Fast computation of inverse Meixner moments transform using Clenshaw’s formula

Multimedia Tools and Applications - Orthogonal moments are recognized as useful tools for object representation and image analysis. It was shown that they have better image representation...     

Fast computation of Charlier moments and its inverses using Clenshaw’s recurrence formula for image analysis

Multimedia Tools and Applications - In this paper, we propose a new fast way to compute both the image Charlier moments and its inverses using Clenshaw’s recurrence formula. Firstly, we...     

Fast computation of accurate Gaussian–Hermite moments for image processing applications

Gaussian–Hermite moments are orthogonal moments widely used in image processing and computer vision applications. Similar to the other families of orthogonal moments, highly computational demands represent the main challenging. In this work, an efficient method is proposed for fast computation of highly accurate Gaussian–Hermite moments for gray-level images. The proposed method achieves the accuracy through the integration of Gaussian–Hermite polynomials over the image pixels. To achieve the efficiency, the symmetry property of Gaussian–Hermite polynomials is employed where the computational complexity is reduced by 75%. Fast computational methodology is employed to significantly accelerate the computational process where the 2D Gaussian–Hermite moments are treated in a separated form. Numerical experiments are performed where the results are compared with the conventional method. The comparison of the obtained results clearly ensures the efficiency of the proposed method.     

New set of adapted Gegenbauer–Chebyshev invariant moments for image recognition and classification

Orthogonal moments and their invariants to geometric transformations for gray-scale images are widely used in many pattern recognition and image processing applications. In this paper, we propose a new set of orthogonal polynomials called adapted Gegenbauer–Chebyshev polynomials (AGC). This new set is used as a basic function to define the orthogonal adapted Gegenbauer–Chebyshev moments (AGCMs). The rotation, scaling, and translation invariant property of (AGCMs) is derived and analyzed. We provide a novel series of feature vectors of images based on the adapted Gegenbauer–Chebyshev orthogonal moments invariants (AGCMIs). We practice a novel image classification system using the proposed feature vectors and the fuzzy k-means classifier. A series of experiments is performed to validate this new set of orthogonal moments and compare its performance with the existing orthogonal moments as Legendre invariants moments, the Gegenbauer and Tchebichef invariant moments using three different image databases: the MPEG7-CE Shape database, the Columbia Object Image Library (COIL-20) database and the ORL-faces database. The obtained results ensure the superiority of the proposed AGCMs over all existing moments in representation and recognition of the images.

     

Fast computation of orthogonal Fourier�CMellin moments in polar coordinates

Fast, accurate and memory-efficient method is proposed for computing orthogonal Fourier–Mellin moments. Since the basis polynomials are continuous orthogonal polynomials defined in polar coordinates over a unit disk, the proposed method is applied to polar coordinates where the unit disk is divided into a number of non-overlapping circular rings that are divided into circular sectors of the same area. Each sector is represented by one point in its center. The implementation of this method completely removes both approximation and geometrical errors produced by the conventional methods. Based on the symmetry property, a fast and memory-efficient algorithm is proposed to accelerate the moment’s computations. A comparison to conventional methods is performed. Numerical experiments are performed to ensure the efficiency of the proposed method.     

Multiclass classification of Parkinson’s disease using cepstral analysis

This paper addressees the problem of an early diagnosis of PD (Parkinson’s disease) by the classification of characteristic features of person’s voice knowing that 90% of the people with PD suffer from speech disorders. We collected 375 voice samples from healthy and people suffer from PD. We extracted from each voice sample features using the MFCC and PLP Cepstral techniques. All the features are analyzed and selected by feature selection algorithms to classify the subjects in 4 classes according to UPDRS (unified Parkinson’s disease Rating Scale) score. The advantage of our approach is the resulting and the simplicity of the technique used, so it could also extended for other voice pathologies. We used as classifier the discriminant analysis for the results obtained in previous multiclass classification works. We obtained accuracy up to 87.6% for discrimination between PD patients in 3 different stages and healthy control using MFCC along with the LLBFS algorithm.     

Unsupervised classification of a forest’s image by texture model features

In the capacity of texture features, the parameters of the SAR model for unlabeled classification image of forest landscape are used. We compare the results for two systems of features: the SAR model and the popular Haralik statistics. The histogram clustering algorithm arrange the hierarchy of distributions better by cluster isolation.     

A novel algorithm for image representation using discrete spectrum of the Schrödinger operator

This paper extends the recent signal analysis method based on the spectral analysis of the semi-classical Schrödinger operator to two dimensions. An algorithm based on the tensor product approach when writing the eigenfunctions of the semi-classical Schrödinger operator is proposed. The algorithm is described and the effect of some parameters on the convergence of this method are numerically studied. The performance of the algorithm is illustrated through some examples.     

Rapid scalar value classification and volume clipping for?interactive 3D medical image visualization

In many clinical scenarios, medical data visualization and interaction are important to physicians for exploring inner anatomical structures and extracting meaningful diagnostic information. Real-time high-quality volume rendering, artifact-free clipping, and rapid scalar value classification are important techniques employed in this process. Unfortunately, in practice, it is still difficult to achieve an optimal balance. In this paper, we present some strategies to address this issue, which are based on the calculation of segment-based post color attenuation and dynamic ray–plane intersection (RPI) respectively. When implemented within our visualization system, the new classification algorithm can deliver real-time performance while avoiding the “color over-accumulation” artifacts suffered by the commonly used acceleration algorithms that employ pre-integrated classification. Our new strategy can achieve an optimized balance between image quality and classification speed. Next, the RPI algorithm is used with opacity adjustment technique to effectively remove the “striping” artifacts on the clipping plane caused by the nonuniform integration length. Furthermore, we present techniques for multiple transfer function (TF) based anatomical feature enhancement and “keyhole” based endoscopic inner structure view. Finally, the algorithms are evaluated subjectively by radiologists and quantitatively compared using image power spectrum analysis.     

Handwriting character classification using Freeman’s olfactory KIII model

KIII model is an olfactory model proposed by W. J. Freeman referring to a physiological structure of mammal??s olfactory system. The KIII model has been applied to kinds of pattern recognition systems, for example, electronic nose, tea classification, etc. However, the dynamics of neurons in the KIII model is given by Hodgkin-Huxley??s second-order differential equation and it consumes a very high computation cost. In this paper, we propose a simplified dynamics of chaotic neuron instead of the Hodgkin-Huxley dynamics at first, and secondly, we propose to use Fourier transformation with high resolution capability to extract features of time series behaviors of internal states of M1 nodes in KIII model instead of the conventional standard deviation method. Furthermore, paying attention to the point that human brain does visual processing as same as olfactory processing in the sense of information processing, a handwriting image recognition problem is treated as a new application field of KIII model. Through the computer simulation of the handwriting character classification, it is shown that the proposed method is useful by the comparison of experiment results with both computation time and recognition accuracy.     

Enhanced interpolation-based AMBTC image compression using Weber’s law

Multimedia Tools and Applications - In this paper, we propose a new enhanced absolute moment block truncation coding (AMBTC) image compression method based on interpolation. The proposed...     

Application of self organised Kohonen’s map for fundus vessels classification

The paper discusses a novel approach, based on classification of vessel features by self organized Kohonen’s map, to diagnostic of earliest studies of fundus disease. The results of an experimental research of features calculated on natural and synthetic images are presented. The comparison of the developed method with others methods is described. The article is published in the original.     

A new method of classification for Landsat data using the ‘watershed’ algorithm

Many methods of classifying satellite imagery have been suggested. Most of the accepted methods, either supervised or unsupervised, are founded on the assumption that the radiance values of a particular ground cover type can be characterized by some probability distribution having a central value(s) and a range parameter(s). This assumption is rarely, if ever, tenable and hence the methods must produce unnecessarily high rates of misclassification. Thus there is a need for an approach which avoids this type of assumption. Pattern recognition techniques, e.g. the ‘watershed’ algorithm, can be used to divide up the radiance-frequency domain, provided the radiance values can be reduced to 2 dimensions. These methods do not presuppose any ‘shape’ for the radiance-frequency distributions and therefore provide an unbiased aid to the interpretation of satellite imagery.     

Statistical methods for analysis of Parkinson’s disease gait pattern and classification

Multimedia Tools and Applications - Understanding the human gait and extracting intrinsic feature helps to classify walking patterns of Parkinson disease patients. The measurement of time series...     

Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm

Since approximately 90% of the people with PD (Parkinson’s disease) suffer from speech disorders including disorders of laryngeal, respiratory and articulatory function, using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. All previous works are done to distinguish healthy people from people with Parkinson’s disease (PWP). In this paper, we propose to go further by multiclass classification with three classes of Parkinson stages and healthy control. So we have used 40 features dataset, all the features are analyzed and 9 features are selected to classify PWP subjects in four classes, based on unified Parkinson’s disease Rating Scale (UPDRS). Various classifiers are used and their comparison is done to find out which one gives the best results. Results show that the subspace discriminant reach more than 93% overall classification accuracy.     

A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

To map Arctic lithology in central Victoria Island, Canada, the relative performance of advanced classifiers (Neural Network (NN), Support Vector Machine (SVM), and Random Forest (RF)) were compared to Maximum Likelihood Classifier (MLC) results using Landsat-7 and Landsat-8 imagery. A ten-repetition cross-validation classification approach was applied. Classification performance was evaluated visually and statistically using the global classification accuracy, producer’s and user’s accuracies for each individual lithological/spectral class, and cross-comparison agreement. The advanced classifiers outperformed MLC, especially when training data were not normally distributed. The Landsat-8 classification results were comparable to Landsat-7 using the advanced classifiers but differences were more pronounced when using MLC. Rescaling the Landsat-8 data from 16 bit to 8 bit substantially increased classification accuracy when MLC was applied but had little impact on results from the advanced classifiers.     

Efficient computation of 3D Morse–Smale complexes and persistent homology using discrete Morse theory

We propose an efficient algorithm that computes the Morse–Smale complex for 3D gray-scale images. This complex allows for an efficient computation of persistent homology since it is, in general, much smaller than the input data but still contains all necessary information. Our method improves a recently proposed algorithm to extract the Morse–Smale complex in terms of memory consumption and running time. It also allows for a parallel computation of the complex. The computational complexity of the Morse–Smale complex extraction solely depends on the topological complexity of the input data. The persistence is then computed using the Morse–Smale complex by applying an existing algorithm with a good practical running time. We demonstrate that our method allows for the computation of persistent homology for large data on commodity hardware.     

Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy

The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.