Showing papers by "Gary Bradski published in 2009"
01 Sep 2009
TL;DR: A novel 3D scene interpretation approach for robots in mobile manipulation scenarios using a set of 3D point features (Fast Point Feature Histograms) and probabilistic graphical methods (Conditional Random Fields) to obtain dense depth maps in the robot's manipulators working space.
Abstract: This paper proposes a novel 3D scene interpretation approach for robots in mobile manipulation scenarios using a set of 3D point features (Fast Point Feature Histograms) and probabilistic graphical methods (Conditional Random Fields). Our system uses real time stereo with textured light to obtain dense depth maps in the robot's manipulators working space. For the purposes of manipulation, we want to interpret the planar supporting surfaces of the scene, recognize and segment the object classes into their primitive parts in 6 degrees of freedom (6DOF) so that the robot knows what it is attempting to use and where it may be handled. The scene interpretation algorithm uses a two-layer classification scheme: i) we estimate Fast Point Feature Histograms (FPFH) as local 3D point features to segment the objects of interest into geometric primitives; and ii) we learn and categorize object classes using a novel Global Fast Point Feature Histogram (GFPFH) scheme which uses the previously estimated primitives at each point. To show the validity of our approach, we analyze the proposed system for the problem of recognizing the object class of 20 objects in 500 table settings scenarios. Our algorithm identifies the planar surfaces, decomposes the scene and objects into geometric primitives with 98.27% accuracy and uses the geometric primitives to identify the object's class with an accuracy of 96.69%.
115 citations
Proceedings Article•
07 Dec 2009TL;DR: This work implements a novel LDA-SIFT formulation which performs LDA prior to any vector quantization step, and discovers latent topics which are characteristic of particular transparent patches and quantize the SIFT space into transparent visual words according to the latent topic dimensions.
Abstract: Existing methods for visual recognition based on quantized local features can perform poorly when local features exist on transparent surfaces, such as glass or plastic objects. There are characteristic patterns to the local appearance of transparent objects, but they may not be well captured by distances to individual examples or by a local pattern codebook obtained by vector quantization. The appearance of a transparent patch is determined in part by the refraction of a background pattern through a transparent medium: the energy from the background usually dominates the patch appearance. We model transparent local patch appearance using an additive model of latent factors: background factors due to scene content, and factors which capture a local edge energy distribution characteristic of the refraction. We implement our method using a novel LDA-SIFT formulation which performs LDA prior to any vector quantization step; we discover latent topics which are characteristic of particular transparent patches and quantize the SIFT space into transparent visual words according to the latent topic dimensions. No knowledge of the background scene is required at test time; we show examples recognizing transparent glasses in a domestic environment.
69 citations
01 Dec 2009
TL;DR: A comprehensive perception system with applications to mobile manipulation and grasping for personal robotics, which makes use of dense 3D point cloud data acquired using stereo vision cameras by projecting textured light onto the scene.
Abstract: In this paper we present a comprehensive perception system with applications to mobile manipulation and grasping for personal robotics. Our approach makes use of dense 3D point cloud data acquired using stereo vision cameras by projecting textured light onto the scene. To create models suitable for grasping, we extract the supporting planes and model object clusters with different surface geometric primitives. The resultant decoupled primitive point clusters are then reconstructed as smooth triangular mesh surfaces, and their use is validated in grasping experiments using OpenRAVE [1]. To annotate the point cloud data with primitive geometric labels we make use of our previously proposed Fast Point Feature Histograms [2] and probabilistic graphical methods (Conditional Random Fields), and obtain a classification accuracy of 98.27% for different object geometries. We show the validity of our approach by analyzing the proposed system for the problem of building object models usable in grasping applications with the PR2 robot (see Figure 1).
53 citations
Book•
01 Jan 2009
TL;DR: In this paper, the authors propose a method to find the best translation of a given text in order to translate it to the target language. But they need to translate the text into the target domain.
Abstract: 概要 ＯｐｅｎＣＶ入門 ＯｐｅｎＣＶについて知る ＨｉｇｈＧＵＩ 画像処理 画像変換 ヒストグラムとマッチング 輪郭 画像の部分領域と分割処理 トラッキングとモーション カメラモデルとキャリブレーション 投影と３Ｄビジョン 機械学習 ＯｐｅｎＣＶの未来 ｉＰｈｏｎｅ ＯＳへのＯｐｅｎＣＶ／ＦａｃｅＤｅｔｅｃｔｉｏｎの移植と高速化 Ｗｅｂカメラを使って手や物体を感知するディスプレイを作ろう ＯｐｅｎＣＶインストールガイド
25 citations