Search results for: scene text

Authors: G.P. Nguyen, H.J. Andersen

Abstract:

Building intelligent traffic guide systems has been an interesting subject recently. A good system should be able to observe all important visual information to be able to analyze the context of the scene. To do so, signs in general, and traffic signs in particular, are usually taken into account as they contain rich information to these systems. Therefore, many researchers have put an effort on sign recognition field. Sign localization or sign detection is the most important step in the sign recognition process. This step filters out non informative area in the scene, and locates candidates in later steps. In this paper, we apply a new approach in detecting sign locations using a new color invariant model. Experiments are carried out with different datasets introduced in other works where authors claimed the difficulty in detecting signs under unfavorable imaging conditions. Our method is simple, fast and most importantly it gives a high detection rate in locating signs.

Keywords: Sign localization, color-based segmentation.

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Authors: Liping Jing, Michael K. Ng, Xinhua Yang, Joshua Zhexue Huang

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This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.

Keywords: Subspace Clustering, Text Mining, Feature Weighting, Cluster Interpretation, Ontology

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Authors: Mahmoud Moh'd Mhashi

Abstract:

A new algorithm called Character-Comparison to Character-Access (CCCA) is developed to test the effect of both: 1) converting character-comparison and number-comparison into character-access and 2) the starting point of checking on the performance of the checking operation in string searching. An experiment is performed using both English text and DNA text with different sizes. The results are compared with five algorithms, namely, Naive, BM, Inf_Suf_Pref, Raita, and Cycle. With the CCCA algorithm, the results suggest that the evaluation criteria of the average number of total comparisons are improved up to 35%. Furthermore, the results suggest that the clock time required by the other algorithms is improved in range from 22.13% to 42.33% by the new CCCA algorithm.

Keywords: Pattern matching, string searching, charactercomparison, character-access, text type, and checking

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Authors: Hany Mahgoub, Dietmar Rösner, Nabil Ismail, Fawzy Torkey

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This paper describes text mining technique for automatically extracting association rules from collections of textual documents. The technique called, Extracting Association Rules from Text (EART). It depends on keyword features for discover association rules amongst keywords labeling the documents. In this work, the EART system ignores the order in which the words occur, but instead focusing on the words and their statistical distributions in documents. The main contributions of the technique are that it integrates XML technology with Information Retrieval scheme (TFIDF) (for keyword/feature selection that automatically selects the most discriminative keywords for use in association rules generation) and use Data Mining technique for association rules discovery. It consists of three phases: Text Preprocessing phase (transformation, filtration, stemming and indexing of the documents), Association Rule Mining (ARM) phase (applying our designed algorithm for Generating Association Rules based on Weighting scheme GARW) and Visualization phase (visualization of results). Experiments applied on WebPages news documents related to the outbreak of the bird flu disease. The extracted association rules contain important features and describe the informative news included in the documents collection. The performance of the EART system compared with another system that uses the Apriori algorithm throughout the execution time and evaluating extracted association rules.

Keywords: Text mining, data mining, association rule mining

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Authors: M. Govindarajan

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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In decision tree approach is most useful in classification problem. With this technique, tree is constructed to model the classification process. There are two basic steps in the technique: building the tree and applying the tree to the database. This paper describes a proposed C5.0 classifier that performs rulesets, cross validation and boosting for original C5.0 in order to reduce the optimization of error ratio. The feasibility and the benefits of the proposed approach are demonstrated by means of medial data set like hypothyroid. It is shown that, the performance of a classifier on the training cases from which it was constructed gives a poor estimate by sampling or using a separate test file, either way, the classifier is evaluated on cases that were not used to build and evaluate the classifier are both are large. If the cases in hypothyroid.data and hypothyroid.test were to be shuffled and divided into a new 2772 case training set and a 1000 case test set, C5.0 might construct a different classifier with a lower or higher error rate on the test cases. An important feature of see5 is its ability to classifiers called rulesets. The ruleset has an error rate 0.5 % on the test cases. The standard errors of the means provide an estimate of the variability of results. One way to get a more reliable estimate of predictive is by f-fold –cross- validation. The error rate of a classifier produced from all the cases is estimated as the ratio of the total number of errors on the hold-out cases to the total number of cases. The Boost option with x trials instructs See5 to construct up to x classifiers in this manner. Trials over numerous datasets, large and small, show that on average 10-classifier boosting reduces the error rate for test cases by about 25%.

Keywords: C5.0, Error Ratio, text mining, training data, test data.

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Authors: K. Kraus, M. Uiberacker, O. Martikainen, R. Reda

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One of the major, difficult tasks in automated video surveillance is the segmentation of relevant objects in the scene. Current implementations often yield inconsistent results on average from frame to frame when trying to differentiate partly occluding objects. This paper presents an efficient block-based segmentation algorithm which is capable of separating partly occluding objects and detecting shadows. It has been proven to perform in real time with a maximum duration of 47.48 ms per frame (for 8x8 blocks on a 720x576 image) with a true positive rate of 89.2%. The flexible structure of the algorithm enables adaptations and improvements with little effort. Most of the parameters correspond to relative differences between quantities extracted from the image and should therefore not depend on scene and lighting conditions. Thus presenting a performance oriented segmentation algorithm which is applicable in all critical real time scenarios.

Keywords: Image segmentation, Model-based, Region growing, Blob Analysis, Occlusion, Shadow detection, Intelligent videosurveillance.

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Authors: Nora Taleb, Sellami Mokhtar, Michel Simonet

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This work concerns the evolution and the maintenance of an ontological resource in relation with the evolution of the corpus of texts from which it had been built. The knowledge forming a text corpus, especially in dynamic domains, is in continuous evolution. When a change in the corpus occurs, the domain ontology must evolve accordingly. Most methods manage ontology evolution independently from the corpus from which it is built; in addition, they treat evolution just as a process of knowledge addition, not considering other knowledge changes. We propose a methodology for managing an evolving ontology from a text corpus that evolves over time, while preserving the consistency and the persistence of this ontology. Our methodology is based on the changes made on the corpus to reflect the evolution of the considered domain - augmented surgery in our case. In this context, the results of text mining techniques, as well as the ARCHONTE method slightly modified, are used to support the evolution process.

Keywords: Corpus, Evolution, Ontology

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Authors: Y. Y. Chen, O. M. Foong, S. P. Yong, Kurniawan Iwan

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Information sharing and gathering are important in the rapid advancement era of technology. The existence of WWW has caused rapid growth of information explosion. Readers are overloaded with too many lengthy text documents in which they are more interested in shorter versions. Oil and gas industry could not escape from this predicament. In this paper, we develop an Automated Text Summarization System known as AutoTextSumm to extract the salient points of oil and gas drilling articles by incorporating statistical approach, keywords identification, synonym words and sentence-s position. In this study, we have conducted interviews with Petroleum Engineering experts and English Language experts to identify the list of most commonly used keywords in the oil and gas drilling domain. The system performance of AutoTextSumm is evaluated using the formulae of precision, recall and F-score. Based on the experimental results, AutoTextSumm has produced satisfactory performance with F-score of 0.81.

Keywords: Keyword's probability, synonym sets.

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Authors: K.R. Aida–Zade, C. Ardil, A.M. Sharifova

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In this paper, the main principles of text-to-speech synthesis system are presented. Associated problems which arise when developing speech synthesis system are described. Used approaches and their application in the speech synthesis systems for Azerbaijani language are shown.

Keywords: synthesis of Azerbaijani language, morphemes, phonemes, sounds, sentence, speech synthesizer, intonation, accent, pronunciation.

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Authors: Rozilawati Binti Dollah, Masaki Aono

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Classifying biomedical literature is a difficult and challenging task, especially when a large number of biomedical articles should be organized into a hierarchical structure. In this paper, we present an approach for classifying a collection of biomedical text abstracts downloaded from Medline database with the help of ontology alignment. To accomplish our goal, we construct two types of hierarchies, the OHSUMED disease hierarchy and the Medline abstract disease hierarchies from the OHSUMED dataset and the Medline abstracts, respectively. Then, we enrich the OHSUMED disease hierarchy before adapting it to ontology alignment process for finding probable concepts or categories. Subsequently, we compute the cosine similarity between the vector in probable concepts (in the “enriched" OHSUMED disease hierarchy) and the vector in Medline abstract disease hierarchies. Finally, we assign category to the new Medline abstracts based on the similarity score. The results obtained from the experiments show the performance of our proposed approach for hierarchical classification is slightly better than the performance of the multi-class flat classification.

Keywords: Biomedical literature, hierarchical text classification, ontology alignment, text mining.

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Authors: D. R. Awang Rambli, S. Sulaiman, M.Y. Nayan, A.R. Asoruddin

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Existing image-based virtual reality applications allow users to view image-based 3D virtual environment in a more interactive manner. User could “walkthrough"; looks left, right, up and down and even zoom into objects in these virtual worlds of images. However what the user sees during a “zoom in" is just a close-up view of the same image which was taken from a distant. Thus, this does not give the user an accurate view of the object from the actual distance. In this paper, a simple technique for zooming in an object in a virtual scene is presented. The technique is based on the 'hotspot' concept in existing application. Instead of navigation between two different locations, the hotspots are used to focus into an object in the scene. For each object, several hotspots are created. A different picture is taken for each hotspot. Each consecutive hotspot created will take the user closer to the object. This will provide the user with a correct of view of the object based on his proximity to the object. Implementation issues and the relevance of this technique in potential application areas are highlighted.

Keywords: Hotspots, image-based VR, camera zooms, virtualreality.

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Authors: Mária Csernoch, Piroska Biró

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The legends about “user-friendly” and “easy-to-use” birotical tools (computer-related office tools) have been spreading and misleading end-users. This approach has led us to the extremely high number of incorrect documents, causing serious financial losses in the creating, modifying, and retrieving processes. Our research proved that there are at least two sources of this underachievement: (1) The lack of the definition of the correctly edited, formatted documents. Consequently, end-users do not know whether their methods and results are correct or not. They are not aware of their ignorance. They are so ignorant that their ignorance does not allow them to realize their lack of knowledge. (2) The end-users’ problem solving methods. We have found that in non-traditional programming environments end-users apply, almost exclusively, surface approach metacognitive methods to carry out their computer related activities, which are proved less effective than deep approach methods. Based on these findings we have developed deep approach methods which are based on and adapted from traditional programming languages. In this study, we focus on the most popular type of birotical documents, the text based documents. We have provided the definition of the correctly edited text, and based on this definition, adapted the debugging method known in programming. According to the method, before the realization of text editing, a thorough debugging of already existing texts and the categorization of errors are carried out. With this method in advance to real text editing users learn the requirements of text based documents and also of the correctly formatted text. The method has been proved much more effective than the previously applied surface approach methods. The advantages of the method are that the real text handling requires much less human and computer sources than clicking aimlessly in the GUI (Graphical User Interface), and the data retrieval is much more effective than from error-prone documents.

Keywords: Deep approach metacognitive methods, error-prone birotical documents, financial losses, human and computer resources.

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Authors: Yudhajit Datta, Jonathan Bandi, Ankit Sethia, Hamsi Iyer

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Motion Tracking and Stereo Vision are complicated, albeit well-understood problems in computer vision. Existing softwares that combine the two approaches to perform stereo motion tracking typically employ complicated and computationally expensive procedures. The purpose of this study is to create a simple and effective solution capable of combining the two approaches. The study aims to explore a strategy to combine the two techniques of two-dimensional motion tracking using Kalman Filter; and depth detection of object using Stereo Vision. In conventional approaches objects in the scene of interest are observed using a single camera. However for Stereo Motion Tracking; the scene of interest is observed using video feeds from two calibrated cameras. Using two simultaneous measurements from the two cameras a calculation for the depth of the object from the plane containing the cameras is made. The approach attempts to capture the entire three-dimensional spatial information of each object at the scene and represent it through a software estimator object. In discrete intervals, the estimator tracks object motion in the plane parallel to plane containing cameras and updates the perpendicular distance value of the object from the plane containing the cameras as depth. The ability to efficiently track the motion of objects in three-dimensional space using a simplified approach could prove to be an indispensable tool in a variety of surveillance scenarios. The approach may find application from high security surveillance scenes such as premises of bank vaults, prisons or other detention facilities; to low cost applications in supermarkets and car parking lots.

Keywords: Kalman Filter, Stereo Vision, Motion Tracking, Matlab, Object Tracking, Camera Calibration, Computer Vision System Toolbox.

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Authors: Mohamed Abdel Fattah, Fuji Ren

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This work proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 English religious articles. The results of the proposed approach are promising.

Keywords: Automatic Summarization, Genetic Algorithm, Mathematical Regression, Text Features.

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Authors: Penn P. Wu

Abstract:

Recent advances in virtual reality (VR) technologies have made it possible for students to experience a virtual on-the-scene or virtual in-person observation of an educational event. In an experimental class, the author uses VR, particularly 360° videos, to virtually engage students in an event, through a wide spectrum of educational resources, such s a virtual “bystander.” Students were able to observe the event as if they were physically on site, although they could not intervene with the scene. The author will describe the adopted equipment, specification, and cost of building them as well as the quality of VR. The author will discuss (a) feasibility, effectiveness, and efficiency of using VR as a supplemental technology to teach college students and criteria and methodologies used by the authors to evaluate them; (b) barriers and issues of technological implementation; and (c) pedagogical practices learned through this experiment. The author also attempts to explore (a) how VR could provide an interactive virtual in-person learning experience; (b) how VR can possibly change traditional college education and online education; (c) how educators and balance six critical factors: cost, time, technology, quality, result, and content.

Keywords: Learning with VR, virtual experience of learning, virtual in-person learning, virtual reality for education.

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Authors: J. P. Dubois

Abstract:

Stochastic modeling of network traffic is an area of significant research activity for current and future broadband communication networks. Multimedia traffic is statistically characterized by a bursty variable bit rate (VBR) profile. In this paper, we develop an improved model for uniform activity level video sources in ATM using a doubly stochastic autoregressive model driven by an underlying spatial point process. We then examine a number of burstiness metrics such as the peak-to-average ratio (PAR), the temporal autocovariance function (ACF) and the traffic measurements histogram. We found that the former measure is most suitable for capturing the burstiness of single scene video traffic. In the last phase of this work, we analyse statistical multiplexing of several constant scene video sources. This proved, expectedly, to be advantageous with respect to reducing the burstiness of the traffic, as long as the sources are statistically independent. We observed that the burstiness was rapidly diminishing, with the largest gain occuring when only around 5 sources are multiplexed. The novel model used in this paper for characterizing uniform activity video was thus found to be an accurate model.

Keywords: AR, ATM, burstiness, doubly stochastic, statisticalmultiplexing.

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Authors: L. H. Chong, Y. Y. Chen

Abstract:

Information is increasing in volumes; companies are overloaded with information that they may lose track in getting the intended information. It is a time consuming task to scan through each of the lengthy document. A shorter version of the document which contains only the gist information is more favourable for most information seekers. Therefore, in this paper, we implement a text summarization system to produce a summary that contains gist information of oil and gas news articles. The summarization is intended to provide important information for oil and gas companies to monitor their competitor-s behaviour in enhancing them in formulating business strategies. The system integrated statistical approach with three underlying concepts: keyword occurrences, title of the news article and location of the sentence. The generated summaries were compared with human generated summaries from an oil and gas company. Precision and recall ratio are used to evaluate the accuracy of the generated summary. Based on the experimental results, the system is able to produce an effective summary with the average recall value of 83% at the compression rate of 25%.

Keywords: Information retrieval, text summarization, statistical approach.

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Authors: Imran Sarwar Bajwa

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Internet is one of the major sources of information for the person belonging to almost all the fields of life. Major language that is used to publish information on internet is language. This thing becomes a problem in a country like Pakistan, where Urdu is the national language. Only 10% of Pakistan mass can understand English. The reason is millions of people are deprived of precious information available on internet. This paper presents a system for translation from English to Urdu. A module LESSA is used that uses a rule based algorithm to read the input text in English language, understand it and translate it into Urdu language. The designed approach was further incorporated to translate the complete website from English language o Urdu language. An option appears in the browser to translate the webpage in a new window. The designed system will help the millions of users of internet to get benefit of the internet and approach the latest information and knowledge posted daily on internet.

Keywords: Natural Language Translation, Text Understanding, Knowledge extraction, Text Processing

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Authors: Subramaniam Chandran

Abstract:

The significance of psychology in studying politics is embedded in philosophical issues as well as behavioural pursuits. For the former is often associated with Sigmund Freud and his followers. The latter is inspired by the writings of Harold Lasswell. Political psychology or psychopolitics has its own impression on political thought ever since it deciphers the concept of human nature and political propaganda. More importantly, psychoanalysis views political thought as a textual content which needs to explore the latent from the manifest content. In other words, it reads the text symptomatically and interprets the hidden truth. This paper explains the paradigm of dream interpretation applied by Freud. The dream work is a process which has four successive activities: condensation, displacement, representation and secondary revision. The texts dealing with political though can also be interpreted on these principles. Freud's method of dream interpretation draws its source after the hermeneutic model of philological research. It provides theoretical perspective and technical rules for the interpretation of symbolic structures. The task of interpretation remains a discovery of equivalence of symbols and actions through perpetual analogies. Psychoanalysis can help in studying political thought in two ways: to study the text distortion, Freud's dream interpretation is used as a paradigm exploring the latent text from its manifest text; and to apply Freud's psychoanalytic concepts and theories ranging from individual mind to civilization, religion, war and politics.

Keywords: Psychoanalysis, political thought, dreaminterpretation, latent content, manifest content

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Authors: Othman O. Khalifa, Zakiah Hanim Ahmad, Teddy Surya Gunawan

Abstract:

This paper presents a rule-based text- to- speech (TTS) Synthesis System for Standard Malay, namely SMaTTS. The proposed system using sinusoidal method and some pre- recorded wave files in generating speech for the system. The use of phone database significantly decreases the amount of computer memory space used, thus making the system very light and embeddable. The overall system was comprised of two phases the Natural Language Processing (NLP) that consisted of the high-level processing of text analysis, phonetic analysis, text normalization and morphophonemic module. The module was designed specially for SM to overcome few problems in defining the rules for SM orthography system before it can be passed to the DSP module. The second phase is the Digital Signal Processing (DSP) which operated on the low-level process of the speech waveform generation. A developed an intelligible and adequately natural sounding formant-based speech synthesis system with a light and user-friendly Graphical User Interface (GUI) is introduced. A Standard Malay Language (SM) phoneme set and an inclusive set of phone database have been constructed carefully for this phone-based speech synthesizer. By applying the generative phonology, a comprehensive letter-to-sound (LTS) rules and a pronunciation lexicon have been invented for SMaTTS. As for the evaluation tests, a set of Diagnostic Rhyme Test (DRT) word list was compiled and several experiments have been performed to evaluate the quality of the synthesized speech by analyzing the Mean Opinion Score (MOS) obtained. The overall performance of the system as well as the room for improvements was thoroughly discussed.

Keywords: Natural Language Processing, Text-To-Speech (TTS), Diphone, source filter, low-/ high- level synthesis.

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Authors: Prabh Preet Singh, Harpreet Kaur

Abstract:

This paper presents a method for single image dehazing based on dark channel prior (DCP). The property that the intensity of the dark channel gives an approximate thickness of the haze is used to estimate the transmission and atmospheric light. Instead of constant atmospheric light, the proposed method employs scene depth to estimate spatially varying atmospheric light as it truly occurs in nature. Haze imaging model together with the soft matting method has been used in this work to produce high quality haze free image. Experimental results demonstrate that the proposed approach produces better results than the classic DCP approach as color fidelity and contrast of haze free image are improved and no over-saturation in the sky region is observed. Further, lifting Haar wavelet transform is employed to reduce overall execution time by a factor of two to three as compared to the conventional approach.

Keywords: Depth based atmospheric light, dark channel prior, lifting wavelet.

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Authors: R. Saraçoğlu, N. Allahverdi

Abstract:

Searching similar documents and document management subjects have important place in text mining. One of the most important parts of similar document research studies is the process of classifying or clustering the documents. In this study, a similar document search approach that includes discussion of out the case of belonging to multiple categories (multiple categories problem) has been carried. The proposed method that based on Fuzzy Similarity Classification (FSC) has been compared with Rocchio algorithm and naive Bayes method which are widely used in text mining. Empirical results show that the proposed method is quite successful and can be applied effectively. For the second stage, multiple categories vector method based on information of categories regarding to frequency of being seen together has been used. Empirical results show that achievement is increased almost two times, when proposed method is compared with classical approach.

Keywords: Document similarity, Fuzzy classification, Multiple categories, Text mining.

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Authors: Lewis E. Hibell, Honghai Liu, David J. Brown

Abstract:

This paper presents the results of enhancing images from a left and right stereo pair in order to increase the resolution of a 3D representation of a scene generated from that same pair. A new neural network structure known as a Self Delaying Dynamic Network (SDN) has been used to perform the enhancement. The advantage of SDNs over existing techniques such as bicubic interpolation is their ability to cope with motion and noise effects. SDNs are used to generate two high resolution images, one based on frames taken from the left view of the subject, and one based on the frames from the right. This new high resolution stereo pair is then processed by a disparity map generator. The disparity map generated is compared to two other disparity maps generated from the same scene. The first is a map generated from an original high resolution stereo pair and the second is a map generated using a stereo pair which has been enhanced using bicubic interpolation. The maps generated using the SDN enhanced pairs match more closely the target maps. The addition of extra noise into the input images is less problematic for the SDN system which is still able to out perform bicubic interpolation.

Keywords: Genetic Evolution, Image Enhancement, Neuron Networks, Stereo Vision

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Authors: E. Tskitishvili, L. Jgenti, I. Eliava, T. Tskitishvili, N. Bagathuria, M. Gigolashvili

Abstract:

There has been studied the distribution character of potato stem nematode (Ditylenchus destructor Thorne, 1945) on the potato fields in four municipalities (Tsalka, Bolnisi, Marneuli, Gardabani) of Kvemo Kartli (Eastern Georgia).

As a result of scientific research there is stated the extensiveness of pathogens invasion, accompanying composition of fauna species, environmental groups of populations and quantity.

During the research process in the studied ecosystems there were registered 160 forms of free-living and Phyto-parasitic nematodes, from which 118 forms are determined as species and 42 as genus.

It was found that in almost the entire studied ecosystem there is dominated pathogenic nematodes Ditylenchus destructor. The large number of exemplars (almost uncountable) was found in tubers material of Bolnisi and Gardabani. 

Keywords: Nematoda, potato, steam, bioecological, monitoring.

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Authors: Y. Djouadi., F. Souam.

Abstract:

Most of the existing text mining approaches are proposed, keeping in mind, transaction databases model. Thus, the mined dataset is structured using just one concept: the “transaction", whereas the whole dataset is modeled using the “set" abstract type. In such cases, the structure of the whole dataset and the relationships among the transactions themselves are not modeled and consequently, not considered in the mining process. We believe that taking into account structure properties of hierarchically structured information (e.g. textual document, etc ...) in the mining process, can leads to best results. For this purpose, an hierarchical associations rule mining approach for textual documents is proposed in this paper and the classical set-oriented mining approach is reconsidered profits to a Direct Acyclic Graph (DAG) oriented approach. Natural languages processing techniques are used in order to obtain the DAG structure. Based on this graph model, an hierarchical bottom up algorithm is proposed. The main idea is that each node is mined with its parent node.

Keywords: Graph based association rules mining, Hierarchical document structure, Text mining.

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Authors: A. Harras, A. Tsuji, K. Terada

Abstract:

Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%.

Keywords: Automatic detection, tracking, pedestrians.

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Authors: Siddhartha Bhattacharyya, Paramartha Dutta, Ujjwal Maulik, Prashanta Kumar Nandi

Abstract:

The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.

Keywords: Colour image segmentation, fuzzy set theory, multi-level activation functions, parallel self-organizing neural network.

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Authors: Shigeaki Sakurai, Makino Kyoko, Shigeru Matsumoto

Abstract:

This paper proposes a method that predicts attractive evaluation objects. In the learning phase, the method inductively acquires trend rules from complex sequential data. The data is composed of two types of data. One is numerical sequential data. Each evaluation object has respective numerical sequential data. The other is text sequential data. Each evaluation object is described in texts. The trend rules represent changes of numerical values related to evaluation objects. In the prediction phase, the method applies new text sequential data to the trend rules and evaluates which evaluation objects are attractive. This paper verifies the effect of the proposed method by using stock price sequences and news headline sequences. In these sequences, each stock brand corresponds to an evaluation object. This paper discusses validity of predicted attractive evaluation objects, the process time of each phase, and the possibility of application tasks.

Keywords: Trend rule, frequent pattern, numerical sequential data, text sequential data, evaluation object.

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Authors: Kamil R. Aida-zade, Jamaladdin Z. Hasanov

Abstract:

An approach is offered for more precise definition of base lines- borders in handwritten cursive text and general problems of handwritten text segmentation have also been analyzed. An offered method tries to solve problems arose in handwritten recognition with specific slant or in other words, where the letters of the words are not on the same vertical line. As an informative features, some recognition systems use ascending and descending parts of the letters, found after the word-s baseline detection. In such recognition systems, problems in baseline detection, impacts the quality of the recognition and decreases the rate of the recognition. Despite other methods, here borders are found by small pieces containing segmentation elements and defined as a set of linear functions. In this method, separate borders for top and bottom border lines are found. At the end of the paper, as a result, azerbaijani cursive handwritten texts written in Latin alphabet by different authors has been analyzed.

Keywords: Azeri, azerbaijani, latin, segmentation, cursive, HWR, handwritten, recognition, baseline, ascender, descender, symbols.

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Authors: Tudor Barbu

Abstract:

We provide a supervised speech-independent voice recognition technique in this paper. In the feature extraction stage we propose a mel-cepstral based approach. Our feature vector classification method uses a special nonlinear metric, derived from the Hausdorff distance for sets, and a minimum mean distance classifier.

Keywords: Text-independent speaker recognition, mel cepstral analysis, speech feature vector, Hausdorff-based metric, supervised classification.

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