Comparing images using the Hausdorff distance under translation

Huttenlocher, Daniel P.; Rucklidge, W.J.; Klanderman, G.A.

doi:10.1109/cvpr.1992.223209

Cited by 76 publications

(52 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The particular variations were regular HD [8], MHD [11], and LTS-HD [12]. Such comparisons show the potential of MR HD reading digits under rotation compared to regular HD methods.…”

Section: Hd Mr Comparisonsmentioning

confidence: 99%

“…The work in [7,8] defined HD as a metric and states its advantages in the pattern recognition process as the speed of computation, natural allowance, and small image perturbation. HD is formally expressed as follows [8]. * Correspondence: erodriguez@uninorte.edu.co Given 2 finite point sets,…”

Section: Introductionmentioning

confidence: 99%

“…Partial HD [8] consists of modifying the directed distance calculation. The Kth distance is selected from the ordered sequence of the minimum distances from the elements of A to the elements of B. Censored HD [10] is based on the concept of partial HD.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HD_MR: a new algorithm for number recognition in electrical meters

Rodríguez¹,

Berdugo²,

Molinares³

et al. 2014

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Utility companies in developing countries employ analog electrical meters to determine consumption and bill their customers accordingly. Obtaining an accurate reading is an expensive and time-consuming process. High consumption levels of water, energy, or gas are fined by the government; thus, it is necessary to develop tools that allow users to be informed about their consumption in real time. This paper proposes a new number recognition algorithm named the Hausdorff distance for meter reading (HD MR). Experiments prove that HD MR can achieve a 99.9% recognition rate, even when recognized numbers are under rotation. The maximum recognition time is 31 ms; hence, the proposed method proves to be effective and capable in real time for the task proposed.

show abstract

“…The particular variations were regular HD [8], MHD [11], and LTS-HD [12]. Such comparisons show the potential of MR HD reading digits under rotation compared to regular HD methods.…”

Section: Hd Mr Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HD_MR: a new algorithm for number recognition in electrical meters

Rodríguez¹,

Berdugo²,

Molinares³

et al. 2014

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…This distance may be applied to determine the extent to which one image resembles another. Huttenlocher, Klanderman and Rucklidge [11] compared the Hausdorff distance with binary correlation on edge maps and conclude the former works better. They also provide algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a translated model of the image [12].…”

Section: Introductionmentioning

confidence: 99%

An Abstract Image Representation Based on Edge Pixel Neighborhood Information (EPNI)

Chalechale

Mertins

2002

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper we introduce a new abstract image representation based on Edge Pixel Neighborhood Information (EPNI). It is applied in image retrieval problem when user query is a fast drawn, rough example. The representation consists of two main elements. A neighborhood vector f and a vicinity table v. The former contains the frequencies of edge pixels with similar directions and the latter holds information about neighboring edge directions. An image similarity measure based on EPNI components is also designed and compared with some other measures known from the literature. Experimental results show a good recognition accuracy in a data set containing a wide range of color images.

show abstract

“…1 Without textual tags or other semantic descriptors, we can only approximate shape similarity from the 2D image. There have been a variety of proposed methods for determining similarity between planar shapes, including moment-based matching algorithms, 2,3 Hausdorff-distance based metrics, 4,5 and schemes for matching turning angle around the perimeter of a shape. 6 The goal of any of these algorithms is to approximate the perceptual judgments of the user.…”

Section: Introductionmentioning

confidence: 99%

<title>Retrieving images by 2D shape: a comparison of computation methods with human perceptual judgments</title>

Scassellati

Alexopoulos

Flickner

1994

Storage and Retrieval for Image and Video Databases II

View full text Add to dashboard Cite

In content based image retrieval, systems allow users to ask for objects similar in shape to a query object. However, there is no clear understanding of how computational shape similarity corresponds to human shape similarity. In this paper several shape similarity measures were evaluated on planar, connected, non-occluded binary shapes. Shape similarity using algebraic moments, spline curve distances, cumulative turning angle, sign of curvature and Hausdorff-distance were compared to human similarity judgments on twenty test shapes against a large image database.

show abstract

Comparing images using the Hausdorff distance under translation

Cited by 76 publications

References 2 publications

HD_MR: a new algorithm for number recognition in electrical meters

HD_MR: a new algorithm for number recognition in electrical meters

An Abstract Image Representation Based on Edge Pixel Neighborhood Information (EPNI)

<title>Retrieving images by 2D shape: a comparison of computation methods with human perceptual judgments</title>

Contact Info

Product

Resources

About