Computing an Exact Gaussian Scale-Space

Rey-Otero, Ives; Delbracio, Mauricio

doi:10.5201/ipol.2016.117

Cited by 9 publications

(10 citation statements)

References 13 publications

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“…SIFT has two main parts: keypoint detection and feature description. Keypoints (or salient points) are detected on the Gaussian scale-space pyramid [21,22]. This kind of keypoint detection makes the algorithm scale (zoom in and out) invariant.…”

Section: Object Tracking With Template Matchingmentioning

confidence: 99%

A Real-Time Application of Singular Spectrum Analysis to Object Tracking with SIFT

Cayiroglu¹

2022

Eng. Technol. Appl. Sci. Res.

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This study combined SIFT and SSA to propose a novel algorithm for real-time object tracking. The proposed algorithm utilizes an intermediate fixed-size buffer and a modified SSA algorithm. Since the complete reconstruction step of the SSA algorithm was unnecessary, it was considerably simplified. In addition, the execution time of a Matlab implementation of the SSA algorithm was compared with a respective C++ implementation. Moreover, the performance of the two different matching algorithms in the detection, the FlannBasedMatcher and Brute-Force matcher algorithms of the OpenCV library, was compared.

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Section: Object Tracking With Template Matchingmentioning

confidence: 99%

A Real-Time Application of Singular Spectrum Analysis to Object Tracking with SIFT

Cayiroglu¹

2022

Eng. Technol. Appl. Sci. Res.

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“…In the case of digital images there is some ambiguity on how to define a discrete counterpart to the continuous Gaussian smoothing operator [9,22]. In the present work as in Lowe's original work, the digital Gaussian smoothing is implemented as a discrete convolution with samples of a truncated Gaussian kernel.…”

Section: Gaussian Blurringmentioning

confidence: 99%

“…For the range of values of σ considered in the described algorithm (i.e. σ ≥ 0.7), the digital Gaussian smoothing operator approximately satisfies a semi-group relation with an error below 10 −4 for pixel intensity values ranging from 0 to 1 [22]. Applying successively two digital Gaussian smoothings of parameters σ 1 and σ 2 is approximately equal to applying one digital Gaussian smoothing of parameter…”

mentioning

confidence: 99%

Anatomy of the SIFT Method

Otero

2014

Image Processing on Line

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This article presents a detailed description and implementation of the Scale Invariant Feature Transform (SIFT), a popular image matching algorithm. This work contributes to a detailed dissection of SIFT's complex chain of transformations and to a careful presentation of each of its design parameters. A companion online demonstration allows the reader to use SIFT and individually set each parameter to analyze its impact on the algorithm results. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article 1 .

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“…The Gaussian convolution has been computed using the Lindeberg's discrete scale-space method and its implementation described in [44], that is, we use that the Gaussian convolution v : t → G √ δt * u is the solution of the heat equation ∂v ∂t = ∆v for a diffusion time δt (set to 12 in our experiments, to guarantee the prescribed upper and lower bounds depending on the curvature of the visible shape [38]) so we only need to discretize partial derivatives. We refer to [44] for more details on the discretization. Parameter α needs to be close but less than 1 [47,14].…”

Section: Endmentioning

confidence: 99%

A Computational Model for Amodal Completion

Oliver

Haro

Dimiccoli³

et al. 2016

J Math Imaging Vis

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This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling

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Computing an Exact Gaussian Scale-Space

Cited by 9 publications

References 13 publications

A Real-Time Application of Singular Spectrum Analysis to Object Tracking with SIFT

A Real-Time Application of Singular Spectrum Analysis to Object Tracking with SIFT

Anatomy of the SIFT Method

A Computational Model for Amodal Completion

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