Quantitative evaluation of color image segmentation results

Borsotti, M.; Campadelli, Paola; Schettini, Raimondo

doi:10.1016/s0167-8655(98)00052-x

Cited by 306 publications

(167 citation statements)

References 17 publications

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“…To quantitatively evaluate our experimental results, we used the function proposed in [40], which improved on the one proposed in Ref. [41].…”

Section: Comparisons With Other Segmentation Methodsmentioning

confidence: 99%

A clustering fuzzy approach for image segmentation

CINQUE¹

2004

Pattern Recognition

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Segmentation is a fundamental step in image description or classiÿcation. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of ine ciency. This paper presents a clustering approach for image segmentation based on a modiÿed fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to ÿnd a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations conÿrm the validity of the approach proposed.

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“…To quantitatively evaluate our experimental results, we used the function proposed in [40], which improved on the one proposed in Ref. [41].…”

Section: Comparisons With Other Segmentation Methodsmentioning

confidence: 99%

A clustering fuzzy approach for image segmentation

CINQUE¹

2004

Pattern Recognition

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“…[16] and the Q function proposed in Ref. [17]. These methods can be regarded as goodness methods, but they do not require any user-set parameter for the evaluation of the performance of the segmentation.…”

Section: Selected Evaluation Measurementsmentioning

confidence: 99%

“…[16] and the Q function proposed in Ref. [17]. The first descriptor was introduced to evaluate the stability of a segmentation algorithm when minor shifts occur [15].…”

Section: Introductionmentioning

confidence: 99%

Pyramid segmentation algorithms revisited

Marfil

Molina-Tanco

Bandera

et al. 2006

Pattern Recognition

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The main goal of this work is to compare pyramidal structures proposed to solve segmentation tasks. Segmentation algorithms based on regular and irregular pyramids are described, together with the data structures and decimation procedures which encode and manage the information in the pyramid. In order to compare the different segmentation algorithms, we have employed three types of quality measurements: the shift variance measure, the F function and the Q function.

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“…However, for an automatic selection of the optimal parameter values, the quality of segmentation must be also automatically evaluated. In literature, there are several quality segmentation criteria: Lui and Borsotti [13], classification rates and other statistical measures [14].…”

Section: Introductionmentioning

confidence: 99%

Cell Microscopic Segmentation with Spiking Neuron Networks

Meftah¹,

Lézoray²,

Lecluse³

et al. 2010

Artificial Neural Networks – ICANN 2010

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Abstract. Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy for cell microscopic image segmentation. Results obtained confirm the validity of the approach. The strategy is performed on cytological color images. Quantitative measures are used to evaluate the resulting segmentations.

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Quantitative evaluation of color image segmentation results

Cited by 306 publications

References 17 publications

A clustering fuzzy approach for image segmentation

A clustering fuzzy approach for image segmentation

Pyramid segmentation algorithms revisited

Cell Microscopic Segmentation with Spiking Neuron Networks

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