Search results for: color and texture features

Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

Abstract:

The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: Terrain classification, acoustic features, autonomous robots, feature extraction.

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Authors: Niranjali Jayasuirya, Majell Backhausen

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Water Sensitive Urban Design (WSUD) features are increasingly used to treat and manage polluted stormwater runoff in urbanised areas. It is important to monitor and evaluate the effectiveness of the infrastructure in achieving their intended performance targets after constructing and operating these features overtime. The paper presents the various methods of analysis used to assess the effectiveness of the in-situ WSUD features, such as: onsite visual inspections during operational and non operational periods, maintenance audits and periodic water quality testing. The results will contribute to a better understanding of the operational and maintenance needs of in-situ WSUD features and assist in providing recommendations to better manage life cycle performance.

Keywords: Bio-retention swales, Maintenance plan, Operational plan, Water Sensitive Urban Design, Water quality improvement.

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Authors: Shahram Mohammadi, Ali Frajzadeh

Abstract:

A lot of matching algorithms with different characteristics have been introduced in recent years. For real time systems these algorithms are usually based on minutiae features. In this paper we introduce a novel approach for feature extraction in which the extracted features are independent of shift and rotation of the fingerprint and at the meantime the matching operation is performed much more easily and with higher speed and accuracy. In this new approach first for any fingerprint a reference point and a reference orientation is determined and then based on this information features are converted into polar coordinates. Due to high speed and accuracy of this approach and small volume of extracted features and easily execution of matching operation this approach is the most appropriate for real time applications.

Keywords: Matching, Minutiae, Reference point, Reference orientation

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Authors: Yao-Hong Tsai

Abstract:

This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: Image fusion, iris recognition, local binary pattern, wavelet.

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Authors: Piercarlo Dondi, Luca Lombardi, Marco Porta

Abstract:

In the last years numerous applications of Human- Computer Interaction have exploited the capabilities of Time-of- Flight cameras for achieving more and more comfortable and precise interactions. In particular, gesture recognition is one of the most active fields. This work presents a new method for interacting with a virtual object in a 3D space. Our approach is based on the fusion of depth data, supplied by a ToF camera, with color information, supplied by a HD webcam. The hand detection procedure does not require any learning phase and is able to concurrently manage gestures of two hands. The system is robust to the presence in the scene of other objects or people, thanks to the use of the Kalman filter for maintaining the tracking of the hands.

Keywords: Gesture recognition, human-computer interaction, Time-of-Flight camera.

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Authors: Aiman Elragig, Hanan Dreiwi, Dung Ly, Idriss Elmabrook

Abstract:

This paper highlights a new approach to look at online principle components analysis (OPCA). Given a data matrix X ∈ R,^m x n we characterise the online updates of its covariance as a matrix perturbation problem. Up to the principle components, it turns out that online updates of the batch PCA can be captured by symmetric matrix perturbation of the batch covariance matrix. We have shown that as n→ n0 >> 1, the batch covariance and its update become almost similar. Finally, utilize our new setup of online updates to find a bound on the angle distance of the principle components of X and its update.

Keywords: Online data updates, covariance matrix, online principle component analysis (OPCA), matrix perturbation.

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Authors: Vesile Evrim, Aliyu Awwal

Abstract:

By the evolvement in technology, the way of expressing opinions switched direction to the digital world. The domain of politics, as one of the hottest topics of opinion mining research, merged together with the behavior analysis for affiliation determination in texts, which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 were constituted by Linguistic Inquiry and Word Count (LIWC) features were tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that the “Decision Tree”, “Rule Induction” and “M5 Rule” classifiers when used with “SVM” and “IGR” feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “Function”, as an aggregate feature of the linguistic category, was found as the most differentiating feature among the 68 features with the accuracy of 81% in classifying articles either as Republican or Democrat.

Keywords: Politics, machine learning, feature selection, LIWC.

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Authors: H. Benjamin Fredrick David, R. Balasubramanian, A. Anbarasa Pandian

Abstract:

Image retrieval is the most interesting technique which is being used today in our digital world. CBIR, commonly expanded as Content Based Image Retrieval is an image processing technique which identifies the relevant images and retrieves them based on the patterns that are extracted from the digital images. In this paper, two research works have been presented using CBIR. The first work provides an automated and interactive approach to the analysis of CBIR techniques. CBIR works on the principle of supervised machine learning which involves feature selection followed by training and testing phase applied on a classifier in order to perform prediction. By using feature extraction, the image transforms such as Contourlet, Ridgelet and Shearlet could be utilized to retrieve the texture features from the images. The features extracted are used to train and build a classifier using the classification algorithms such as Naïve Bayes, K-Nearest Neighbour and Multi-class Support Vector Machine. Further the testing phase involves prediction which predicts the new input image using the trained classifier and label them from one of the four classes namely 1- Normal brain, 2- Benign tumour, 3- Malignant tumour and 4- Severe tumour. The second research work includes developing a tool which is used for tumour stage identification using the best feature extraction and classifier identified from the first work. Finally, the tool will be used to predict tumour stage and provide suggestions based on the stage of tumour identified by the system. This paper presents these two approaches which is a contribution to the medical field for giving better retrieval performance and for tumour stages identification.

Keywords: Brain tumour detection, content based image retrieval, classification of tumours, image retrieval.

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Authors: J. I. Asnor, S. Rosnah, Z. W. H. Wan, H. A. B. Badrul

Abstract:

Pineapples can be classified using an index with seven levels of maturity based on the green and yellow color of the skin. As the pineapple ripens, the skin will change from pale green to a golden or yellowish color. The issues that occur in agriculture nowadays are to do with farmers being unable to distinguish between the indexes of pineapple maturity correctly and effectively. There are several reasons for why farmers cannot properly follow the guideline provide by Federal Agriculture Marketing Authority (FAMA) and one of reason is that due to manual inspection done by experts, there are no specific and universal guidelines to be adopted by farmers due to the different points of view of the experts when sorting the pineapples based on their knowledge and experience. Therefore, an automatic system will help farmers to identify pineapple maturity effectively and will become a universal indicator to farmers.

Keywords: Artificial Neural Network, Image Processing, Index of Maturity, Pineapple

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Authors: Sanpachai Huvanandana

Abstract:

A human verification system is presented in this paper. The system consists of several steps: background subtraction, thresholding, line connection, region growing, morphlogy, star skelatonization, feature extraction, feature matching, and decision making. The proposed system combines an advantage of star skeletonization and simple statistic features. A correlation matching and probability voting have been used for verification, followed by a logical operation in a decision making stage. The proposed system uses small number of features and the system reliability is convincing.

Keywords: Human verification, object recognition, videounderstanding, segmentation.

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Authors: Hasup Lee, Yoshisuke Tateyama, Tetsuro Ogi

Abstract:

We developed a vision interface immersive projection system, CAVE in virtual rea using hand gesture recognition with computer vis background image was subtracted from current webcam and we convert the color space of the imag Then we mask skin regions using skin color range t a noise reduction operation. We made blobs fro gestures were recognized using these blobs. Using recognition, we could implement an effective bothering devices for CAVE. e framework for an reality research field vision techniques. ent image frame age into HSV space. e threshold and apply from the image and ing our hand gesture e interface without

Keywords: CAVE, Computer Vision, Ges Virtual Reality esture Recognition,

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Authors: Jihan Abdulazeez Ahmed, Adnan Mohsin Abdulazeez Brifcani

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This paper suggests a new internal architecture of holon based on feature selection model using the combination of Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is used to generate features while ANN is used as a classifier to evaluate the produced features. Proposed system is applied on the Wine dataset, the statistical result proves that the proposed system is effective and has the ability to choose informative features with high accuracy.

Keywords: Artificial Neural Networks, Holonic Approach, Feature Selection, Bee Algorithm.

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Authors: Rifah Ediati, Jajang

Abstract:

The quality of Ribbed Smoked Sheets (RSS) primarily based on color, dryness, and the presence or absence of fungus and bubbles. This quality is strongly influenced by the drying and fumigation process namely smoking process. Smoking that is held in high temperature long time will result scorched dark brown sheets, whereas if the temperature is too low or slow drying rate would resulted in less mature sheets and growth of fungus. Therefore need to find the time and temperature for optimum quality of sheets. Enhance, unmonitored heat and mass transfer during smoking process lead to high losses of energy balance. This research aims to generate simple empirical mathematical model describing the effect of smoking time and temperature to RSS quality of color, water content, fungus and bubbles. The second goal of study was to analyze energy balance during smoking process. Experimental study was conducted by measuring temperature, residence time and quality parameters of 16 sheets sample in smoking rooms. Data for energy consumption balance such as mass of fuel wood, mass of sheets being smoked, construction temperature, ambient temperature and relative humidity were taken directly along the smoking process. It was found that mathematical model correlating smoking temperature and time with color is Color = -169 - 0.184 T4 - 0.193 T3 - 0.160 0.405 T1 + T2 + 0.388 t1 +3.11 t2 + 3.92t3 + 0.215 t4 with R square 50.8% and with moisture is Moisture = -1.40-0.00123 T4 + 0.00032 T3 + 0.00260 T2 - 0.00292 T1 - 0.0105 t1 + 0.0290 t2 + 0.0452 t3 + 0.00061 t4 with R square of 49.9%. Smoking room energy analysis found useful energy was 27.8%. The energy stored in the material construction 7.3%. Lost of energy in conversion of wood combustion, ventilation and others were 16.6%. The energy flowed out through the contact of material construction with the ambient air was found to be the highest contribution to energy losses, it reached 48.3%.

Keywords: RSS quality, temperature, time, smoking room, energy

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Authors: Veeresham Mokali

Abstract:

The refractory alloys are crucial for high-temperature applications to improve performance and reduce cost. They are used in several applications such as aerospace, outer space, military and defense, nuclear powerplants, automobiles, and industry. The conventional refractory alloys show greater stability at high temperatures and in contrast they have operational limitations due to their low melting temperatures. However, there is a huge requirement to improve the refractory alloys’ operational temperatures and replace the conventional alloys. The newly emerging refractory high entropy alloys (RHEAs) could be alternative materials for conventional refractory alloys and fulfill the demands and requirements of various practical applications in the future. The RHEA TaHfNbZrTi was prepared through an arc melting process. The annealing behavior of severely deformed equiatomic RHEATaHfNbZrTi has been investigated. To obtain deformed condition, the alloy is cold-rolled to 90% thickness reduction and then subjected to an annealing process to observe recrystallization and microstructural evolution in the range of 800 °C to 1400 °C temperatures. The cold-rolled – 90% condition shows the presence of microstructural heterogeneity. The annealing microstructure of 800 °C temperature reveals that partial recrystallization and further annealing treatment carried out annealing treatment in the range of 850 °C to 1400 °C temperatures exhibits completely recrystallized microstructures, followed by coarsening with a degree of annealing temperature. The deformed and annealed conditions featured the development of body-centered cubic (BCC) fiber textures. The experimental investigation of heavy deformation and followed by high-temperature annealing up to 1400 °C temperature will contribute to the understanding of microstructure and texture evolution of emerging RHEAs.

Keywords: Refractory high entropy alloys, cold-rolling, annealing, microstructure, texture.

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Authors: Rajneesh Rani, Renu Dhir, G. S. Lehal

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Script identification is one of the challenging steps in the development of optical character recognition system for bilingual or multilingual documents. In this paper an attempt is made for identification of English numerals at word level from Punjabi documents by using Gabor features. The support vector machine (SVM) classifier with five fold cross validation is used to classify the word images. The results obtained are quite encouraging. Average accuracy with RBF kernel, Polynomial and Linear Kernel functions comes out to be greater than 99%.

Keywords: Script identification, gabor features, support vector machines.

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Authors: Pinki Sharma, Himanshu Joshi

Abstract:

Distillery spentwash contains high chemical oxygen demand (COD), biological oxygen demand (BOD), color, total dissolved solids (TDS) and other contaminants even after biological treatment. The effluent can’t be discharged as such in the surface water bodies or land without further treatment. Reverse osmosis (RO) treatment plants have been installed in many of the distilleries at tertiary level in many of the distilleries in India, but are not properly working due to fouling problem which is caused by the presence of high concentration of organic matter and other contaminants in biologically treated spentwash. In order to make the membrane treatment a proven and reliable technology, proper pre-treatment is mandatory. In the present study, ultra-filtration (UF) for pretreatment of RO at tertiary stage has been performed. Operating parameters namely initial pH (pHo: 2–10), trans-membrane pressure (TMP: 4-20 bars) and temperature (T: 15-43°C) were used for conducting experiments with UF system. Experiments were optimized at different operating parameters in terms of COD, color, TDS and TOC removal by using response surface methodology (RSM) with central composite design. The results showed that removal of COD, color and TDS was 62%, 93.5% and 75.5% respectively, with UF, at optimized conditions with increased permeate flux from 17.5 l/m2/h (RO) to 38 l/m2/h (UF-RO). The performance of the RO system was greatly improved both in term of pollutant removal as well as water recovery.

Keywords: Bio-digested distillery spentwash, reverse osmosis, Response surface methodology, ultra-filtration.

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Authors: Fahanim Abdul Rashid, Muhammad Azzam Ismail, Deo Prasad

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In order to encourage the construction of green homes (GH) in Malaysia, a simple and attainable framework for designing and building GHs is needed. This can be achieved by aligning GH principles against Cole-s 'Sustainable Building Criteria' (SBC). This set of considerations was used to categorize the GH features of three case studies from Malaysia. Although the categorization of building features is useful at exploring the presence of sustainability inclinations of each house, the overall impact of building features in each of the five SBCs are unknown. Therefore, this paper explored the possibility of quantifying the impact of building features categorized in SBC1 – “Buildings will have to adapt to the new environment and restore damaged ecology while mitigating resource use" based on existing GH assessment tools and methods and other literature. This process as reported in this paper could lead to a new dimension in green home rating and assessment methods.

Keywords: Green homes, Malaysia, Sustainable BuildingCriteria, Sustainable homes

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Authors: Khalid Elasnaoui, Brahim Aksasse, Mohammed Ouanan

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In this paper, we are interested in the problem of finding similar images in a large database. For this purpose we propose a new algorithm based on a combination of the 2-D histogram intersection in the HSV space and statistical moments. The proposed histogram is based on a 3x3 window and not only on the intensity of the pixel. This approach overcome the drawback of the conventional 1-D histogram which is ignoring the spatial distribution of pixels in the image, while the statistical moments are used to escape the effects of the discretisation of the color space which is intrinsic to the use of histograms. We compare the performance of our new algorithm to various methods of the state of the art and we show that it has several advantages. It is fast, consumes little memory and requires no learning. To validate our results, we apply this algorithm to search for similar images in different image databases.

Keywords: 2-D histogram, Statistical moments, Indexing, Similarity distance, Histograms intersection.

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Authors: Zafer Ceylan, Gülgün F. Unal Sengor, Onur Gönülal

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The differences on proximate composition, sensorial analysis (for raw and cooked samples) and flesh productivity of the samples of Parapenaus longirostris that were caught in the North Aegean Sea and Marmara Sea were investigated. Moisture, protein, lipid, ash, carbohydrate, energy content of the North Aegean Sea shrimp were found 74.92 ± 0.1, 20.32 ± 0.16, 2.55 ± 0.1, 2.13 ± 0.08, 0.08%, 110.1 kcal/100 g, respectively. On the other hand, the Marmara Sea shrimp was found 76.9 ± 0.02, 19.06 ± 0.03, 2.22 ± 0.08, 1.51 ± 0.04, 0.33, 102.77 kcal/100g, respectively. Protein, lipid, ash and energy values of the Northern Aegean Sea shrimp were higher than the Marmara Sea shrimp. On the other hand, moisture, carbohydrate values of the Northern Aegean Sea shrimp were lower than the Marmara samples. Sensorial analyses were carried on for raw and cooked samples. Among all properties for raw samples, flesh color, shrimp connective tissue, shrimp body parameters were different from each other according to the result of the panel. According to the result of the cooked shrimp samples among all properties, cooked odour, flavor and texture were different from each other as well. Especially, flavor and textural properties of cooked shrimps of the Northern Aegean Sea were higher than the Marmara Sea shrimp. The flesh yield of the Northern Aegean Sea shrimp was found 46.42%, while Marmara Sea shrimp was found 47.74%.

Keywords: Proximate value, sensorial evaluation, Parapenaus longirostris flesh yield.

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Authors: Y. H. Tsai, Y. W. Chen

Abstract:

Facial features are frequently used to represent local properties of a human face image in computer vision applications. In this paper, we present a fast algorithm that can extract the facial features online such that they can give a satisfying representation of a face image. It includes one step for a coarse detection of each facial feature by AdaBoost and another one to increase the accuracy of the found points by Active Shape Models (ASM) in the regions of interest. The resulted facial features are evaluated by matching with artificial face models in the applications of physiognomy. The distance measure between the features and those in the fate models from the database is carried out by means of the Hausdorff distance. In the experiment, the proposed method shows the efficient performance in facial feature extractions and online system of physiognomy.

Keywords: Facial feature extraction, AdaBoost, Active shapemodel, Hausdorff distance

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Authors: M. L. Anitha, K. A. Radhakrishna Rao

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With the advent of modern computing technology, there is an increased demand for developing recognition systems that have the capability of verifying the identity of individuals. Recognition systems are required by several civilian and commercial applications for providing access to secured resources. Traditional recognition systems which are based on physical identities are not sufficiently reliable to satisfy the security requirements due to the use of several advances of forgery and identity impersonation methods. Recognizing individuals based on his/her unique physiological characteristics known as biometric traits is a reliable technique, since these traits are not transferable and they cannot be stolen or lost. Since the performance of biometric based recognition system depends on the particular trait that is utilized, the present work proposes a fusion approach which combines Inner knuckle print (IKP) trait of the middle, ring and index fingers with the geometrical features of hand. The hand image captured from a digital camera is preprocessed to find finger IKP as region of interest (ROI) and hand geometry features. Geometrical features are represented as the distances between different key points and IKP features are extracted by applying local binary pattern descriptor on the IKP ROI. The decision level AND fusion was adopted, which has shown improvement in performance of the combined scheme. The proposed approach is tested on the database collected at our institute. Proposed approach is of significance since both hand geometry and IKP features can be extracted from the palm region of the hand. The fusion of these features yields a false acceptance rate of 0.75%, false rejection rate of 0.86% for verification tests conducted, which is less when compared to the results obtained using individual traits. The results obtained confirm the usefulness of proposed approach and suitability of the selected features for developing biometric based recognition system based on features from palmar region of hand.

Keywords: Biometrics, hand geometry features, inner knuckle print, recognition.

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Authors: Soo- Young Ye, Ki-Gon Nam, Ki-Won Byun

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In this paper, we were introduces a skin detection method using a histogram approximation based on the mean shift algorithm. The proposed method applies the mean shift procedure to a histogram of a skin map of the input image, generated by comparison with standard skin colors in the CbCr color space, and divides the background from the skin region by selecting the maximum value according to brightness level. The proposed method detects the skin region using the mean shift procedure to determine a maximum value that becomes the dividing point, rather than using a manually selected threshold value, as in existing techniques. Even when skin color is contaminated by illumination, the procedure can accurately segment the skin region and the background region. The proposed method may be useful in detecting facial regions as a pretreatment for face recognition in various types of illumination.

Keywords: Skin region detection, mean shift, histogram approximation.

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Authors: Yongsuk Jang Kim, Sun-Tae Chung, Boogyun Kim, Seongwon Cho

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Realistic 3D face model is more precise in representing pose, illumination, and expression of face than 2D face model so that it can be utilized usefully in various applications such as face recognition, games, avatars, animations, and etc. In this paper, we propose a 3D face modeling method based on 3D dense morphable shape model. The proposed 3D modeling method first constructs a 3D dense morphable shape model from 3D face scan data obtained using a 3D scanner. Next, the proposed method extracts and matches facial landmarks from 2D image sequence containing a face to be modeled, and then reconstructs 3D vertices coordinates of the landmarks using a factorization-based SfM technique. Then, the proposed method obtains a 3D dense shape model of the face to be modeled by fitting the constructed 3D dense morphable shape model into the reconstructed 3D vertices. Also, the proposed method makes a cylindrical texture map using 2D face image sequence. Finally, the proposed method generates a 3D face model by rendering the 3D dense face shape model using the cylindrical texture map. Through building processes of 3D face model by the proposed method, it is shown that the proposed method is relatively easy, fast and precise.

Keywords: 3D Face Modeling, 3D Morphable Shape Model, 3DReconstruction, 3D Correspondence.

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Zuriati Ismail, Naomie Salim

Abstract:

Seeking and sharing knowledge on online forums have made them popular in recent years. Although online forums are valuable sources of information, due to variety of sources of messages, retrieving reliable threads with high quality content is an issue. Majority of the existing information retrieval systems ignore the quality of retrieved documents, particularly, in the field of thread retrieval. In this research, we present an approach that employs various quality features in order to investigate the quality of retrieved threads. Different aspects of content quality, including completeness, comprehensiveness, and politeness, are assessed using these features, which lead to finding not only textual, but also conceptual relevant threads for a user query within a forum. To analyse the influence of the features, we used an adopted version of voting model thread search as a retrieval system. We equipped it with each feature solely and also various combinations of features in turn during multiple runs. The results show that incorporating the quality features enhances the effectiveness of the utilised retrieval system significantly.

Keywords: Content quality, Forum search, Thread retrieval, Voting techniques.

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Authors: Steven Whittle, Ingrida Valiusaityte

Abstract:

Within the domain of Systems Engineering the need to perform property aggregation to understand, analyze and manage complex systems is unequivocal. This can be seen in numerous domains such as capability analysis, Mission Essential Competencies (MEC) and Critical Design Features (CDF). Furthermore, the need to consider uncertainty propagation as well as the sensitivity of related properties within such analysis is equally as important when determining a set of critical properties within such a system. This paper describes this property breakdown in a number of domains within Systems Engineering and, within the area of CDFs, emphasizes the importance of uncertainty analysis. As part of this, a section of the paper describes possible techniques which may be used within uncertainty propagation and in conclusion an example is described utilizing one of the techniques for property and uncertainty aggregation within an aircraft system to aid the determination of Critical Design Features.

Keywords: Complex Systems, Critical Design Features, Property Aggregation, Uncertainty.

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Authors: Satish Kumar

Abstract:

The feature extraction method(s) used to recognize hand-printed characters play an important role in ICR applications. In order to achieve high recognition rate for a recognition system, the choice of a feature that suits for the given script is certainly an important task. Even if a new feature required to be designed for a given script, it is essential to know the recognition ability of the existing features for that script. Devanagari script is being used in various Indian languages besides Hindi the mother tongue of majority of Indians. This research examines a variety of feature extraction approaches, which have been used in various ICR/OCR applications, in context to Devanagari hand-printed script. The study is conducted theoretically and experimentally on more that 10 feature extraction methods. The various feature extraction methods have been evaluated on Devanagari hand-printed database comprising more than 25000 characters belonging to 43 alphabets. The recognition ability of the features have been evaluated using three classifiers i.e. k-NN, MLP and SVM.

Keywords: Features, Hand-printed, Devanagari, Classifier, Database

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Authors: Yi-an Cui, Lu Wang, Jian-ping Xiao

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This paper presents an automatic feature recognition method based on center-surround difference detecting and fuzzy logic that can be applied in ground-penetrating radar (GPR) image processing. Adopted center-surround difference method, the salient local image regions are extracted from the GPR images as features of detected objects. And fuzzy logic strategy is used to match the detected features and features in template database. This way, the problem of objects detecting, which is the key problem in GPR image processing, can be converted into two steps, feature extracting and matching. The contributions of these skills make the system have the ability to deal with changes in scale, antenna and noises. The results of experiments also prove that the system has higher ratio of features sensing in using GPR to image the subsurface structures.

Keywords: feature recognition, GPR image, matching strategy, salient image

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Authors: F. Rotaru, S. Bejinariu, C. D. Niţâ, R. Luca, I. Pâvâloi, C. Lazâr

Abstract:

The paper presents a technique suitable in robot vision applications where it is not possible to establish the object position from one view. Usually, one view pose calculation methods are based on the correspondence of image features established at a training step and exactly the same image features extracted at the execution step, for a different object pose. When such a correspondence is not feasible because of the lack of specific features a new method is proposed. In the first step the method computes from two views the 3D pose of feature points. Subsequently, using a registration algorithm, the set of 3D feature points extracted at the execution phase is aligned with the set of 3D feature points extracted at the training phase. The result is a Euclidean transform which have to be used by robot head for reorientation at execution step.

Keywords: features correspondence, registration algorithm, robot vision, triangulation method.

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Authors: Wei-Chih Tang, Shih-Wei Lu, Chih-Mong Tsai, Cheng-Yan Kao, Hsiu-Hui Lee

Abstract:

Previously, harmonic parameters (HPs) have been selected as features extracted from EEG signals for automatic sleep scoring. However, in previous studies, only one HP parameter was used, which were directly extracted from the whole epoch of EEG signal. In this study, two different transformations were applied to extract HPs from EEG signals: Hilbert-Huang transform (HHT) and wavelet transform (WT). EEG signals are decomposed by the two transformations; and features were extracted from different components. Twelve parameters (four sets of HPs) were extracted. Some of the parameters are highly diverse among different stages. Afterward, HPs from two transformations were used to building a rough sleep stages scoring model using the classifier SVM. The performance of this model is about 78% using the features obtained by our proposed extractions. Our results suggest that these features may be useful for automatic sleep stages scoring.

Keywords: EEG, harmonic parameter, Hilbert-Huang transform, sleep stages, wavelet transform.

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Authors: Elena Lyakso, Olga Frolova, Yuri Matveev

Abstract:

The aim of the study is to determine the acoustic features of voice and speech of children with autism spectrum disorders (ASD) as a possible additional diagnostic criterion. The participants in the study were 95 children with ASD aged 5-16 years, 150 typically development (TD) children, and 103 adults – listening to children’s speech samples. Three types of experimental methods for speech analysis were performed: spectrographic, perceptual by listeners, and automatic recognition. In the speech of children with ASD, the pitch values, pitch range, values of frequency and intensity of the third formant (emotional) leading to the “atypical” spectrogram of vowels are higher than corresponding parameters in the speech of TD children. High values of vowel articulation index (VAI) are specific for ASD children’s speech signals. These acoustic features can be considered as diagnostic marker of autism. The ability of humans and automatic recognition of the psychoneurological state of children via their speech is determined.

Keywords: Autism spectrum disorders, biomarker of autism, child speech, voice features.

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