Robust Ellipse-Specific Fitting for Real-Time Machine Vision

Maini, Eliseo Stefano

doi:10.1007/11565123_31

Cited by 16 publications

(21 citation statements)

References 8 publications

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“…A new interesting LS technique, the Enhanced Least-Square Fitting of Ellipses (EDFE), has been developed recently by our work team by Maini et Al, and it was proposed in [15], [8]. This is a LS procedure that improves the work described in [7].…”

Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

“…Specifically, if the data points lie exactly on the ellipse the eigenvalue corresponding to the optimal solution is zero thus the original Fitzgibbon's algorithm [7] does not lead to any solution. Moreover, this happens even if the data points are close to the ideal ellipse therefore B2AC performs poorly both when noise is absent and low [15]. The problem has been solved by slightly perturbing the original data by adding a known Gaussian noise.…”

Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

“…For a more precise analysis of the method one can refer to [15], and [8]. However, the author describes his technique only as a mathematical procedure, without inserting it within an actual robotics context.…”

Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

“…However, the author describes his technique only as a mathematical procedure, without inserting it within an actual robotics context. In fact, the authors tested its characteristics only in M atlab simulation [15], [8].…”

Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

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Real-Time Least-Square Fitting of Ellipses Applied to the RobotCub Platform

Greggio

Manfredi

Laschi

et al. 2008

Simulation, Modeling, and Programming for Autonomous Robots

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Abstract. This paper presents the first implementation of a new algorithm for pattern recognition in machine vision developed in our laboratory. This algorithm has been previously presented only theoretically, without practical use. In this work we applied it to the RobotCub humanoid robotics platform simulator. We used it as a base for a circular object localization within the 3D surrounding space. The algorithm is a robust and direct method for the least-square fitting of ellipses to scattered data. RobotCub is an open source platform, born to study the development of neuro-scientific and cognitive skills in human beings, especially in children. Visual pattern recognition is a basic capability of many species in nature. The skill of visually recognizing and distinguishing different objects in the surrounding environment gives rise to the development of sensory-motor maps in the brain, with the consequent capability of object manipulation. In this work we present an improvement of the RobotCub project in terms of machine vision software, by implementing the method of the least-square fitting of ellipses of Maini (EDFE), previous developed in our laboratory, in a robotics context. Moreover, we compared its performance with the Hough Tranform, and others least-square ellipse fittings techniques. We used our system to detect spherical objects by applying it to the simulated RobotCub platform. We performed several tests to prove the robustness of the algorithm within the overall system. Finally we present our results.

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Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

Section: Least Square Of Ellipses: the State Of The Artmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Least-Square Fitting of Ellipses Applied to the RobotCub Platform

Greggio

Manfredi

Laschi

et al. 2008

Simulation, Modeling, and Programming for Autonomous Robots

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“…15 Considerable work has been done in studying the usefulness of conic images in image reconstruction, both in segmenting conic curves in images and in determining pose based on successful segmentations. [16][17][18][19][20][21][22][23][24] It has been demonstrated that images of conics can be used in pose determination. In particular, it is possible to determine, to a single rotational degree of freedom, the pose from which an image was generated based on a single image of a circle within the image frame.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the PPBE Process in the Current Dynamic Political Environment

Holcombe¹,

Johnston²

2008

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE 01-11-20072. REPORT TYPE Annual Summary DATES COVERED PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERJohns Hopkins University Baltimore, MD 21218-2686 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland 21702-5012 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for Public Release; Distribution Unlimited SUPPLEMENTARY NOTESOriginal contains colored plates: ALL DTIC reproductions will be in black and white. ABSTRACTFavorable outcome in prostate brachytherapy critically depends on the accurate placement of radioactive sources in their planned locations. Unfortunately, there is variety of mechanical factors that cause the seeds to divert from their planned locations. While this problem has been known to brachytherapists, current technology does not allow for reliable localization of the implanted sources, thereby prohibiting the prediction and modification of seed distribution intra-operatively. The Research Objective of the proposal is to develop and evaluate ex-vivo a method for intra-operative localization of the implanted seeds in relation to the prostate, to facilitate in-situ dosimetric optimization and exit dosimetry. In particular, we will: [1] Registration of Ultrasound to Fluoroscopy (RUF): Develop methods for reconstruction of seed implants from X-ray fluoroscopy and spatially register them to the prostate anatomy identified in TRUS [2] System Integration: Integrate the above methods in a software package and link it with the FDA-approved CMS Interplant® prostate brachytherapy system to enable in-situ dosimetry calculation [3] Experimental Validation: Evaluate the performance of the RUF system on phantoms and pre-recorded patient data. SUBJECT TERMSProstate Brachytherapy, X-ray reconstruction, C-arm, TRUS Progress Report Summary Project Year 2 (2006 -2007)A INTRODUCTION For several decades, the definitive treatment for low and medium risk prostate cancer was radical prostatectomy or external beam radiation ther...

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Advanced Signal Processing and Modeling for ANS Data

Valenza

Scilingo

2014

Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition

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Robust Ellipse-Specific Fitting for Real-Time Machine Vision

Cited by 16 publications

References 8 publications

Real-Time Least-Square Fitting of Ellipses Applied to the RobotCub Platform

Real-Time Least-Square Fitting of Ellipses Applied to the RobotCub Platform

Analysis of the PPBE Process in the Current Dynamic Political Environment

Advanced Signal Processing and Modeling for ANS Data

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