57 research outputs found

    Human versus Machine Perception of Patterns OR A visual Turing Test: “Are you a human or a robot?”

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    Skeleton Filter:A Self-Symmetric Filter for Skeletonization in Noisy Text Images

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    Innerspec: Technical Report

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    In this report we describe “InnerSpec”, an approach for symmetric object detection that is based both on the com- putation of a symmetry measure for each pixel and on gra- dient information analysis. The symmetry value is obtained as the energy balance of the even-odd decomposition of an oriented square patch with respect to its central axis. Such an operation is akin to the computation of a row-wise con- volution in the midpoint. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered angle. These axes are finally evaluated by computing the image gradient in their neighborhood, in particular checking whether the gradient information displays specular characteristics

    Image symmetries: The right balance between evenness and perception

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    A recent and fascinating interest in computational symmetry for computer vision and computer graphics applications has led to a remarkable realization of new symmetry detection algorithms. Such a concern is culminated in a symmetry detection competition as a workshop affiliated with the 2011 and 2013 CVPR Conferences. In this paper, we propose a method based on the computation of the symmetry level associated to each pixel. Such a value is determined through the energy balance of the even/odd decomposition of a patch with respect to a central axis (which is equivalent to estimate the middle point of a row-wise convolution). Peaks localization along the perpendicular direction of each angle allows to identify possible symmetry axes. The evaluation of a feature based on gradient information allows to establish a classification confidence for each detected axis. By adopting the aforementioned rigorous validation framework, the proposed method indicates significant performance increase

    A normalized mirrored correlation measure for data symmetry detection

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    Symmetry detection algorithms are enjoying a renovated interest in the scientific community, fueled by recent advancements in computer vision and computer graphics applications. This paper is inspired by recent efforts in building a symmetric object detection system in natural images. In particular, it is first shown how correlation can be a core operator that allows finding local reflection symmetry points in 1-D sequences that are optimal in an energetic sense. Then, the importance of 2-D correlation in natural images to correctly align the symmetric object axis is demonstrated. Using the correlation as described is crucial in boosting the performance of the system, as proven by the results on a standard dataset

    On reflection symmetry in natural images

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    Many new symmetry detection algorithms have been recently developed, thanks to an interest revival on computational symmetry for computer graphics and computer vision applications. Notably, in 2013 the IEEE CVPR Conference organized a dedicated workshop and an accompanying symmetry detection competition. In this paper we propose an approach for symmetric object detection that is based both on the computation of a symmetry measure for each pixel and on saliency. The symmetry value is obtained as the energy balance of the even-odd decomposition of a patch w.r.t. each possible axis. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered axis orientation. These found candidate axes are finally evaluated through a confidence measure that also allow removing redundant detected symmetries. The obtained results within the framework adopted in the aforementioned competition show significant performance improvement

    Laboratori de captchas

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    C.A.P.T.C.H.A: Completely Automated Public Turing test to tell Computers and Humans Apart. Segons Cloudfare*, els humans tardem de mitjana 32 segons en resoldre cada captcha. Això suposa un equivalent de 500 anys destinats col.lectivament cada dia a resoldre aquests tests per evitar les intrusions de bots a les pàgines web. Si bé els objectius inicials d’aquests tests eren únicament separar humans de bots, altres objectius paral.lels han alterat el funcionament d’aquestes peces, com la digitalització de llibres o l’entrenament d’intel.lència artificial i altres interessos comercials. Com a resultat, tot i el volum immens de tests que es fan diàriament (200 milions), el disseny d’aquestes peçes interactives no han evolucionat tant com ho ha fet el de la resta de les interfícies i experiències d’usuari. Aquest projecte proposa prèmer el botó de reinici dels captcha i explorar les seves possibilitats per millorar-ne el funcionament. Utilitzant com a punt de partida les increïbles habilitats que diferencien els seus usuaris, els humans, dels bots. Posant especial atenció en la manera única amb la que perceben la realitat
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