1,314 research outputs found

    Semantizing Complex 3D Scenes using Constrained Attribute Grammars

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    International audienceWe propose a new approach to automatically semantize complex objects in a 3D scene. For this, we define an expressive formalism combining the power of both attribute grammars and constraint. It offers a practical conceptual interface, which is crucial to write large maintainable specifications. As recursion is inadequate to express large collections of items, we introduce maximal operators, that are essential to reduce the parsing search space. Given a grammar in this formalism and a 3D scene, we show how to automatically compute a shared parse forest of all interpretations -- in practice, only a few, thanks to relevant constraints. We evaluate this technique for building model semantization using CAD model examples as well as photogrammetric and simulated LiDAR data

    Digital Image Access & Retrieval

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    The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

    I2T: Image Parsing to Text Description

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    An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part B: Applications

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    Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. This report, Part B of a three part report on AI, presents overviews of the key application areas: Expert Systems, Computer Vision, Natural Language Processing, Speech Interfaces, and Problem Solving and Planning. The basic approaches to such systems, the state-of-the-art, existing systems and future trends and expectations are covered

    Probabilistic Scene Modeling for Situated Computer Vision

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    3D Reconstruction of Indoor Corridor Models Using Single Imagery and Video Sequences

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    In recent years, 3D indoor modeling has gained more attention due to its role in decision-making process of maintaining the status and managing the security of building indoor spaces. In this thesis, the problem of continuous indoor corridor space modeling has been tackled through two approaches. The first approach develops a modeling method based on middle-level perceptual organization. The second approach develops a visual Simultaneous Localisation and Mapping (SLAM) system with model-based loop closure. In the first approach, the image space was searched for a corridor layout that can be converted into a geometrically accurate 3D model. Manhattan rule assumption was adopted, and indoor corridor layout hypotheses were generated through a random rule-based intersection of image physical line segments and virtual rays of orthogonal vanishing points. Volumetric reasoning, correspondences to physical edges, orientation map and geometric context of an image are all considered for scoring layout hypotheses. This approach provides physically plausible solutions while facing objects or occlusions in a corridor scene. In the second approach, Layout SLAM is introduced. Layout SLAM performs camera localization while maps layout corners and normal point features in 3D space. Here, a new feature matching cost function was proposed considering both local and global context information. In addition, a rotation compensation variable makes Layout SLAM robust against cameras orientation errors accumulations. Moreover, layout model matching of keyframes insures accurate loop closures that prevent miss-association of newly visited landmarks to previously visited scene parts. The comparison of generated single image-based 3D models to ground truth models showed that average ratio differences in widths, heights and lengths were 1.8%, 3.7% and 19.2% respectively. Moreover, Layout SLAM performed with the maximum absolute trajectory error of 2.4m in position and 8.2 degree in orientation for approximately 318m path on RAWSEEDS data set. Loop closing was strongly performed for Layout SLAM and provided 3D indoor corridor layouts with less than 1.05m displacement errors in length and less than 20cm in width and height for approximately 315m path on York University data set. The proposed methods can successfully generate 3D indoor corridor models compared to their major counterpart

    Picture: A Probabilistic Programming Language for Scene Perception

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    Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision. Generative probabilistic models, or “analysis-by-synthesis” approaches, can capture rich scene structure but have been less widely applied than their discriminative counterparts, as they often require considerable problem-specific engineering in modeling and inference, and inference is typically seen as requiring slow, hypothesize-and-test Monte Carlo methods. Here we present Picture, a probabilistic programming language for scene understanding that allows researchers to express complex generative vision models, while automatically solving them using fast general-purpose inference machinery. Picture provides a stochastic scene language that can express generative models for arbitrary 2D/3D scenes, as well as a hierarchy of representation layers for comparing scene hypotheses with observed images by matching not simply pixels, but also more abstract features (e.g., contours, deep neural network activations). Inference can flexibly integrate advanced Monte Carlo strategies with fast bottom-up data-driven methods. Thus both representations and inference strategies can build directly on progress in discriminatively trained systems to make generative vision more robust and efficient. We use Picture to write programs for 3D face analysis, 3D human pose estimation, and 3D object reconstruction – each competitive with specially engineered baselines.Norman B. Leventhal FellowshipUnited States. Office of Naval Research (Award N000141310333)United States. Army Research Office. Multidisciplinary University Research Initiative (W911NF-13-1-2012)National Science Foundation (U.S.). Science and Technology Centers (Center for Brains, Minds and Machines. Award CCF-1231216
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