A modified Hausdorff distance for object matching
09 Oct 1994-Vol. 1, pp 566-568
TL;DR: Based on experiments on synthetic images containing various levels of noise, the authors determined that one of these distance measures, called the modified Hausdorff distance (MHD) has the best performance for object matching.
Abstract: The purpose of object matching is to decide the similarity between two objects. This paper introduces 24 possible distance measures based on the Hausdorff distance between two point sets. These measures can be used to match two sets of edge points extracted from any two objects. Based on experiments on synthetic images containing various levels of noise, the authors determined that one of these distance measures, called the modified Hausdorff distance (MHD) has the best performance for object matching. The advantages of MHD ever other distances are also demonstrated on several edge snaps of objects extracted from real images.
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TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
14,054 citations
Cites methods from "A modified Hausdorff distance for o..."
...Recently, several researchers [Huttenlocher et al. 1993; Dubuisson and Jain 1994] have used the Hausdorff distance in a point set matching context....
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TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
4,543 citations
Cites methods from "A modified Hausdorff distance for o..."
...M. P. Dubuisson and A. K. Jain, ‘‘A modified Hausdorff distance f object matching,’’ICPR’94, 12th Intl. Conf....
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...Since the maximum distance is sensitive to outliers, we have measured the shape distortion via the average of the modified Hausdorff distances (MHD) Dubuisson [132] over all objects....
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TL;DR: A two-step process that allows both coarse detection and exact localization of faces is presented and an efficient implementation is described, making this approach suitable for real-time applications.
Abstract: The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works on grayscale still images. The Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image. The paper describes an efficient implementation, making this approach suitable for real-time applications. A two-step process that allows both coarse detection and exact localization of faces is presented. Experiments were performed on a large test set base and rated with a new validation measurement.
984 citations
Additional excerpts
...It is defined as hmod(A,B) = 1 |A| ∑ a∈A min b∈B ‖a− b‖ ....
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TL;DR: The technical aspect of automated driving is surveyed, with an overview of available datasets and tools for ADS development and many state-of-the-art algorithms implemented and compared on their own platform in a real-world driving setting.
Abstract: Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.
851 citations
Cites methods from "A modified Hausdorff distance for o..."
...Point cloud based methods may also use similarity metrics such as point density and Hausdorff distance [161], [183]....
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04 Nov 2009
TL;DR: The results show that the ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories and when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.
Abstract: Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.
817 citations
References
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TL;DR: Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented and it is shown that the method extends naturally to the problem of comparing a portion of a model against an image.
Abstract: The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image. >
4,194 citations
21 Jun 1994
TL;DR: An object matching system which is able to extract objects of interest from outdoor scenes and match them to obtain a reliable estimate of the average travel time in a road network is described.
Abstract: This paper describes an object matching system which is able to extract objects of interest from outdoor scenes and match them. Our application (in the domain of IVHS) involves measuring the average travel time in a road network. The extraction of the object of interest is performed by fusing multiple cues including motion, color, edges, and model information. Two objects extracted from images captured by two independent cameras at different times are then matched to evaluate their similarity. Color indexing based on histogram matching is used to avoid matching all possible pairs of objects. To resolve ambiguities, further matching is done by measuring the Hausdorff distance between two sets of edge points. The object matching system was given 2 sets of 40 vehicles. It was able to identify 23 of the 30 correct matches and all the false matches were rejected. Color indexing reduced the number of candidates for a match from 40 to 2. This matching accuracy is adequate to obtain a reliable estimate of the average travel time. >
10 citations