Object Recognition

Andrade‐Cetto, Juan; Villamizar, Michael

doi:10.1002/047134608x.w5512

Cited by 10 publications

(20 citation statements)

References 72 publications

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“…We adapt its definition from our own survey [7] to the scope of this study. Given one (or a sequence of) scene image of the environment for a particular robot pose (motion), can we identify and localize those landmarks that are sufficiently visible to the sensory system?…”

Section: Dynamic Environmentsmentioning

confidence: 99%

“…There exist a mirage of methods for edge detection on images, since it is a primary element of computer vision technology; from the very well known Sobel, Prewitt, or Roberts masks that use first-order derivative operations over images, or the LOG detector that uses second-order derivatives, to a family of optimally designed methods for edge extraction based on the detection of extremes in the output of the convolution of the image with an impulse response, namely the Canny, Deriche, and Spaceck detectors [7,35,36,74,137]. Another widely used operator, the Heitger edge detector, uses energy oriented maps, yielding good continuity of features near junctions and precise estimation of gradient orientation [94].…”

Section: Low-level Processing Of 2d Intensity Imagesmentioning

confidence: 99%

“…The tasks of edge extraction, labeling and segmentation are usually performed via morphological operations. In our survey on object recognition [7], we provide a detailed description on the methods to compute surface normals from sets of points in space via planar patch fitting; on recursive techniques for 3d surface segmentation; and on the characterization of surface segments according to their curvature properties thanks to the Extended Gaussian Image [99].…”

Section: Low-level Processing Of Laser Datamentioning

confidence: 99%

“…A description of some invariants for the recognition of planar landmarks from twodimensional images appears in our survey [7]. Various contributions from our research group to the study of geometric and projective invariants for three-dimensional object recognition from images are due to Sanfeliu et al [73,170,171,175].…”

Section: Landmark Characterizationmentioning

confidence: 99%

“…The typical data structure used for the representation of objects in the context of object recognition is the attribute graph [7]. In our particular domain, the nodes of such a graph can represent each of the labeled segments in an object, and the edges represent Euclidean distance or any other relationship between these segments.…”

Section: Bibliographical Notesmentioning

confidence: 99%

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Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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This thesis focuses on the various aspects of autonomous environment learning for indoor service robots. Particularly, on landmark extraction from sensor data, autonomous map building, and robot localization.To univocally identify landmarks from sensor data, we study several landmark representations, and the mathematical foundation necessary to extract the features that build them from images and laser range data. The features extracted from just one sensor may not suffice in the invariant characterization of landmarks and objects, pushing for the combination of information from multiple sources. We present a new algorithm that fuses complementary information from two low level vision modules into coherent object models that can be tracked and learned in a mobile robotics context. Illumination conditions and occlusions are the most prominent artifacts that hinder data association in computer vision. By using photogrammetric and geometric constraints we restrict the search for landmark matches in successive images, and by locking our interest in one or a set of landmarks in the scene, we track those landmarks along successive frames, reducing considerably the data association problem. We concentrate on those tools from the geometry of multiple views that are relevant to the computation of initial landmark location estimates for coarse motion recovery; a desirable characteristic when odometry is not available or is highly unreliable.Once landmarks are accurately extracted and identified, the second part of the problem is to use these observations for the localization of the robot, as well as the refinement of the landmark location estimates. We consider robot motion and sensor observations as stochastic processes, and treat the problem from an estimation theoretic point of view, dealing with noise by using probabilistic methods.The main drawback we encounter is that current estimation techniques have been devised for static environments, and that they lack robustness in more realistic situations. To aid in those situations in which landmark observations might not be consistent in time, we propose a new set of temporal landmark quality functions, and show how iii by incorporating these functions in the data association tests, the overall estimationtheoretic approach to map building and localization is improved. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association, together with the spatial compatibility tests already available.Special attention is paid in that the removal of spurious landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature; namely, asymptotic convergence and full correlation.The thesis also gives an in depth analysis of the fully correlated model to localization and map building from a control systems theory point of view. Considering the fact that the Kalman filter is nothing else but an optimal observer,...

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Section: Dynamic Environmentsmentioning

confidence: 99%

Section: Low-level Processing Of 2d Intensity Imagesmentioning

confidence: 99%

Section: Low-level Processing Of Laser Datamentioning

confidence: 99%

Section: Landmark Characterizationmentioning

confidence: 99%

Section: Bibliographical Notesmentioning

confidence: 99%

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Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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Visual Detection of Hexagonal Headed Bolts Using Method of Frames and Matching Pursuit

Mazzeo

Stella

Ancona

et al. 2005

Pattern Recognition and Image Analysis

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Abstract. In this paper we focus on the problem of automatically detecting the absence of the fastening bolts that secure the rails to the sleepers. The proposed visual inspection system uses images acquired from a digital line scan camera installed under a train. The general performances of the system, in terms of speed and detection rate, are mainly influenced by the adopted features for representing images and by their number. In this paper we use overcomplete dictionaries of waveforms, called frames, which allow dense and sparse representations of images and analyze the performances of the system with respect to the sparsity of the representation. Sparse means a representation with only few no vanishing components. In particular we show that, in the case of Gabor dictionaries, dense representations provide the highest detection rate. Moreover, the number of no vanishing components of 1% of the total reduces of 10% the detection rate of the system, indicating that very sparse representations do not heavily influence the performances. We show the adopted techniques by using images acquired in real experimental conditions.

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Fast Object Registration and Robotic Bin Picking

Böhnke¹,

Gottscheber²

2010

Communications in Computer and Information Science

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Object Recognition

Cited by 10 publications

References 72 publications

Environment Learning for Indoor Mobile Robots

Environment Learning for Indoor Mobile Robots

Visual Detection of Hexagonal Headed Bolts Using Method of Frames and Matching Pursuit

Fast Object Registration and Robotic Bin Picking

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