Integrated region-based segmentation using color components and texture features with prior shape knowledge

Emambakhsh, Mehryar; Ebrahimnezhad, Hossein; Sedaaghi, Mohammad Hossein

doi:10.2478/v10006-010-0054-y

Cited by 8 publications

(12 citation statements)

References 34 publications

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“…RGB color model is not a good choice for color image processing because it is highly correlated [27], for this reason in the training phase, a color transformation from the RGB color model into HSV and CIE L * a * b * color models are applied to the enhanced image. HSV and CIE L * a * b * are nonlinear color models and are less sensitive to variations of illumination intensity [4]; consequently a more detachable feature space is produced.…”

Section: Sevue Methodologymentioning

confidence: 99%

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

Hernández-Rabadán

Ramos-Quintana

Juk

2014

The Scientific World Journal

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This work presents a methodology that integrates a nonsupervised learning approach (self-organizing map (SOM)) and a supervised one (a Bayesian classifier) for segmenting diseased plants that grow in uncontrolled environments such as greenhouses, wherein the lack of control of illumination and presence of background bring about serious drawbacks. During the training phase two SOMs are used: one that creates color groups of images, which are classified into two groups using K-means and labeled as vegetation and nonvegetation by using rules, and a second SOM that corrects classification errors made by the first SOM. Two color histograms are generated from the two color classes and used to estimate the conditional probabilities of the Bayesian classifier. During the testing phase an input image is segmented by the Bayesian classifier and then it is converted into a binary image, wherein contours are extracted and analyzed to recover diseased areas that were incorrectly classified as nonvegetation. The experimental results using the proposed methodology showed better performance than two of the most used color index methods.

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Section: Sevue Methodologymentioning

confidence: 99%

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

Hernández-Rabadán

Ramos-Quintana

Juk

2014

The Scientific World Journal

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“…As a global optimisation method, SA is rela tively insensitive to initialisation and, in many applications, has been found to be faster than pattern search and genetic algo rithms [19,4]. SA is applied for a fixed number of iterations and then the evolution is continued from the lowest value of the energy function using a local optimisation method.…”

Section: X-and Y-axes Rotational Alignmentmentioning

confidence: 99%

Self-dependent 3D face rotational alignment using the nose region

Emambakhsh

Evans

2011

4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011)

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One of the challenging issues for 3D face recognition is face alignment. Many alignment algorithms are computationally expensive, making them unsuitable for real-time biometrics, or not robust enough to detect large variations in pose. In this work, a novel algorithm for 3D face rotational alignment is proposed, that uses the nose region. After preprocessing and nose region identification, alignment is performed by apply ing two energy functions to the nose footprint, identified as the largest filled region in the inverted depth map. These functions are minimised using Simulated Annealing and the Levenberg Marqurdt algorithm. The energy minimisation and segmenta tion procedures continue iteratively until a stopping criterion is met. The method has been applied to images from the Face Recognition Grand Challenge (FRGC) v2 dataset and the con sistency of its alignment has been verified using the iterative closest point (ICP) algorithm. As a self-dependent algorithm, it does not require a pre-aligned image as a reference and also has a high computational speed, approximately three times faster than the brute force ICP technique.

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“…() and ∇ are the divergence and gradient operators, respectively and g m (.) is a decreasing function [25] given by…”

Section: Denoising Algorithmsmentioning

confidence: 99%

Noise modelling for denoising and three‐dimensional face recognition algorithms performance evaluation

Emambakhsh¹,

Gao²,

Evans³

2015

IET Computer Vision

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This paper proposes an algorithm is proposed to quantitatively evaluate the performance of 3D holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (1) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (2) shows which parts of the face are more vulnerable to noise and require further post-processing after data acquisition; (3) compares the performance of three different categories of recognition algorithms: training-free matching-based, subspace projection-based and training-based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Also, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training-based (including the subspace projection) methods.

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Integrated region-based segmentation using color components and texture features with prior shape knowledge

Cited by 8 publications

References 34 publications

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

Self-dependent 3D face rotational alignment using the nose region

Noise modelling for denoising and three‐dimensional face recognition algorithms performance evaluation

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