Ball detection in soccer images using isophote’s curvature and discriminative features

Ball recognition in soccer matches is a critical issue for automatic soccer video analysis. Unfortunately, because of the difficulty in solving the problem, many efforts of numerous researchers have still not produced fully satisfactory results in terms of accuracy. This paper proposes a ball recognition approach that introduces a double level of innovation. Firstly, a randomized circle detection approach based on the local curvature information of the isophotes is used to identify the edge pixels belonging to the ball boundaries. Then, ball candidates are validated by a learning framework formulated into a three-layered model based on a variation of the conventional local binary pattern approach. Experimental results were obtained on a significant set of real soccer images, acquired under challenging lighting conditions during Italian “Serie A” matches. The results have been also favorably compared with the leading state-of-the-art methods.     

Evaluating Algeria’s social and economic development using a series of night-time light images between 1992 to 2012

ABSTRACT

After social and economic problems, Algeria regained its night-time light (NL) brightness as this analysis of space-borne images shows. We evaluate the advancement of artificial light sources and the socioeconomic development between 1992 to 2012 for all 48 provinces. Image calibration improved the consistency and comparability of the DMSP-OLS data. Light detected from gas flaring has been separated to focus on light from human settlements. We then estimated the relationship between night-time light and selected socioeconomic parameters (SEP). Night-time light growth (NLG) and night-time light annual growth (NLAG) indices are calculated at the national, regional and provincial level. Considering the Oil price crash and the Algerian Civil Concord, we take a detailed look at these historic events in Algeria. Our main findings are: (A) The total night-time light (TNL) for human settlements grew nationwide by 128%, (B) night-time light data correlates with socioeconomic parameters such as population, and electric power consumption, (C) the oil price crash resulted in slower night-time light growth, while the Algerian Civil Concord and the end of the crises led to increased TNL, (D) because of government investment, the High Plateaus are the fastest growing region since the end of the crises, (E) space-borne night-time light data can consistently fill the information gap when data from official sources are not available. Government and policy-making bodies may benefit from the presented findings allowing them to oversee and address provincial inequalities and economic development.     

A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals

Higher-order spectra (HOS) is an efficient feature extraction method used in various biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities, and affective computing. The motive of this work was to explore the application of HOS for an automated diagnosis of Parkinson’s disease (PD) using electroencephalography (EEG) signals. Resting-state EEG signals collected from 20 PD patients with medication and 20 age-matched normal subjects were used in this study. HOS bispectrum features were extracted from the EEG signals. The obtained features were ranked using t value, and highly ranked features were used in order to develop the PD Diagnosis Index (PDDI). The PDDI is a single value, which can discriminate the two classes. Also, the ranked features were fed one by one to the various classifiers, namely decision tree (DT), fuzzy K-nearest neighbor (FKNN), K-nearest neighbor (KNN), naive bayes (NB), probabilistic neural network (PNN), and support vector machine (SVM), to choose the best classifier using minimum number of features. We have obtained an optimum mean classification accuracy of 99.62%, mean sensitivity and specificity of 100.00 and 99.25%, respectively, using the SVM classifier. The proposed PDDI can aid the clinicians in their diagnosis and help to test the efficacy of drugs.

     

Fault detection in electrical equipment’s images by using optimal features with deep learning classifier

Multimedia Tools and Applications - Infrared imaging frameworks have been broadly utilized as a part of the military and civil fields, for example, target recognition, fault diagnosis, fire...     

An expert system for predicting Manning’s roughness coefficient in open channels by using gene expression programming

Manning’s roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Accurate estimation of Manning’s roughness coefficient is essential for the computation of flow rate, velocity. Conventional formulae that are greatly based on empirical methods lack in providing high accuracy for the prediction of Manning’s roughness coefficient. Consequently, new and accurate techniques are still highly demanded. In this study, gene expression programming (GEP) is used to estimate the Manning’s roughness coefficient. The estimated value of the roughness coefficient is used in Manning’s equation to compute the flow parameters in open-channel flows in order to carry out a comparison between the proposed GEP-based approach and the conventional ones. Results show that computed discharge using estimated value of roughness coefficient by GEP is in good agreement (±10%) with the experimental results compared to the conventional formulae (R ² = 0.97 and RMSE = 0.0034 for the training data and R ² = 0.94 and RMSE = 0.086 for the testing data).     

Advancing understanding using Nonaka’s model of knowledge creation and problem-based learning

Nonaka’s model of knowledge creation can provide guidance for designing learning environments and activities. However, Bereiter is critical of the model because it does not address whether understanding is deepened in the process of socialization, externalization, combination and internalization. To address this issue of understanding, this paper proposed a framework that synthesizes the basic phases of problem-based learning with Nonaka’s model. This paper reports on a study investigating if a course designed based on this authentic framework can help to stimulate knowledge creation based on deepening understanding. Several types of data were collected in this design-based research, namely: reflections by the participants and instructor; group discussions; student-created artifacts; and documents, records and artifacts that reflect the overall design of the course. The findings suggest that the participants demonstrated advancing understanding amidst knowledge creating conditions and processes consistent with Nonaka’s model. Other key implications are also discussed.     

Satellite survey of seasonal trophic status and occasional anoxic ‘malaïgue’ crises in the Thau lagoon using MERIS images

The Thau lagoon, located in southern France, suffers episodically from anoxic crises locally known as ‘malaïgue’. Such crises mostly occur under warm conditions, low winds leading to a strong eutrophication of the lagoon. The development of a sulphur bacterium sometimes gives locally to the waters a ‘milky turquoise’ appearance and leads to shellfish mortality. One of the indicators of the eutrophication status of the lagoon can be surveyed by the chlorophyll product provided by remote sensing images such as Medium Resolution Imaging Spectrometer (MERIS). In this paper we compare chl2 (or algal2) estimations provided by MERIS level 2 products and the ground measurements of chlorophyll a concentrations in water and we propose a linear correction of the chl2 MERIS product. The corrected chl2 estimations obtained over four years are analysed to understand the seasonal evolution of the trophic status of the Thau lagoon. We also study the influence of the anoxic crises of summers 2003 and 2006 on the chl2 estimations and we find a strong correlation between chl2 and the oxygen percentage at 1 m depth (0.70 for measurements in summers 2003 and 2006).     

Application of astigmatic method and snell’s law on the thickness and refractive index measurement of a transparent plate

In this study, a thickness and refractive index measuring system for the transparent plate has been proposed. The measuring system is composed of an astigmatic measurement module and a light spot measurement module using a DVD optical pickup, a laser diode and a quadrant photodiode detector. Both of the thickness and refractive index of a transparent plate are converted into the focusing error of DVD pickup based on astigmatic method and the light spot displacements based on snell’s law in our proposed system. The thickness and the refractive index are simultaneously calculated by means of two nonlinear formulas. The accuracy of the proposed system is 99 % verified by a height Gauge and the refractive index by the TF-166.     

Integration of panchromatic and SAR features into multispectral SPOT images using the ‘à trous’ wavelet decomposition

A method is described for integrating panchromatic (P) and synthetic aperture radar (SAR) features into multispectral (XS) images using conjointly the modified Brovey transform (MBT) and the ‘à trous’ wavelet decomposition (ATDW). The MBT is based on the local modulation of each multispectral image by the ratio of the new and initial intensity components to produce new multispectral images directly. The ATWD allows extraction of features from P and SAR images, which are combined through a feature selection rule to integrate into the initial intensity component. For evaluating the effect of each feature selection on new XS images, experimental results are conducted on SPOT (XS, P) and Radarsat (SAR) images using both visual inspection and many refined statistical measures.     

Perception of Order and Ambiguity in Leonardo’s Design Concepts

Leonardo da Vinci used geometry to give his design concepts both structural and visual balance. The paper examines aesthetic order in Leonardo’s structural design, and reflects on his belief in analogy between structure and anatomy. Leonardo’s drawings of grids and roof systems are generated from processes best known from ornamentation and can be developed into spatial structures assembled from loose elements with no need for binding elements. His architectural plans are patterns based on principles of tessellation, tiling and recursion, also characteristic of the reversible, ambiguous structures which led to Leonardo’s further inventions in structural and mechanical design as well as dynamic representations of space in his painting. In recent times, the ambiguous structures in the art of Joseph Albers, the reversible and impossible structures of M. C. Escher, the recurring patterns and spherical geometry of Buckminster Fuller and the reciprocal grids in structural design of Cecil Balmond display a similar interest. Computer models and animations have been used to simulate processes of perceiving and creating ambiguity in structures.     

Identification of material parameters for McGinty’s model using adaptive RBFs and optimization

In this paper, an optimization approach is adopted to obtain the 12 material parameters used in McGinty’s Model for AL6022 by minimizing the differences between simulation and experimental stress–strain curves. Since the differences between the two stress–strain curves are implicitly related to the change of material parameters, the metamodeling technique is utilized to create explicit, approximate functions of these relationships. Radial basis functions (RBFs), which are shown from previous studies to be effective for both low- and high-order nonlinear responses, are used for the metamodels that are adaptively updated in the optimization work. Two optimization formulation schemes are studied to address the issue of using inaccurate RBF models in optimization. The sampling, metamodeling, and optimization works are performed using the integrated optimization framework HiPPO.     

New technique of obtaining visually perceived positions of 3-D images using movements of users’ bodies

Three-dimensional (3-D) images are perceived as images that float in front of the screens of 3-D displays. Users should be able to interact with these images instantaneously and accurately in applications where their bodies actually seen by them interact with the images. However, conventional techniques using just binocular disparity are too slow and inaccurate. Therefore, we propose a new technique where the visually perceived positions of images are obtained from the body movements of users. The feasibility of this technique was evaluated in an experiment using the positions obtained from users as they reached out to touch the images. These positions were closer to the visually perceived positions of the images than those calculated from binocular disparity. These findings demonstrate the feasibility of the proposed technique for 3-D interactive applications.     

A psycho-cognitive segmentation of organ donors in Egypt using Kohonen’s self-organizing maps

This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on organ donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting organ donation are related to perceived benefits/risks of organ donation, organ donation knowledge, attitudes toward organ donation, and intention to donate organs. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.     

Identifying potential areas of understorey coffee in Ethiopia’s highlands using predictive modelling

Coffee production is one of the main economic activities in Ethiopia, representing about 40% of the country’s economy. Coffee is particularly important in the Ethiopian highlands, where appropriate climate allows higher productivity and quality. The Ethiopian highlands also host an outstanding biodiversity, being considered one of the world’s most important biodiversity hotspots. In this context, conciliating agricultural practices with biodiversity conservation is a priority task for researchers and other stakeholders. However, identifying and mapping understorey coffee plantations in Ethiopian highlands is particularly challenging due to the presence of scattered exotic trees and the characteristics of understorey cultivation. In this research, we mapped potential areas of understorey coffee using predictive modelling and evaluated how projected changes in climate would affect the suitability of coffee production in the study area. Landscape maps, which were mapped using remote-sensing data based on object-based image analysis, remotely sensed spectral vegetation indices, and climatic variables were used to delineate probability maps showing the most likely location of understorey coffee. Normalized difference vegetation index and maximum temperature and precipitation were considered the best predictors for explaining the spatial distribution of understorey coffee. The accuracy of the probability map was validated based on existing understorey coffee areas mapped during field surveys. In addition, we show that potential changes in temperature and precipitation by 2050 are likely to shift suitable areas of understorey coffee to higher altitudes, affecting the landscape changes dynamics in the region.     

Optimization of extrusion blow molding processes using soft computing and Taguchi’s method

The objective of this study is to present a new numerical strategy using soft-computing techniques to determine the optimal die gap programming of extrusion blow molding processes. In this study, the design objective is to target a uniform part thickness after parison inflation by manipulating the parison die gap openings over time. To model the whole process, that is, the parison extrusion, the mould clamping and the parison inflation, commercial finite element software (BlowSim) from the National Research Council (NRC) of Canada is used. However, the use of such software is time-consuming and one important issue in a design environment is to minimize the number of simulations to get the optimal operating conditions. To do so, we proposed a new strategy called fuzzy neural–Taguchi network with genetic algorithm (FUNTGA) that establishes a back propagation network using a Taguchis experimental array to predict the relationship between design variables and responses. Genetic algorithm (GA) is then applied to search for the optimum design of die gap parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The extrapolation distance concept is proposed and introduced to GA using fuzzy rules to modify the fitness function and thus improving search efficiency. The comparison of the results with commercial optimization software from NRC demonstrates the effectiveness of the proposed approach.     

Simulations of Shor’s algorithm using matrix product states

We show that under the matrix product state formalism the states produced in Shor’s algorithm can be represented using \(O(\max (4lr^2, 2^{2l}))\) space, where l is the number of bits in the number to factorise and r is the order and the solution to the related order-finding problem. The reduction in space compared to an amplitude formalism approach is significant, allowing simulations as large as 42 qubits to be run on a single processor with 32 GB RAM. This approach is readily adapted to a distributed memory environment, and we have simulated a 45-qubit case using 8 cores with 16 GB RAM in approximately 1 h.     

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.     

Image indexing and content analysis in children’s picture books using a large-scale database

In this paper we introduce a visual database for children’s picture book and we also present an intelligent robot trained on this database. Firstly, a large-scale image dataset is built that contains image samples of book pages. It can be used to verify image indexing algorithms and content recognition algorithms. Secondly, we study the state-of-the-art algorithms in image matching and object recognition. Several approaches are presented and compared from the aspects of computational efficiency and recognition accuracy. In order to improve the speed we proposed a novel hierarchical algorithm for fast search. Finally, using this large-scale database we are able to build a robot that can read children’s picture books and initial experimental results are presented. We can see that both the training database and the algorithms are promising, yet there are still a few open challenges concerning the costs and robustness.

     

Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network

Multimedia Tools and Applications - Alzheimer’s disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the...     

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.