Feature selection or the process of selecting the most discriminating feature subset is an essential practice in speech processing that significantly affects the performance of classification. However, the volume of features that presents in speech processing makes the feature selection perplexing. Moreover, determining the best feature subset is a NP-hard problem (2n). Thus, a good searching strategy is required to avoid evaluating large number of combinations in the whole feature subsets. As a result, in recent years, many heuristic based search algorithms are developed to address this NP-hard problem. One of the several meta heuristic algorithms that is applied in many application domains to solve feature selection problem is Ant Colony Optimization (ACO) based algorithms. ACO based algorithms are nature-inspired from the foraging behavior of actual ants. The success of an ACO based feature selection algorithm depends on the choice of the construction graph with respect to runtime behavior. While most ACO based feature selection algorithms use fully connected graphs, this paper proposes ACO based algorithm that uses graphs with prescribed degree sequences. In this method, the degree of the graph representing the search space will be predicted and the construction graph that satisfies the predicted degree will be generated. This research direction on graph representation for ACO algorithms may offer possibilities to reduce computation complexity from O(n2) to O(nm) in which m is the number of edges. This paper outlines some popular optimization based feature selection algorithms in the field of speech processing applications and overviewed ACO algorithm and its main variants. In addition to that, ACO based feature selection is explained and its application in various speech processing tasks is reviewed. Finally, a degree based graph construction for ACO algorithms is proposed. � 2018, Insight Society.

In blood cell image analysis, segmentation is crucial step in quantitative cytophotometry. Blood cell images have become particularly useful in medical diagnostics tools for cases involving blood. In this paper, we present a better approach on merging segmentation algorithms of K-means and Median-cut for colour blood cells images. Median-cut technique will be employed after comparing best outcomes from Fuzzy c-means, K-means and Means-shift. We used blood cell images infected with malaria parasites as cell images for our research. The result of proposed method shows better improvement in terms of object segmentations for further feature extraction process. � 2011 IEEE.

Basically, detecting convex and concave points on the boundary of an object plays an important role in computer vision, object recognition and image understanding. In this paper a method that combines boundary and skeleton information for detecting these critical points is proposed. Specifically, the method is developed with the aim of obtaining high performance and efficiency, and producing a more robust method in detecting concave and convex points with minimum cost of computation. Furthermore, for faster execution of the proposed method, the detection of convex and concave points can be run concurrently. In order to evaluate the performance of the proposed method, the results of the proposed method are compared with three related convex and concave points detection methods. The experimental results have shown that the proposed method provides better output and detection rate. � 2010 IEEE.

Many factors, such as noise level in the original image and the noise-removal methods that clean the image prior to performing a vectorization, may play an important role in affecting the line detection of raster-to-vector conversion methods. In this paper, we propose an empirical performance evaluation methodology that is coupled with a robust statistical analysis method to study many factors that may affect the quality of line detection. Three factors are studied: noise level, noise-removal method, and the raster-to-vector conversion method. Eleven mechanical engineering drawings, three salt-and-pepper noise levels, six noise-removal methods, and three commercial vectorization methods were used in the experiment. The Vector Recovery Index (VRI) of the detected vectors was the criterion used for the quality of line detection. A repeated measure ANOVA analyzed the VRI scores. The statistical analysis shows that all the studied factors affected the quality of line detection. It also shows that two-way interactions between the studied factors affected line detection. Copyright � 2011 The Institute of Electronics, Information and Communication Engineers.

In this paper an enhanced background subtraction approach for image segmentation is proposed in order to precisely detect and represent the objects in the images. It is based on automatic detection of the background by estimating the background and then subtracting it from the original image. This step is incorporated in the background subtraction approach in order to reduce the computational cost and overcome the problems where the environment is complex such as underwater images and when there are many kinds of objects. The segmentation results using this enhanced approach are compared with the recent background subtraction techniques in terms of speed and accuracy in order to show the efficiency and the effectiveness of the proposed approach. � 2010 IEEE.

Underwater images are affected by reduced contrast and non-uniform colour cast due to the absorption and scattering of light in the aquatic environment. This affects the quality and reliability of image processing and therefore colour correction is a necessary pre-processing stage. In this paper, we propose an Unsupervised Colour Correction Method (UCM) for underwater image enhancement. UCM is based on colour balancing, contrast correction of RGB colour model and contrast correction of HSI colour model. Firstly, the colour cast is reduced by equalizing the colour values. Secondly, an enhancement to a contrast correction method is applied to increase the Red colour by stretching red histogram towards the maximum (i.e., right side), similarly the Blue colour is reduced by stretching the blue histogram towards the minimum (i.e., left side). Thirdly, the Saturation and Intensity components of the HSI colour model have been applied for contrast correction to increase the true colour using Saturation and to address the illumination problem through Intensity. We compare our results with three well known methods, namely Gray World, White Patch and Histogram Equalisation using Adobe Photoshop. The proposed method has produced better results than the existing methods. �2010 IEEE.

In digital signal processing, speech processing is one of the areas that is used in many type of applications. It is one of an intensive field of research. The major criterion for good speech processing system is the selection of feature extraction technique, which plays a major role in achieving higher accuracy. In this paper, most commonly used techniques for feature extraction such as Linear Predictive Coefficient (LPC), Mel Frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP) and Wavelet Transform (WT) are presented. Comparisons that highlight the strengths and the weaknesses of these techniques are also presented. Studies show that feature extraction techniques are mainly selected based on the requirement of the applications. Wavelet transform outperform other techniques for the analysis of non-stationary signals in audio signal. Enhanced Wavelet transform technique is a way forward and studies can be focused on its coefficients. Hybrid methods can be further explored to increase the performance in speech processing. A number of hybrid methods were reviewed, and studies show that Mel-Frequency Cepstral Coefficients (WPCC) provide better results for speech processing applications with standard coefficient for classification. � 2019, World Academy of Research in Science and Engineering. All rights reserved.

Outdoor machine vision is getting a concern nowadays. Ranging from surveillance and monitoring system to automotive system such as driver assistance system require vision application or artificial eye to keep monitoring the situations. However, most of these applications works very well during clear weather and degrade during bad weather due to the atmospheric particles mitigate the quality of vision system. This paper discuss the state of the art of image enhancement techniques used to adjust the contrast of an outdoor image degrade by fog, haze, and rain. A brief overview of bad weather will be discussed and several recent techniques on removing fog, haze and rain are discussed. � 2013 IEEE.

Clustering is one of the most common automated segmentation techniques used in the fields of bioinformatics applications specifically for the microscopic image processing usage. Recently many scientists have performed tremendous research in helping the haematologists in the issue of segmenting the leukocytes region from the blood cells microscopic images in the early of prognosis. During the post processing, image filtering can cause some discrepancies on the processed image which may lead to insignificant result. This research aims to segment the blood cell microscopic images of patients suffering from acute leukaemia. In this research we are using three clustering techniques which are (Fuzzy C-Means (FCM), Classic K-Means (CKM) and Enhanced K-Means (EKM) then we performed filtering techniques which are Mean-shift Filtering (MSF) and Seeded Region Growing (SRG). We tested individual clustering, from the results it show Enhanced K-Means gives the best result. We performed hybrid between EKM and MSF gave a better result from other comparison. The integrated clustering techniques have produced tremendous output images with minimal filtering process to remove the background scene. � 2005 - 2015 JATIT & LLS. All rights reserved.

Clustering is one of the most common automated image segmentation techniques used in many fields including machine learning, pattern recognition, image processing, and bioinformatics. Recently many scientists have performed tremendous research in helping the hematologists in the issue of segmenting the blood cells in the early of prognosis. This paper aims to segment the blood cell images of patients suffering from acute leukemia using an adaptive K-Means clustering together with mean shift algorithm. The integrated clustering techniques have produced comprehensive output images with minimal filtering process to remove the background scene. � 2013 IEEE.

Automatic vehicle detection in urban traffic surveillance is an important and urgent issue, since it provides necessaryinformation for further processing. Conventional techniques utilize either motion segmentation or appearance-baseddetection, which involves either poor adaptation or high computation. The complexity of urban traffic scenarios lies in slowmotion temporarily stopped or parked vehicles, dynamic background, and sudden illumination variations. In this paper, anew motion segmentation technique is proposed based on spatio-temporal background–foreground bimodal. The temporalbackground information is modeled using a weighted sigma–delta estimation, cumulative frame differencing is used tomodel the foreground pixels, and the spatial correlation between neighboring pixels is utilized to combine both backgroundand foreground models. The median of consecutive frame difference adapts sudden illumination variation, update back-ground model, and reinitialize foreground model. Comparative experimental results for typical urban traffic sequencesshow that the proposed technique achieves robust and accurate segmentation, which improves adaptation, reduce falsedetection, and satisfy real-time requirements. KeywordsMotion segmentation-Background subtraction-Cumulative frame differencing-Sigma–delta filter-Vehicle detection

Researchers concerned with Speech Emotion Recognition have proposed various useful features associated with their performance analysis related to emotions. However, a majority of the studies rely on acoustic features, characterized by vocal tract responses. The usefulness of vocal source related features has not been extensively explored, even though they are expected to convey useful emotion-related information. In this research, we study the significance of vocal source related features in Speech Emotion Recognition and assess the comparative performance of vocal source related features and vocal tract related features in emotion identification. The vocal source related features are extracted from the Linear Prediction residuals. The study shows that the vocal source related features contain emotion discriminant information and integrating them with vocal tract related features leads to performance improvement in emotion recognition rate.

The Holy Quran being the most comprehensive book of reference has discussed on every minute details of human anatomy. One of them is human senses and can be categorized by external parts and internal parts. For example external parts such as ears are used to sense audio, eyes are used to sense visual, and nose is used to sense smell. While for internal parts, emotion is used to sense feelings such as happiness and sadness. Although all of the senses that comes from the external and internal parts are important in human�s well-being, the Holy Quran has placed different emphasizes on all of these senses. This is because some of the senses are quoted more often than others. Using data extraction technology, this study finds that ears and eyes are quoted most often although ears seem to be more superior. Therefore, it is the purpose of this paper to investigate the reason why ears are quoted more often than eyes in the Holy Quran. In addressing this question, this paper applies two methods. Firstly, all verses related to sound and sight senses are gathered. Then the superiority between them are measured based on the book of exegesis (tafsir) and scientific reports. 2017 American Scientific Publishers All rights reserved.

The impact of rain streaks on single images can make it difficult to recognize the surrounding environment using an outdoor camera. Furthermore, a single image is important to use in numerous areas such as in object recognition and scientific research. Therefore, outdoor images and videos in rainy weather conditions will reduce visibility and damage the performance of computer vision algorithms used for extracting features and information from images. This study proposes a new algorithm as a suggestion for the detection and removal of rain streaks in a single image using total variation and sparse coding to restore images. This proposed algorithm will use a retrieval method from a case-based reasoning approach. The experiments and statistical measurements, namely Mean Square Error (MSE), Peak-Signal Noise Ratio (PSNR), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF) and Blind or Referenceless Image Spatial Quality Evaluator (BRISQUE) are used to distinguish which method has better accuracy. The results demonstrated an advantage for our proposed algorithm for the removal of rain streaks. � Medwell Journals, 2017.

The haze phenomenon exerts a degrading effect that decreases contrast and causes colour shifts in outdoor images. The presence of haze in digital images is bothersome, unpleasant, and, occasionally, even dangerous. The atmospheric light scattering (ALS) model is widely used to restore hazy images. In this model, two unknown parameters should be estimated: airlight and scene transmission. The quality of dehazed images depends considerably on the accuracy of both estimates. Classic methods typically determine airlight based on the brightest pixels in an image. However, in the traffic scene context, this estimate is compromised when other light sources, such as vehicle headlights from the opposite direction, are present. Transmission estimation is usually more complicated. Hence, the complexity of the overall dehazing process is dependent on this estimate. To address this issue, this study proposes a framework for constant-time airlight and linear transmission estimation. This framework consists of two methods: airlight by image integrals (ALII), which is utilized to estimate the airlight value in real time with high accuracy, and bounded transmission (BT), which is proposed for the linear and simplified estimation of transmission maps. To evaluate the proposed framework, three image datasets are used: (1) seven images that are gathered from the works of existing methods (called the global dataset); (2) the synthetic foggy road image database (FRIDA), which is a synthetically generated dataset for simulating different bad weather conditions; and (3) a dataset of images that were extracted from videos in Malaysia (IV-M), which consists of images that were extracted from traffic video sequences, which were captured in various weather conditions from 2014 to 2016 in Malaysia. Experimental results show that the proposed framework is at least seven times faster than existing methods. In addition, the ANOVA test proves that the quality of the dehazed images is statistically similar to or better than the image quality that was achieved using existing methods. � 2018 Elsevier Inc.

Rain, fog and haze are natural phenomena that fade scenes, limit the visibility range, and cause shifts in colors. These phenomena also play a decisive role in determining the degree of reliability of many kinds of outdoor applications, such as aerial and satellite imaging, surveillance, and driver assistance systems. Thus, removing their effects from images/videos is very crucial. Due to its mathematically ill posed nature, enhancement process of rain, fog, and haze plagued images/videos is highly challenging. In this paper, we propose a fast yet robust technique to enhance the visibility of video frames using the dark channel prior combined with fuzzy logic-based technique. The dark channel prior is a statistical regularity of outdoor haze-free images based on the observation that most local patches in the haze-free images contain pixels which are dark in at least one color channel, where the fuzzy logic-based technique is used to map an input space to an output space using a collection of fuzzy membership functions and rules to decide softly in case of uncertainties. The combination of the dark channel and the fuzzy logic-based technique will produce high quality haze-free images in real-time. Furthermore, it will be combined with rules derived from the stable atmospheric scattering model and will yield a fast yet high quality enhancement results. � 2014 IEEE.

Enhancing images that are plagued with weather related conditions; such as haze, fog and rain poses a challenging problem due to its ill-posed nature, which means the unknowns that need to be found are more than the equations that we have. To address such challenges, a fast yet robust method is proposed in this paper where unknowns in the light scattering model are estimated based on physically sound assumptions. Light scattering model describes the formation of those phenomena in an image as a combination of airlight and the original scene, where this combination is controlled by how much transmission value present at the scene's point. The transmission value determines how much of the original scene's intensity were attenuated and how much airlight was added. The attenuation term of the light scattering model causes the reduction in contrast and the airlight term causes the effect of color shift. In this paper, Intensity Deterioration Ratio (IDR) and Saturation Deterioration Ratio (SDR) are proposed, where the former can be used to estimate the reduction of contrast and so gives a clue about the attenuation term, and the latter to estimate the reduction of chromaticity in a scene which gives a clue about the airlight term. IDR and SDR are therefore used to give us a new insight in using the light scattering model when enhancing images. � 2015 IEEE.

The Holy Quran ontology models are gaining popularity among researchers due to people's demands in understanding this divine book. Due to this, there are many studies and research have been conducted in this area to facilitate people's understanding of the Quran. The Quran knowledge is represented conforming to an ontology within a system framework. This also includes various concepts that are interrelated with the others. From the literature, however, the existing Quranic ontology models do not cover all concepts in the Quran, which limit them to domains such as place nouns, themes, pronouns, antonyms and Islamic knowledge in the Quran. Thus, this research aims to identify relevant research works from various electronic data sources using systematic literature review (SLR) method to provide a comprehensive review of this area. This paper presents a systematic review of the literature related to the existing ontology models, where it leads to disseminating the correct knowledge of the Quran using semantic technologies.

Requirements engineering (RE) is an integral and fundamental part of software development life cycle (SDLC). The most cited reason for software failure is the inability to fully capture and implement the exact user requirements in a timely, operationally and financially expected manner. Requirements can be categorized into two types; explicit and tacit. Explicit requirements usually expressed clearly and well-identified requirements in which the system must perform. In contrast, tacit requirements are considered as hidden or embedded requirements that incapability of getting such may caused a software failure. The importance of tacit requirements in requirements engineering (RE) has been acknowledged widely as characteristics for developing projects in software engineering (SE). Therefore, the aim of this paper is to construct a framework for tacit requirement elicitation process. The framework is design based on the integration of the tacit knowledge elicitation process of Nonaka and Takeuchi model with the tacit requirements elicitation process. � 2006-2015 Asian Research Publishing Network (ARPN).

Video-based analysis of traffic surveillance is an active area of research, which has a wide variety of applications in intelligent transport systems (ITSs). In particular, urban environments are more challenging than highways due to camera placement, background clutter, and vehicle pose or orientation variations. This paper provide a comprehensive review of the state-of-the-art video processing techniques for vehicle detection, recognition and tracking with analytical description. In this survey, we categorize vehicle detection into motion and appearance based techniques, varying from simple frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling and feature extracting. We also discuss vehicle recognition and classification utilizing vehicle attributes like color, license plate, logo and type, provide a detailed description of the advances in the field. Next we categorize tracking into model, region and features based tracing. Finally tracking algorithms including Kalman and particle filter are discussed in term of correspondence matching, filtering, estimation and dynamical models. � Research India Publications.