Object Matching Using Boundary Descriptors

Arandjelović, Ognjen

doi:10.5244/c.26.85

Cited by 32 publications

(35 citation statements)

References 11 publications

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“…The complete process of the proposed descriptor generation is shown in Figure 8. Compared to the descriptor in [39] (named OMBD (Object Matching Using Boundary Descriptors) for short), which is presented for smooth and untextured object matching, our proposed descriptor seems more suitable for remote sensing images with large background variations mainly due to the following aspects.…”

Section: Feature Descriptormentioning

confidence: 99%

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Shi

Jiang

2016

Remote Sensing

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Abstract:Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotation-and scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.

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Section: Feature Descriptormentioning

confidence: 99%

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Shi

Jiang

2016

Remote Sensing

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“…Most of these methods are local feature based, employing local feature descriptors such as SIFT [10] or SURF [11]. The reported performance of these methods has been rather disappointing and a major factor appears to be the loss of spatial, geometric relationship in the aforementioned representations [12,13]. In an effort to overcome this limitation, a number of approaches which divide a coin into segments have been described [14].…”

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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“…Similarly, a histogram over a vocabulary of elementary shapes, learnt by clustering local shape descriptors, is used to capture the object's shape. I adopt the standard SIFT descriptor as the basic building block of the texture representation and an analogous descriptor of local shape for the characterization of shape [19]. In both cases descriptors are matched using the Euclidean distance.…”

Section: Object Recognitionmentioning

confidence: 99%

Baseline Fusion for Image and Pattern Recognition—What Not to Do (and How to Do Better)

Arandjelović

2017

J. Imaging

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Abstract:The ever-increasing demand for a reliable inference capable of handling unpredictable challenges of practical application in the real world has made research on information fusion of major importance; indeed, this challenge is pervasive in a whole range of image understanding tasks. In the development of the most common type-score-level fusion algorithms-it is virtually universally desirable to have as a reference starting point a simple and universally sound baseline benchmark which newly developed approaches can be compared to. One of the most pervasively used methods is that of weighted linear fusion. It has cemented itself as the default off-the-shelf baseline owing to its simplicity of implementation, interpretability, and surprisingly competitive performance across a widest range of application domains and information source types. In this paper I argue that despite this track record, weighted linear fusion is not a good baseline on the grounds that there is an equally simple and interpretable alternative-namely quadratic mean-based fusion-which is theoretically more principled and which is more successful in practice. I argue the former from first principles and demonstrate the latter using a series of experiments on a diverse set of fusion problems: classification using synthetically generated data, computer vision-based object recognition, arrhythmia detection, and fatality prediction in motor vehicle accidents. On all of the aforementioned problems and in all instances, the proposed fusion approach exhibits superior performance over linear fusion, often increasing class separation by several orders of magnitude.

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Object Matching Using Boundary Descriptors

Cited by 32 publications

References 11 publications

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments

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

Baseline Fusion for Image and Pattern Recognition—What Not to Do (and How to Do Better)

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