An equivalent definition of the histogram of forces: Theoretical and algorithmic implications

Ni, Jingbo; Matsakis, Pascal

doi:10.1016/j.patcog.2009.09.020

Cited by 15 publications

(12 citation statements)

References 20 publications

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“…Force histograms have proven to be quite powerful relative position descriptors. They have a known behavior with regards to affine transformations [15], and efficient algorithms have been designed for their computation [16]. Force histograms are used in several application domains such as linguistic descriptions [17], scene matching [18] or content-based image retrieval [19], [20].…”

Section: Related Workmentioning

confidence: 99%

Directional Enlacement Histograms for the Description of Complex Spatial Configurations between Objects

Clément

Poulenard

Kurtz

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

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The analysis of spatial relations between objects in digital images plays a crucial role in various application domains related to pattern recognition and computer vision. Classical models for the evaluation of such relations are usually sufficient for the handling of simple objects, but can lead to ambiguous results in more complex situations. In this article, we investigate the modeling of spatial configurations where the objects can be imbricated in each other. We formalize this notion with the term enlacement, from which we also derive the term interlacement, denoting a mutual enlacement of two objects. Our main contribution is the proposition of new relative position descriptors designed to capture the enlacement and interlacement between two-dimensional objects. These descriptors take the form of circular histograms allowing to characterize spatial configurations with directional granularity, and they highlight useful invariance properties for typical image understanding applications. We also show how these descriptors can be used to evaluate different complex spatial relations, such as the surrounding of objects. Experimental results obtained in the different application domains of medical imaging, document image analysis and remote sensing, confirm the genericity of this approach.

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Section: Related Workmentioning

confidence: 99%

Directional Enlacement Histograms for the Description of Complex Spatial Configurations between Objects

Clément

Poulenard

Kurtz

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Among the various relative position descriptors, the histograms of angles [11] and the histograms of forces [12] are widely used due to their ability to process pairwise information following a set of directions. They have a known behavior with regards to affine transformations [13], and efficient algorithms have been designed for their computation [14]. These descriptors are applied in differ-ent works, such as the linguistic description of spatial relations [15], scene matching [16] or image retrieval [17,18].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy directional enlacement landscapes for the evaluation of complex spatial relations

Clément

Kurtz

Wendling

2020

Pattern Recognition

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Structural spatial relations between image components are fundamental in the human perception of image similarity, and constitute a challenging topic in the domain of image analysis. By definition, some specific relations are ambiguous and difficult to formalize precisely by humans. In this work, we deal with the issue of evaluating complex spatial configurations, where objects can surround each other, potentially with multiple levels of depth. Based on a recently introduced spatial relation called enlacement, which generalizes the idea of surrounding for arbitrary objects, we propose a fuzzy landscape model that allows both to visualize and evaluate this relation directly in the image space, following different directions. Experiments on several characteristic examples highlight the interest and the behavior of this approach, allowing for rich interpretations of these complex spatial configurations.

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“…The former runs in O(KN√N) time, where K is the number of directions in which forces are considered and N is the number of pixels in the image; the latter runs in O(NlogN). Which algorithm performs better under which conditions is an issue discussed in [5]. A partition-based algorithm for the computation of force histograms in the case of 2-D vector data has also been developed.…”

Section: Introductionmentioning

confidence: 99%

Efficient computation of objects' spatial relations in digital images

Shabani

Matsakis

2012

2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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Both quantitative and qualitative measures of directional spatial relationships (e.g., left, right, above) between two raster objects in a digital image are important for high-level computer vision tasks such as scene analysis and robot navigation. The histogram of forces can provide such measures, but cannot be computed in real-time. A new approach for real-time computation, based on a vector representation of raster objects, is presented. The performance of the proposed approach is examined in an extensive experiment. Considering processing time and accuracy, optimal assessments of directional spatial relationships for use in real-time applications can be obtained.Index Terms-Directional spatial relationships, force histograms, Multiple Rectangle Representation (MRR) of an object, relative position linguistic descriptions

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An equivalent definition of the histogram of forces: Theoretical and algorithmic implications

Cited by 15 publications

References 20 publications

Directional Enlacement Histograms for the Description of Complex Spatial Configurations between Objects

Directional Enlacement Histograms for the Description of Complex Spatial Configurations between Objects

Fuzzy directional enlacement landscapes for the evaluation of complex spatial relations

Efficient computation of objects' spatial relations in digital images

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