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Proceedings ArticleDOI

A Microscopic Framework For Distributed Object-Recognition & Pose-Estimation

TL;DR: A novel, vision-based microscopic framework for active and distributed object-recognition and pose-estimation using a team of robots of simple construction and initial simulation results corroborate the system design and field coverage methods.
Abstract: Effective self-organization schemes lead to the creation of autonomous and reliable robot teams that can outperform a single, sophisticated robot on several tasks. We present here a novel, vision-based microscopic framework for active and distributed object-recognition and pose-estimation using a team of robots of simple construction. The team performs the task of locating a given object(s) in an unknown territory, recognizing it with sufficient confidence and estimating its pose. The larger goal is to experiment with probabilistic frameworks and graph-theoretic methods in the design of robot teams to achieve autonomous self-organization independent of the task at hand. We have chosen 3D object recognition as a first problem area to evaluate the effectiveness of our system design. The system comprises a probabilistic framework for the successful detection of the object in a coordinated manner and adaptive measures in case of machinery failures or presence of obstacles. A pose estimation method for the detected object and graph theoretic solutions for optimal field coverage by the robots are also presented. Each robot is provided with a part-based, spatial model of the object. The object to be recognized is taken to be much bigger than the robots and need not fit completely into the field of view of the robot cameras. We assume no knowledge of the internal parameters of the robot cameras and perform no camera calibration procedures. Initial simulation results corroborate our system design and field coverage methods.
Citations
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Journal ArticleDOI
TL;DR: A systematic literature review concerning 3D object recognition and classification published between 2006 and 2016 is presented, using the methodology for systematic review proposed by Kitchenham.
Abstract: In this paper, we present a systematic literature review concerning 3D object recognition and classification. We cover articles published between 2006 and 2016 available in three scientific databases (ScienceDirect, IEEE Xplore and ACM), using the methodology for systematic review proposed by Kitchenham. Based on this methodology, we used tags and exclusion criteria to select papers about the topic under study. After the works selection, we applied a categorization process aiming to group similar object representation types, analyzing the steps applied for object recognition, the tests and evaluation performed and the databases used. Lastly, we compressed all the obtained information in a general overview and presented future prospects for the area.

36 citations


Cites background or methods from "A Microscopic Framework For Distrib..."

  • ...Some works ignore the noise concept in their study, just by using synthetic data and simulations [11, 172]....

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  • ...Other works performed simulations based on the proposed method, for example the work proposed by Anand [11], or employed only synthetic data for the experiments, for example the work proposed by Kordelas [144]....

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  • ...These are the cases of the analyzed works presented by Anand, Raytchev e Kent. Anand shows an active vision-based system for 3D object recognition and pose estimation [11] which employs an autonomous robot team, data fusion of multiple sensors and a self-organization mechanism to complete the task....

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  • ...Anand shows an active vision-based system for 3D object recognition and pose estimation [11] which employs an autonomous robot team, data fusion of multiple sensors and a self-organization mechanism to complete the task....

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References
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Proceedings ArticleDOI
01 Dec 2001
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

18,620 citations

Proceedings ArticleDOI
Rainer Lienhart1, J. Maydt1
10 Dec 2002
TL;DR: This paper introduces a novel set of rotated Haar-like features that significantly enrich the simple features of Viola et al. scheme based on a boosted cascade of simple feature classifiers.
Abstract: Recently Viola et al. [2001] have introduced a rapid object detection. scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated Haar-like features. These novel features significantly enrich the simple features of Viola et al. and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%.

3,133 citations

Proceedings Article
31 Dec 1993
TL;DR: Results from constrained optimization some results from algebraic geometry differential geometry are shown.
Abstract: Projective geometry modelling and calibrating cameras edge detection representing geometric primitives and their uncertainty stereo vision determining discrete motion from points and lines tracking tokens over time motion fields of curves interpolating and approximating three-dimensional data recognizing and locating objects and places answers to problems. Appendices: constrained optimization some results from algebraic geometry differential geometry.

2,744 citations

Journal ArticleDOI

1,102 citations


Additional excerpts

  • ...We assume no knowledge of the internal parameters of the robot cameras and perform no camera calibration procedures, and also do not require that the entire object fit into one single camera view....

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Journal ArticleDOI
TL;DR: A strategy for acquiring 3-D data of an unknown scene, using range images obtained by a light stripe range finder is addressed, where the foci of attention are occluded regions and the system can resolve the appearance of occlusions by analyzing them.
Abstract: A strategy for acquiring 3-D data of an unknown scene, using range images obtained by a light stripe range finder is addressed. The foci of attention are occluded regions, i.e., only the scene at the borders of the occlusions is modeled to compute the next move. Since the system has knowledge of the sensor geometry, it can resolve the appearance of occlusions by analyzing them. The problem of 3-D data acquisition is divided into two subproblems due to two types of occlusions. An occlusion arises either when the reflected laser light does not reach the camera or when the directed laser light does not reach the scene surface. After taking the range image of a scene, the regions of no data due to the first kind of occlusion are extracted. The missing data are acquired by rotating the sensor system in the scanning plane, which is defined by the first scan. After a complete image of the surface illuminated from the first scanning plane has been built, the regions of missing data due to the second kind of occlusions are located. Then, the directions of the next scanning planes for further 3-D data acquisition are computed. >

339 citations


"A Microscopic Framework For Distrib..." refers background in this paper

  • ...The object to be recognized is taken to be much bigger than the robots and need not fit completely into the field of view of the robot cameras....

    [...]

  • ...We present here, an active vision-based 3D object recognition and pose estimation system that employs an autonomous team of robots, to obtain multiple views of the object, fusion of data from multiple sensors and uses a sensor selforganization mechanism to complete its tasks....

    [...]