An Evaluation of Open Source SURF Implementations

Gossow, David; Decker, Peter; Paulus, Dietrich

doi:10.1007/978-3-642-20217-9_15

Cited by 25 publications

(17 citation statements)

References 12 publications

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“…However, there is a number of open-source implementations of the SURF algorithm but they differ from the original one. An evaluation of some of the open-source implementations is available in [35]. Our implementation is based on the Open-SURF library [36] which was ported to Java programming language.…”

Section: Methodsmentioning

confidence: 99%

Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

Zukal¹,

Beneš²,

Číka³

et al. 2013

Measurement Science Review

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This paper focuses on the comparison of different interest point detectors and their utilization for measurements in ultrasound (US) images. Certain medical examinations are based on speckle tracking which strongly relies on features that can be reliably tracked frame to frame. Only significant features (interest points) resistant to noise and brightness changes within US images are suitable for accurate long-lasting tracking. We compare three interest point detectors -Harris-Laplace, Difference of Gaussian (DoG) and Fast Hessian -and identify the most suitable one for use in US images on the basis of an objective criterion. Repeatability rate is assumed to be an objective quality measure for comparison. We have measured repeatability in images corrupted by different types of noise (speckle noise, Gaussian noise) and for changes in brightness. The Harris-Laplace detector outperformed its competitors and seems to be a sound option when choosing a suitable interest point detector for US images. However, it has to be noted that Fast Hessian and DoG detectors achieved better results in terms of processing speed.

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Section: Methodsmentioning

confidence: 99%

Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

Zukal¹,

Beneš²,

Číka³

et al. 2013

Measurement Science Review

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“…Gossow et al [12] evaluated multiple implementations of the SURF algorithms. They tested two aspects that are common to all implementations.…”

Section: Related Workmentioning

confidence: 99%

Speeded-up robust features (SURF) as a benchmark for heterogeneous computers

Iparraguirre

Balmaceda

Mariani

2014

2014 IEEE Biennial Congress of Argentina (ARGENCON)

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Heterogeneous computers performance measurement is not a trivial task. Because of the implicit diversity of the architectures, it is hard to define a clear performance metric. We used SURF as a benchmark for heterogeneous computers. We measure results on multiple computers using OpenCL and CUDA implementations. Results show consistent measurements among platforms, implementations, and dataset size. Finally, we conclude that the relevant factor for this kind of application is the amount of GPU cores.Resumen-La medición de desempeño de computadoras heterogéneas no es una tarea trivial. Debido a la diversidad implícita de de las arquitecturas, es difícil definir una clara métrica de desempeño. Usamos SURF como banco de pruebas para computadoras heterogéneas. Medimos resultados en múltiples computadoras usando implementaciones en OpenCL y CUDA. Los resultados muestran mediciones consistentes entre plataformas, implementaciones y tamaño del conjunto de datos. Finalmente, concluimos que el factor relevante para este tipo de aplicación es la cantidad de procesadores en el GPU.

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“…This method describe the input image by selecting characteristic key-points. Let us give only a short description here, for more details please see [30], [31], [32], [33] or [34]. In this work we used SURF combining selection of key-points with calculating 64-element vector (descriptor).…”

Section: A Surf -Classic Attemptmentioning

confidence: 99%

Simplified firefly algorithm for 2D image key-points search

Napoli

Pappalardo

Tramontana

et al. 2014

2014 IEEE Symposium on Computational Intelligence for Human-Like Intelligence (CIHLI)

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Abstract-In order to identify an object, human eyes firstly search the field of view for points or areas which have particular properties. These properties are used to recognise an image or an object. Then this process could be taken as a model to develop computer algorithms for images identification. This paper proposes the idea of applying the simplified firefly algorithm to search for key-areas in 2D images. For a set of input test images the proposed version of firefly algorithm has been examined. Research results are presented and discussed to show the efficiency of this evolutionary computation method.

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An Evaluation of Open Source SURF Implementations

Cited by 25 publications

References 12 publications

Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

Speeded-up robust features (SURF) as a benchmark for heterogeneous computers

Simplified firefly algorithm for 2D image key-points search

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