Computing a shape's moments from its boundary

Leu, Jia-Guu

doi:10.1016/0031-3203(91)90092-j

Cited by 90 publications

(30 citation statements)

References 4 publications

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“…Other approaches compute these moments exploiting the discrete structure of the representation and performing a layered decomposition and analysis of the object by means of the Green's theorem [8,13]. In the current scenario, it might be assumed that objects in the scene present a rough radial symmetry in the xy plane, thus simplifying the application the abovementioned divergence theorems and posing the computation of the centroid x t as the average of the positions of the surface voxels:…”

Section: D Position Estimation From a Random Setmentioning

confidence: 99%

Real-Time 3D multi-person tracking using Monte Carlo Surface Sampling

Canton-Ferrer

Casas

Pardàs

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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The current paper presents a low-complexity approach to the problem of simultaneous tracking of several people in low resolution sequences from multiple calibrated cameras. Redundancy among cameras is exploited to generate a discrete 3D colored representation of the scene. The proposed filtering technique estimates the centroid of a target using only a sparse set of points placed on its surface and making this set evolve along time based on the seminal particle filtering principle. In this case, the likelihood function is based on local neighborhoods computations thus drastically decreasing the computational load of the algorithm. In order to handle multiple interacting targets, a separate filter is assigned to each subject in the scenario while a blocking scheme is employed to model their interactions. Tests over a standard annotated dataset yield quantitative results showing the effectiveness of the proposed technique in both accuracy and real-time performance.

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Section: D Position Estimation From a Random Setmentioning

confidence: 99%

Real-Time 3D multi-person tracking using Monte Carlo Surface Sampling

Canton-Ferrer

Casas

Pardàs

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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“…This is achieved by computing two shape characteristics for each colour class: spreadness and elongation [13,22]. The first characteristic measures the compactness of the spatial distribution of a colour class.…”

Section: Article In Pressmentioning

confidence: 99%

Adaptive image replica detection based on support vector classifiers

Maret

Dufaux

Ebrahimi

2006

Signal Processing: Image Communication

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This paper presents a system for image replica detection. The idea behind the proposed approach is to adapt a system for detecting the replica of a specific reference image. The system is then able to classify test images as replicas of the reference image or as unrelated images. More precisely, the test procedure is as follows. A set of features is extracted from a test image, representing texture, colour and grey-level characteristics. These features are then feed into a preprocessing step, which is fine-tuned to the reference image. Finally, the resulting features are entered to a support vector classifier that determines if the test image is a replica of the reference image. Experimental results show the effectiveness of the proposed system. Target applications include search for copyright infringement (e.g. variations of copyrighted images) and known illicit content (e.g. paedophile images known to the police). r

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“…Meanwhile, Mukundan et al have proposed a fast algorithm that employs the contour integration method and square-to-circular method to compute Zernike moments [8][9][10][11]. With minor modifications, this technique can be utilized to compute pseudo-Zernike moments.…”

Section: Introductionmentioning

confidence: 99%