Classification of coins using an eigenspace approach

Huber, Reinhold; Ramoser, Herbert; Mayer, Konrad; Penz, Harald; Rubik, Michael

doi:10.1016/j.patrec.2004.09.006

Cited by 82 publications

(45 citation statements)

References 28 publications

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“…From the point of view of computer vision, two modern coins at the time of production are identical. This far more restricted problem setting allows for visual analysis to be conducted using holistic representations such as raw appearance [4] or edges [5], and off-the-shelf learning methods such as principal component analysis [4] or conventional neural networks [6]. However such approaches offer little promise in the context of ancient numismatics.…”

Section: Previous Workmentioning

confidence: 99%

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Fare

Arandjelović

2017

Lecture Notes in Computer Science

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Abstract. Ancient coins are historical artefacts of great significance which attract the interest of scholars, and a large and growing number of amateur collectors. Computer vision based analysis and retrieval of ancient coins holds much promise in this realm, and has been the subject of an increasing amount of research. The present work is in great part motivated by the lack of systematic evaluation of the existing methods in the context of coin grade which is one of the key challenges both to humans and automatic methods. We describe a series of methods -some being adopted from previous work and others as extensions thereof -and perform the first thorough analysis to date.

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Section: Previous Workmentioning

confidence: 99%

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Fare

Arandjelović

2017

Lecture Notes in Computer Science

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“…Huber et al . [41] applied a multistage approach to coin recognition. In this work, segmentation and rotational angle estimation were used to achieve translation and rotation invariance.…”

Section: Related Workmentioning

confidence: 99%

Robust and Effective Component-Based Banknote Recognition for the Blind

Hasanuzzaman

Yang

Tian

2012

IEEE Trans. Syst., Man, Cybern. C

103

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We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users.

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“…1) addresses the above impetus in a step-wise manner that allows teams of students to study these principles at increasing levels of complexity so as to match their level of learning and engagement [8], [9]. While coin sorting mechanisms abound, the sub-tasks involved present several challenges including vision [10] and control [11], especially for autonomous operations. More generally, the coin sorting task has subproblems that involve each of the aforementioned algorithmic areas -end-effector position placement, velocity control (Jacobeans), object recognition, obstacle avoidance, system identification, autonomous operation, and underactuated (saturated) controls.…”

Section: Introductionmentioning

confidence: 99%

CHARM: A platform for algorithmic robotics education & research

Singh¹,

Kurniawati²,

Naveh³

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper introduces autonomous sorting of moving coins via a robot as a multi-level task that supports the principled study of robotics fundamentals including kinematics, dynamics, perception, motion planning, controls, and optimization based around a widely obtainable, standardized, low-cost object (a coin). The paper also presents a demonstrated solution to this in the form of the CHARM (Coin Handling Arm for Robotics Mastery) robot, which addresses the autonomous coin sorting problem using an economical kit made from commodity computing hardware and three Dynamixel servomotors. From a learning perspective, this problem facilitates interdisciplinary practice across subject and grade levels with an algorithmic foundation that is central to modern robotics. Evidence supporting this approach is illustrated from case studies of student projects and, in particular, the CHARM robot. Beyond practice alone, by presenting a challenging (but manageable) research problem, we found that the coin sorting task teaches robotics in a principled way. Further, algorithmic complexity tiers the problem to academic levels. While motivated by robotics education, the (optimal) coin sorting problem may also be seen as an archetype problem for manipulation/motion-planning research. Thus, this also promotes a research foundation supporting later research opportunities.

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Classification of coins using an eigenspace approach

Cited by 82 publications

References 28 publications

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Robust and Effective Component-Based Banknote Recognition for the Blind

CHARM: A platform for algorithmic robotics education & research

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Classification of coins using an eigenspace approach

Cited by 82 publications

References 28 publications

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Robust and Effective Component-Based Banknote Recognition for the Blind

CHARM: A platform for algorithmic robotics education &amp; research

Contact Info

Product

Resources

About

CHARM: A platform for algorithmic robotics education & research