Robust Color Classification Using Fuzzy Rule-Based Particle Swarm Optimization

Kashani-Pour, Amir; Milani, Narges Shamshiri; Eghrary, Hadi Haji

doi:10.1109/cisp.2008.770

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…Many of the existing techniques employ fuzzifications on the colour classes to solve ambiguity issues. Kashanipour et al [15] in 2008 proposed a colour classification technique using fuzzy rule-sets operating in the HSI colour space. The rules were optimised with a particle swarm optimisation technique.…”

Section: Related Workmentioning

confidence: 99%

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

Shin

Reyes

2009

Memetic Comp.

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This paper presents a complete Fuzzy-Geneticbased self-calibrating illumination intensity-invariant colour classification system. Previously, we have developed a novel fuzzy colour processing technique called Fuzzy Colour Contrast Fusion (FCCF) that selectively and adaptively corrects colours depicting target colour objects. FCCF has been proven to compensate for the effects of spatially varying illumination intensities in the scene, in various colour spaces. However, FCCF requires a huge set of parameters that is extremely tedious to calibrate by hand. To address these problems, we present a system that combines FCCF with a Heuristic-Assisted Genetic Algorithm (HAGA). FCCF-HAGA fully automates the fine-tuning of all colour descriptors, with significantly improved colour classification accuracy. Furthermore, we have reduced FCCF's storage space requirements by processing colour channels selectively at varying colour depths. This is accomplished by combining a Variable Colour Depth (VCD) algorithm with FCCF that searches for the most effective colour depth for each colour channel. Our results show that for all cases, the FCCF-HAGA-VCD combination improves pie-slice colour classification. For six different target colours, under varying illuminations, the hybrid algorithm was able to yield 17.63% higher overall colour classification accuracy as compared to the pure fuzzy approach. Furthermore, it was able to reduce LUT storage space requirements by 78.06%, as compared to the full-colour depth LUT.

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Section: Related Workmentioning

confidence: 99%

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

Shin

Reyes

2009

Memetic Comp.

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“…Thus, semantic segmentation is the key step for registration. Over the past few decades, several kinds of image segmentation methods have been proposed, e.g., threshold-based segmentation [21]- [23], region growth segmentation [24]- [26], edge detection segmentation [27], [28], and specific-theory-based segmentation [29]- [31]. Recently, various CNN-based methods have been proposed.…”

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A Novel Region-Based Image Registration Method for Multisource Remote Sensing Images Via CNN

Zeng

Lin

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The comprehensive utilization of images from various satellite sensors can significantly increase the performance of remote sensing applications and has, therefore, attracted extensive research attention. One of the essential challenges that research encounters comes from multisource image registration. This article proposes a novel region-based image registration method for multisource images. The proposed method exploits the region features of input images, which provide more consistent and common information of the multisource data. The image region features are extracted based on image semantic segmentation using the deep convolutional neural network approach. The final registration result is a pixel-level output corresponding to the input images. The proposed registration scheme overcomes the limits of traditional feature extraction methods (e.g., point feature) adopted in previous registration schemes. Results indicate that the proposed method has good performance for the multisource remote sensing image registration and can serve as a building block for the fusion of multisource images.

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On the Robustness of Fuzzy-Genetic Colour Contrast Fusion with Variable Colour Depth

Shin

Husselmann

Reyes

2009

Neural Information Processing

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Robust Color Classification Using Fuzzy Rule-Based Particle Swarm Optimization

Cited by 10 publications

References 10 publications

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

A Novel Region-Based Image Registration Method for Multisource Remote Sensing Images Via CNN

On the Robustness of Fuzzy-Genetic Colour Contrast Fusion with Variable Colour Depth

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