioning, 2D shape classification and 3D non-rigid shape retrieval. Moreover, the embeddings are used to translate the shape matching problem into an image matching problem, utilizing existing arsenal of image matching tools that could not be utilized in shape matching before. Finally, the ‘Local Convexity Encoding Fields’ is formed by encoding information related to local symmetry and local convexity-concavity properties. The representation performance of the shape fields is presented both qualitatively and quantitatively. The descriptors obtained using the regional encoding perspective outperform existing state-of-the-art shape retrieval methods over public benchmark databases, which is highly motivating for further study of regional-volumetric shape representations