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Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.


Papers
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Proceedings ArticleDOI
23 Jun 2008
TL;DR: This work proposes rank-based distance metric learning for information retrieval by comparing the distances only among the relevant and irrelevant objects for a given query and applies the proposed framework to tattoo image retrieval in forensics and law enforcement application domain.
Abstract: We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements, has been studied widely, for example pairwise constraint-based distance metric learning. However, the capacity of existing algorithms is limited, because they usually assume that the distance between two similar objects is smaller than the distance between two dissimilar objects. This assumption may not hold, especially in the case of information retrieval when the input space is heterogeneous. To address this problem explicitly, we propose rank-based distance metric learning. Our approach overcomes the drawback of existing algorithms by comparing the distances only among the relevant and irrelevant objects for a given query. To avoid over-fitting, a regularizer based on the Burg matrix divergence is also introduced. We apply the proposed framework to tattoo image retrieval in forensics and law enforcement application domain. The goal of the application is to retrieve tattoo images from a gallery database that are visually similar to a tattoo found on a suspect or a victim. The experimental results show encouraging results in comparison to the standard approaches for distance metric learning.

93 citations

Proceedings ArticleDOI
10 Oct 2009
TL;DR: This paper presents a sensor-based online coverage path planning algorithm guaranteeing a complete coverage of unstructured planar environments by a mobile robot and develops an efficient path planner to link the simple spiral paths using the constrained inverse distance transform.
Abstract: This paper presents a sensor-based online coverage path planning algorithm guaranteeing a complete coverage of unstructured planar environments by a mobile robot. The proposed complete coverage algorithm abstracts the environment as a union of robot-sized cells and then uses a spiral filling rule. It can be largely classified as an approximate cellular decomposition approach as defined by Choset. In this paper, we first propose a special map coordinate assignment scheme based on active wall-finding using the history of sensor readings, which can drastically reduce the number of turns on the generated coverage path. Next, we develop an efficient path planner to link the simple spiral paths using the constrained inverse distance transform that we introduced the first time. This planner selects the next target cell which is at the minimal path length away from the current cell among the remaining non-contiguous uncovered cells while at the same time, finding the path to this target to save both the memory and time which are important concern in embedded robotics. Experiments on both simulated and real cleaning robots demonstrate the practical efficiency and robustness of the proposed algorithm.

93 citations

Proceedings ArticleDOI
Romer Rosales1, Glenn Fung1
20 Aug 2006
TL;DR: A method for constructing relative-distance preserving low-dimensional mapping (sparse mappings) that allows learning unknown distance functions (or approximating known functions) with the additional property of reducing distance computation time.
Abstract: Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficiency since distances are usually evaluated a large number of times, the classical example being query-by-example (find objects that are similar to a given query object). Moreover, the performance of these algorithms depends critically on choosing a good distance function. However, it is often the case that (1) the correct distance is unknown or chosen by hand, and (2) its calculation is computationally expensive (e.g., such as for large dimensional objects). In this paper, we propose a method for constructing relative-distance preserving low-dimensional mapping (sparse mappings). This method allows learning unknown distance functions (or approximating known functions) with the additional property of reducing distance computation time. We present an algorithm that given examples of proximity comparisons among triples of objects (object i is more like object j than object k), learns a distance function, in as few dimensions as possible, that preserves these distance relationships. The formulation is based on solving a linear programming optimization problem that finds an optimal mapping for the given dataset and distance relationships. Unlike other popular embedding algorithms, this method can easily generalize to new points, does not have local minima, and explicitly models computational efficiency by finding a mapping that is sparse, i.e. one that depends on a small subset of features or dimensions. Experimental evaluation shows that the proposed formulation compares favorably with a state-of-the art method in several publicly available datasets.

92 citations

Journal ArticleDOI
TL;DR: A new method called DBE (dark block extraction) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for visual assessment of cluster tendency (VAT) of a data set, using several common image and signal processing techniques.
Abstract: Clustering is a popular tool for exploratory data analysis. One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In this paper we investigate a new method called DBE (dark block extraction) for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for visual assessment of cluster tendency (VAT) of a data set, using several common image and signal processing techniques. Basic steps include: 1) generating a VAT image of an input dissimilarity matrix; 2) performing image segmentation on the VAT image to obtain a binary image, followed by directional morphological filtering; 3) applying a distance transform to the filtered binary image and projecting the pixel values onto the main diagonal axis of the image to form a projection signal; 4) smoothing the projection signal, computing its first-order derivative, and then detecting major peaks and valleys in the resulting signal to decide the number of clusters. Our new DBE method is nearly "automatic", depending on just one easy-to-set parameter. Several numerical and real-world examples are presented to illustrate the effectiveness of DBE.

92 citations

Journal ArticleDOI
TL;DR: In this article, a fast and exact Euclidean distance transformation using decomposed grayscale morphological operators is presented, which assigns each object pixel a value that corresponds to the shortest distance between the object pixel and the background pixels.
Abstract: A fast and exact Euclidean distance transformation using decomposed grayscale morphological operators is presented. Applied on a binary image, a distance transformation assigns each object pixel a value that corresponds to the shortest distance between the object pixel and the background pixels. It is shown that the large structuring element required for the Euclidean distance transformation can be easily decomposed into 3/spl times/3 windows. This is possible because the square of the Euclidean distance matrix changes uniformly both in the vertical and horizontal directions. A simple extension for a 3D Euclidean distance transformation is discussed. A fast distance transform for serial computers is also presented. Acting like thinning algorithms, the version for serial computers focuses operations only on the potential changing pixels and propagates from the boundary of objects, significantly reducing execution time. Nonsquare pixels can also be used in this algorithm. An example application, shape filtering using arbitrary sized circular dilation and erosion, is discussed. Rotation-invariant basic morphological operations can be done using this example application. >

91 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20235
202217
202161
202099
2019112
201881