On the evaluation of cost functions for parameter optimization of a multiscale shape descriptor

Carneiro, Allan Cordeiro; Lopes, José Gerardo Fonteles; Neto, Jeova F. S. Rocha; Souza, Marcelo M. S.; Medeiros, Fátima N. S. de; Bezerra, Francisco Nivando

doi:10.1109/isspit.2017.8388317

Cited by 1 publication

(3 citation statements)

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“…Shape analysis and recognition tasks can benefit from algorithms that use multiscale curvature to represent the shapes of objects (Paula et al, 2013;Souza et al, 2016;Carneiro et al, 2017) or monoscale description (Ushizima et al, 2015). Shape representation can also be performed to handle corners in tasks such as scene analysis, polygonal…”

Section: Introductionmentioning

confidence: 99%

“…Souza et al (2016) designed a new approach to optimising the parameters of the normalised multiscale bending energy (NMBE) (Cesar Jr and Costa, 1997) in order to improve its shape discrimination ability. Carneiro et al (2017) investigated the role of different cost functions in the optimisation of NMBE by performing experiments on shapes from different public datasets.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we present the multiscale entropy of curvature (MEC), which is a descriptor that discriminates subtle details in the shape contour by evaluating the probability of occurrence of the feature based on curvature. Thus, this descriptor relies on the multiscale curvature in the same way as the ones proposed in (Souza et al, 2016;Carneiro et al, 2017Carneiro et al, , 2019.…”

Section: Introductionmentioning

confidence: 99%

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A multiscale shape descriptor based on diferential entropy

Pinheiro

Lopes

Carneiro

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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We propose a shape descriptor based on the differential entropy of multiscale curvature (MEC), which has a better shape discrimination ability than the normalised multiscale bending energy (NMBE) descriptor. We compare both NMBE and MEC with a monoscale descriptor by conducting multiple-class classification experiments on images from three public datasets: Kimia99 (99 shapes/9 classes), MPEG7-CE (1400 shapes/70 classes) and Flavia leaves (1907 shapes/32 classes). The classification results for precision, recall and F1-score measures, show that the use of MEC improved shape description for the MPEG7-CE and Flavia datasets. A qualitative analysis based on visualisation of pairwise Euclidean distance matrices also confirmed that MEC is a competitive method of shape description compared to NMBE for these datasets.

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