Search results for: path recognition

Authors: Banashree N. P., Andhe Dharani, R. Vasanta, P. S. Satyanarayana

Abstract:

The applications on numbers are across-the-board that there is much scope for study. The chic of writing numbers is diverse and comes in a variety of form, size and fonts. Identification of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], machine printed or handwritten characters/numerals are recognized. There are plentiful approaches that deal with problem of detection of numerals/character depending on the sort of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent our work focused on a technique in feature extraction i.e. Local-based approach, a method using 16-segment display concept, which is extracted from halftoned images & Binary images of isolated numerals. These feature vectors are fed to neural classifier model that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. Experimentation result shows that recognition rate of halftoned images is 98 % compared to binary images (95%).

Keywords: OCR, Halftoning, Neural classifier, 16-segmentdisplay concept.

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Authors: Abhijit Mitra, Bhargav Kumar Mitra, Biswajoy Chatterjee

Abstract:

We present a novel scheme to recognize isolated speech signals using certain statistical parameters derived from those signals. The determination of the statistical estimates is based on extracted signal information rather than the original signal information in order to reduce the computational complexity. Subtle details of these estimates, after extracting the speech signal from ambience noise, are first exploited to segregate the polysyllabic words from the monosyllabic ones. Precise recognition of each distinct word is then carried out by analyzing the histogram, obtained from these information.

Keywords: Isolated speech signals, Block overlapping technique, Positive peaks, Histogram analysis.

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Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: Edge network, embedded network, MMA, matrix multiplication accelerator and semantic segmentation network.

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Authors: Tossaporn Kachanubal, Somkait Udomhunsakul

Abstract:

In this paper, we proposed a method to classify each type of natural rock texture. Our goal is to classify 26 classes of rock textures. First, we extract five features of each class by using principle component analysis combining with the use of applied spatial frequency measurement. Next, the effective node number of neural network was tested. We used the most effective neural network in classification process. The results from this system yield quite high in recognition rate. It is shown that high recognition rate can be achieved in separation of 26 stone classes.

Keywords: Texture classification, SFM, neural network, rock texture classification.

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Authors: Jan Flusser

Abstract:

This paper aims to present a survey of object recognition/classification methods based on image moments. We review various types of moments (geometric moments, complex moments) and moment-based invariants with respect to various image degradations and distortions (rotation, scaling, affine transform, image blurring, etc.) which can be used as shape descriptors for classification. We explain a general theory how to construct these invariants and show also a few of them in explicit forms. We review efficient numerical algorithms that can be used for moment computation and demonstrate practical examples of using moment invariants in real applications.

Keywords: Object recognition, degraded images, moments, moment invariants, geometric invariants, invariants to convolution, moment computation.

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Authors: Akshit Singhal, Xuan Wang, Zhijun Wang, Hao Che, Hong Jiang

Abstract:

Multiple Path Transmission Control Protocols (MPTCPs) allow flows to explore path diversity to improve the throughput, reliability and network resource utilization. However, the existing solutions may discourage users to adopt the solutions in the face of multipath scenario where different paths are charged based on different pricing structures, e.g., WiFi vs. cellular connections, widely available for mobile phones. In this paper, we propose a Hybrid MPTCP (H-MPTCP) with a built-in mechanism to incentivize users to use multiple paths with different pricing structures. In the meantime, H-MPTCP preserves the nice properties enjoyed by the state-of-the-art MPTCP solutions. Extensive real Linux implementation results verify that H-MPTCP can indeed achieve the design objectives.

Keywords: Congestion control, Network Utility Maximization, Multipath TCP, network.

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Authors: Girija Chetty, Monica Singh

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In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..

Keywords: Biometrics, gait recognition, PCA, LDA, Eigenface, Fisherface, Multivariate Gaussian Classifier

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Authors: Pierre-Alain Moëllic, Patrick Hède, Gr egory Grefenstette, Christophe Millet

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Pattern recognition and image recognition methods are commonly developed and tested using testbeds, which contain known responses to a query set. Until now, testbeds available for image analysis and content-based image retrieval (CBIR) have been scarce and small-scale. Here we present the one million images CEA-List Image Collection (CLIC) testbed that we have produced, and report on our use of this testbed to evaluate image analysis merging techniques. This testbed will soon be made publicly available through the EU MUSCLE Network of Excellence.

Keywords: CBIR, CLIC, evaluation, image indexing and retrieval, testbed.

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Authors: Yuan L. Lai, Jian H. Chen, Jui P. Hung

Abstract:

Owning to the high-speed feed rate and ultra spindle speed have been used in modern machine tools, the tool-path generation plays a key role in the successful application of a High-Speed Machining (HSM) system. Because of its importance in both high-speed machining and tool-path generation, approximating a contour by NURBS format is a potential function in CAD/CAM/CNC systems. It is much more convenient to represent an ellipse by parametric form than to connect points laboriously determined in a CNC system. A new approximating method based on optimum processes and NURBS curves of any degree to the ellipses is presented in this study. Such operations can be the foundation of tool-radius compensation interpolator of NURBS curves in CNC system. All operating processes for a CAD tool is presented and demonstrated by practical models.

Keywords: Ellipse, Approximation, NURBS, Optimum.

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Authors: Samppa Suoniemi, Harri Terho, Rami Olkkonen

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In recent methodological articles related to structural equation modeling (SEM), the question of how to measure endogenous formative variables has been raised as an urgent, unresolved issue. This research presents an empirical application from the CRM system development context to test a recently developed technique, which makes it possible to measure endogenous formative constructs in structural models. PLS path modeling is used to demonstrate the feasibility of measuring antecedent relationships at the formative indicator level, not the formative construct level. Empirical results show that this technique is a promising approach to measure antecedent relationships of formative constructs in SEM.

Keywords: CRM system development, formative measures, PLS path modeling, research methodology.

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Authors: Seo-Hee Kim, Kee-Won Kim, Seung-Hoon Kim

Abstract:

The most important elements of a novel are the characters, events and background. The background represents the time, place and situation that character appears, and conveys event and atmosphere more realistically. If readers have the proper knowledge about background of novels, it may be helpful for understanding the atmosphere of a novel and choosing a novel that readers want to read. In this paper, we are targeting Korean historical novels because spatio-temporal background especially performs an important role in historical novels among the genre of Korean novels. To the best of our knowledge, we could not find previous study that was aimed at Korean novels. In this paper, we build a Korean historical national dictionary. Our dictionary has historical places and temple names of kings over many generations as well as currently existing spatial words or temporal words in Korean history. We also present a method for recognizing spatio-temporal background based on patterns of phrasal words in Korean sentences. Our rules utilize postposition for spatial background recognition and temple names for temporal background recognition. The knowledge of the recognized background can help readers to understand the flow of events and atmosphere, and can use to visualize the elements of novels.

Keywords: Data mining, Korean historical novels, Korean linguistic feature, spatio-temporal background.

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

Abstract:

Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: Computing methodologies, interest point, salient region detections, image segmentation.

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

Abstract:

In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

Keywords: Iot, activity recognition, automatic classification, unconstrained environment, deep neural networks.

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Authors: Rachel C. F. Sun

Abstract:

This study examined the predictive effects of moral competence, prosocial norms and positive behavior recognition on school misbehavior among Chinese junior secondary school students. Results of multiple regression analysis showed that students were more likely to misbehave in school when they had lower levels of moral competence and prosocial norms, and when they perceived their positive behavior being less likely recognized. Practical implications were discussed on how to guide students to make the right choices to behave appropriately in school. Implications for future research were also discussed.

Keywords: Moral competence, positive behavior recognition, prosocial norms, school misbehavior.

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Authors: Nidhi Gupta, Sanjoy Das, Khushal Singh

Abstract:

Mobile Ad hoc Network is a set of self-governing nodes which communicate through wireless links. Dynamic topology MANETs makes routing a challenging task. Various routing protocols are there, but due to various fundamental characteristic open medium, changing topology, distributed collaboration and constrained capability, these protocols are tend to various types of security attacks. Black hole is one among them. In this attack, malicious node represents itself as having the shortest path to the destination but that path not even exists. In this paper, we aim to develop a routing protocol for detection and prevention of black hole attack by modifying AODV routing protocol. This protocol is able to detect and prevent the black hole attack. Simulation is done using NS-2, which shows the improvement in network performance.

Keywords: Ad hoc, AODV, Back Bone, routing, Security.

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Authors: Mangesh S. Deshpande, Raghunath S. Holambe

Abstract:

Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable for speaker features that are located in high frequency regions. The speaker individual information, which is non-uniformly distributed in the high frequencies, is equally important for speaker recognition. Based on this fact we proposed an admissible wavelet packet based filter structure for speaker identification. Multiresolution capabilities of wavelet packet transform are used to derive the new features. The proposed scheme differs from previous wavelet based works, mainly in designing the filter structure. Unlike others, the proposed filter structure does not follow Mel scale. The closed-set speaker identification experiments performed on the TIMIT database shows improved identification performance compared to other commonly used Mel scale based filter structures using wavelets.

Keywords: Speaker identification, Wavelet transform, Feature extraction, MFCC, GMM.

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Authors: Alexander N. Savostyanov, Tatiana A. Dolgorukova, Elena A. Esipenko, Mikhail S. Zaleshin, Margherita Malanchini, Anna V. Budakova, Alexander E. Saprygin, Tatiana A. Golovko, Yulia V. Kovas

Abstract:

EEG correlates of mathematical and trait anxiety level were studied in 52 healthy Russian-speakers during execution of error-recognition tasks with lexical, arithmetic and algebraic conditions. Event-related spectral perturbations were used as a measure of brain activity. The ERSP plots revealed alpha/beta desynchronizations within a 500-3000 ms interval after task onset and slow-wave synchronization within an interval of 150-350 ms. Amplitudes of these intervals reflected the accuracy of error recognition, and were differently associated with the three conditions. The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16- 20 Hz) frequency bands. In theta band the effects of mathematical anxiety were stronger expressed in lexical, than in arithmetic and algebraic condition. The mathematical anxiety effects in theta band were associated with differences between anterior and posterior cortical areas, whereas the effects of trait anxiety were associated with inter-hemispherical differences. In beta1 and beta2 bands effects of trait and mathematical anxiety were directed oppositely. The trait anxiety was associated with increase of amplitude of desynchronization, whereas the mathematical anxiety was associated with decrease of this amplitude. The effect of mathematical anxiety in beta2 band was insignificant for lexical condition but was the strongest in algebraic condition. EEG correlates of anxiety in theta band could be interpreted as indexes of task emotionality, whereas the reaction in beta2 band is related to tension of intellectual resources.

Keywords: EEG, brain activity, lexical and numerical error-recognition tasks, mathematical and trait anxiety.

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Authors: S. Maheswari, P. Arockia Jansi Rani

Abstract:

Human action is recognized directly from the video sequences. The objective of this work is to recognize various human actions like run, jump, walk etc. Human action recognition requires some prior knowledge about actions namely, the motion estimation, foreground and background estimation. Region of interest (ROI) is extracted to identify the human in the frame. Then, optical flow technique is used to extract the motion vectors. Using the extracted features similarity measure based classification is done to recognize the action. From experimentations upon the Weizmann database, it is found that the proposed method offers a high accuracy.

Keywords: Background subtraction, human silhouette, optical flow, classification.

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Authors: Ahmad M. Sarhan, Omar I. Al Helalat

Abstract:

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.

Keywords: ANN, Backpropagation, Gaussian, LMS, MSE, Neuron, standard deviation, Widrow-Hoff rule.

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Authors: Massoud Masoumi, Hosseyn Mahdizadeh

Abstract:

A new and highly efficient architecture for elliptic curve scalar point multiplication which is optimized for a binary field recommended by NIST and is well-suited for elliptic curve cryptographic (ECC) applications is presented. To achieve the maximum architectural and timing improvements we have reorganized and reordered the critical path of the Lopez-Dahab scalar point multiplication architecture such that logic structures are implemented in parallel and operations in the critical path are diverted to noncritical paths. With G=41, the proposed design is capable of performing a field multiplication over the extension field with degree 163 in 11.92 s with the maximum achievable frequency of 251 MHz on Xilinx Virtex-4 (XC4VLX200) while 22% of the chip area is occupied, where G is the digit size of the underlying digit-serial finite field multiplier.

Keywords: Elliptic curve cryptography, FPGA implementation, scalar point multiplication.

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Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, thalassemia.

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Authors: G. Dimauro, S. Impedovo, M.G. Lucchese, R. Modugno, G. Pirlo

Abstract:

This paper presents the analysis of similarity between local decisions, in the process of alphanumeric hand-prints classification. From the analysis of local characteristics of handprinted numerals and characters, extracted by a zoning method, the set of classification decisions is obtained and the similarity among them is investigated. For this purpose the Similarity Index is used, which is an estimator of similarity between classifiers, based on the analysis of agreements between their decisions. The experimental tests, carried out using numerals and characters from the CEDAR and ETL database, respectively, show to what extent different parts of the patterns provide similar classification decisions.

Keywords: Handwriting Recognition, Optical Character Recognition, Similarity Index, Zoning.

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Authors: Kexin Jia, Youxin Lu

Abstract:

To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).

Keywords: Modulation classification, combined classifier, fuzzy neural network, interclass distance.

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Authors: Jafreezal Jaafar, Eric McKenzie

Abstract:

This paper presents a solution for the behavioural animation of autonomous virtual agent navigation in virtual environments. We focus on using Dempster-Shafer-s Theory of Evidence in developing visual sensor for virtual agent. The role of the visual sensor is to capture the information about the virtual environment or identifie which part of an obstacle can be seen from the position of the virtual agent. This information is require for vitual agent to coordinate navigation in virtual environment. The virual agent uses fuzzy controller as a navigation system and Fuzzy α - level for the action selection method. The result clearly demonstrates the path produced is reasonably smooth even though there is some sharp turn and also still not diverted too far from the potential shortest path. This had indicated the benefit of our method, where more reliable and accurate paths produced during navigation task.

Keywords: Agent, navigation, Dempster Shafer, fuzzy logic.

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Authors: A.K.M. Khaled Ahsan Talukder, Taibun Nessa, Kaushik Roy

Abstract:

The weight constrained shortest path problem (WCSPP) is one of most several known basic problems in combinatorial optimization. Because of its importance in many areas of applications such as computer science, engineering and operations research, many researchers have extensively studied the WCSPP. This paper mainly concentrates on the reduction of total search space for finding WCSP using some existing Genetic Algorithm (GA). For this purpose, some controlled schemes of genetic operators are adopted on list chromosome representation. This approach gives a near optimum solution with smaller elapsed generation than classical GA technique. From further analysis on the matter, a new generalized schema theorem is also developed from the philosophy of Holland-s theorem.

Keywords: Genetic Algorithm, Evolutionary Optimization, Multi Objective Optimization, Non-linear Schema Theorem, WCSPP.

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Authors: Meysam Mohamad pour, Fardad Farokhi

Abstract:

In this paper in consideration of each available techniques deficiencies for speech recognition, an advanced method is presented that-s able to classify speech signals with the high accuracy (98%) at the minimum time. In the presented method, first, the recorded signal is preprocessed that this section includes denoising with Mels Frequency Cepstral Analysis and feature extraction using discrete wavelet transform (DWT) coefficients; Then these features are fed to Multilayer Perceptron (MLP) network for classification. Finally, after training of neural network effective features are selected with UTA algorithm.

Keywords: Multilayer perceptron (MLP) neural network, Discrete Wavelet Transform (DWT) , Mels Scale Frequency Filter , UTA algorithm.

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Authors: Agus Harjoko, Sri Hartati, Henry Dwiyasa

Abstract:

There have been numerous implementations of security system using biometric, especially for identification and verification cases. An example of pattern used in biometric is the iris pattern in human eye. The iris pattern is considered unique for each person. The use of iris pattern poses problems in encoding the human iris. In this research, an efficient iris recognition method is proposed. In the proposed method the iris segmentation is based on the observation that the pupil has lower intensity than the iris, and the iris has lower intensity than the sclera. By detecting the boundary between the pupil and the iris and the boundary between the iris and the sclera, the iris area can be separated from pupil and sclera. A step is taken to reduce the effect of eyelashes and specular reflection of pupil. Then the four levels Coiflet wavelet transform is applied to the extracted iris image. The modified Hamming distance is employed to measure the similarity between two irises. This research yields the identification success rate of 84.25% for the CASIA version 1.0 database. The method gives an accuracy of 77.78% for the left eyes of MMU 1 database and 86.67% for the right eyes. The time required for the encoding process, from the segmentation until the iris code is generated, is 0.7096 seconds. These results show that the accuracy and speed of the method is better than many other methods.

Keywords: Biometric, iris recognition, wavelet transform.

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

Abstract:

Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: Human Motion Recognition, Motion representation, Laban Movement Analysis, Discrete Hidden Markov Model.

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Authors: Iman A. Saad, Loay E. George

Abstract:

Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: Iris recognition, contrast stretching, gradient features, texture features, Euclidean metric.

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Authors: S.L. Loh, S. Salleh, N.H. Sarmin

Abstract:

A spanning tree of a connected graph is a tree which consists the set of vertices and some or perhaps all of the edges from the connected graph. In this paper, a model for spanning tree transformation of connected graphs into single-row networks, namely Spanning Tree of Connected Graph Modeling (STCGM) will be introduced. Path-Growing Tree-Forming algorithm applied with Vertex-Prioritized is contained in the model to produce the spanning tree from the connected graph. Paths are produced by Path-Growing and they are combined into a spanning tree by Tree-Forming. The spanning tree that is produced from the connected graph is then transformed into single-row network using Tree Sequence Modeling (TSM). Finally, the single-row routing problem is solved using a method called Enhanced Simulated Annealing for Single-Row Routing (ESSR).

Keywords: Graph theory, simulated annealing, single-rowrouting and spanning tree.

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