Search results for: online lexical segmentation

Authors: Khalil Khan, Ikram Syed, Muhammad Ehsan Mazhar, Iran Uddin, Nasir Ahmad

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Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method (FPL) which divides a given image into its constitutes parts is proposed in this paper. A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. The testing phase is performed with two semantic segmentation methods, i.e., pixel and super-pixel based segmentation. In pixel-based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixel only – as a result, the same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68 % and 93.45% respectively.

Keywords: face labeling, semantic segmentation, classification, face segmentation

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Authors: Yayun Hsu, Henry Horng-Shing Lu

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This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate cross talk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds since biological information is inherently included in the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: magnetic resonance image (MRI), c-means model, image segmentation, information entropy

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Authors: Khaled Barkaoui

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Research on second-language (L2) learning tends to focus on comparing students with different levels of proficiency at one point in time. However, to understand L2 development, we need more longitudinal research. In this study, we adopt a longitudinal approach to examine changes in three indicators of L2 ability, complexity, accuracy, and fluency (CAF), as reflected in the writing of L2 learners when writing on different tasks before and after a period L2 instruction. Each of 85 Chinese learners of English at three levels of English language proficiency responded to two writing tasks (independent and integrated) before and after nine months of English-language study in China. Each essay (N= 276) was analyzed in terms of numerous CAF indices using both computer coding and human rating: number of words written, number of errors per 100 words, ratings of error severity, global syntactic complexity (MLS), complexity by coordination (T/S), complexity by subordination (C/T), clausal complexity (MLC), phrasal complexity (NP density), syntactic variety, lexical density, lexical variation, lexical sophistication, and lexical bundles. Results were then compared statistically across tasks, L2 proficiency levels, and time. Overall, task type had significant effects on fluency and some syntactic complexity indices (complexity by coordination, structural variety, clausal complexity, phrase complexity) and lexical density, sophistication, and bundles, but not accuracy. L2 proficiency had significant effects on fluency, accuracy, and lexical variation, but not syntactic complexity. Finally, fluency, frequency of errors, but not accuracy ratings, syntactic complexity indices (clausal complexity, global complexity, complexity by subordination, phrase complexity, structural variety) and lexical complexity (lexical density, variation, and sophistication) exhibited significant changes after instruction, particularly for the independent task. We discuss the findings and their implications for assessment, instruction, and research on CAF in the context of L2 writing.

Keywords: second language writing, Fluency, accuracy, complexity, longitudinal

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Authors: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammad Hossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: facial image, segmentation, PCM, FCM, skin error, facial surgery

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Authors: Vivian Orsi

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In this research, we aim to investigate the lexicon of women's magazines, with special attention to fashion, whose universe is very receptive to lexical borrowings, especially those from English, called Anglicisms. Thus, we intend to discuss the presence of English items and expressions on the online French women's magazine Glamour France collected from six months. Highlighting the quantitative aspects of the use of English in that publication, we can affirm that the use of those lexical borrowings seems to represent sophistication to attract readers and identification with other cultures, establishing communication and intensifying the language of fashion. The potential for creativity in fashion lexicon is made possible by its permeability to social and linguistic phenomena across all social classes that allow constant manipulation of genuine borrowings. Besides, it seems to assume the value of prerequisite to participate in the fashion centers of the world. The use of Anglicisms in Glamour France is not limited to designate concepts and fashionable items that have no equivalent in French, but it acts as a kind of seduction tool, which uses the symbolic capital of English as the global language of communication.

Keywords: Anglicisms, lexicology, borrowings, fashion language

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Authors: Shu-Yu Hsu, Chen-Chien Hsu, Wei-Yen Wang

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Color segmentation is a basic and simple way for recognizing the visual markers in a robotic tracking system. In this paper, we propose a new method for color segmentation by incorporating differential evolution algorithm and connected component labeling to autonomously preset the HSV threshold of visual markers. To evaluate the effectiveness of the proposed algorithm, a ROBOTIS OP2 humanoid robot is used to conduct the experiment, where five most commonly used color including red, purple, blue, yellow, and green in visual markers are given for comparisons.

Keywords: color segmentation, differential evolution, connected component labeling, humanoid robot

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Authors: Amaal Ali Al-Gamde

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The Syrian revolution is a major issue in the Middle East, which draws in world powers and receives a great focus in international mass media since 2011. The heavy global reliance on cyber news and digital sources plays a key role in conveying a sense of bias to a wide range of online readers. Thus, based on the assumption that media discourse possesses ideological implications, this study investigates the representation of Syrian revolution in online media. The paper explores the discursive constructions of anti and pro-government powers in Syrian revolution in 1000,000-word corpus of Fars online reports (an Iranian news agency), issued between 2013 and 2015. Taking a corpus assisted discourse analysis approach, the analysis investigates three types of lexicosemantic relations, the semantic macrostructures within which the two social actors are framed, the lexical collocations characterizing the news discourse and the discourse prosodies they tell about the two sides of the conflict. The study utilizes computer-based approaches, sketch engine and AntConc software to minimize the bias of the subjective analysis. The analysis moves from the insights of lexical frequencies and keyness scores to examine themes and the collocational patterns. The findings reveal the Fars agency’s ideological mode of representations in reporting events of Syrian revolution in two ways. The first is by stereotyping the opposition groups under the umbrella of terrorism, using words such as (law breakers, foreign-backed groups, militant groups, terrorists) to legitimize the atrocities of security forces against protesters and enhance horror among civilians. The second is through emphasizing the power of the government and depicting it as the defender of the Arab land by foregrounding the discourse of international conspiracy against Syria. The paper concludes discussing the potential importance of triangulating corpus linguistic tools with critical discourse analysis to elucidate more about discourses and reality.

Keywords: discourse prosody, ideology, keyness, semantic macrostructure

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Authors: Mahdieh Farzin Asanjan, Erkan Unal Mumcuoglu

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Mitochondria are cytoplasmic organelles of the cell, which have a significant role in the variety of cellular metabolic functions. Mitochondria act as the power plants of the cell and are surrounded by two membranes. Significant morphological alterations are often due to changes in mitochondrial functions. A powerful technique in order to study the three-dimensional (3D) structure of mitochondria and its alterations in disease states is Electron microscope tomography. Detection of mitochondria in electron microscopy images due to the presence of various subcellular structures and imaging artifacts is a challenging problem. Another challenge is that each image typically contains more than one mitochondrion. Hand segmentation of mitochondria is tedious and time-consuming and also special knowledge about the mitochondria is needed. Fully automatic segmentation methods lead to over-segmentation and mitochondria are not segmented properly. Therefore, semi-automatic segmentation methods with minimum manual effort are required to edit the results of fully automatic segmentation methods. Here two editing tools were implemented by applying spline surface dragging and interactive live-wire segmentation tools. These editing tools were applied separately to the results of fully automatic segmentation. 3D extension of these tools was also studied and tested. Dice coefficients of 2D and 3D for surface dragging using splines were 0.93 and 0.92. This metric for 2D and 3D for live-wire method were 0.94 and 0.91 respectively. The root mean square symmetric surface distance values of 2D and 3D for surface dragging was measured as 0.69, 0.93. The same metrics for live-wire tool were 0.60 and 2.11. Comparing the results of these editing tools with the results of automatic segmentation method, it shows that these editing tools, led to better results and these results were more similar to ground truth image but the required time was higher than hand-segmentation time

Keywords: medical image segmentation, semi-automatic methods, transmission electron microscopy, surface dragging using splines, live-wire

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Authors: Dagmara Wrzecionkowska

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Mass media campaigns against obesity are often designed to impact large audiences. This usually means that their audience is defined based on general demographic characteristics like age, gender, occupation etc., not taking into account psychographics like behavior, motivations, wants, etc. Using psychographics, as the base for the audience segmentation, is a common practice in case of successful campaigns, as it allows developing more relevant messages. It also serves a purpose of identifying key segments, those that generate the best return on investment. For a health campaign, that would be segments that have the best chance of being converted into healthy lifestyle at the lowest cost. This paper presents the limitations of the demographic targeting, based on the findings from the reception study of IMSS anti-obesity TV commercials and proposes mothers as the first level of segmentation, in the process of identifying the key segment for these campaigns.

Keywords: anti-obesity campaigns, mothers, segmentation, targeting

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Yuliya E. Leshchenko, Tatyana S. Ostapenko

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Language variation is a widespread linguistic phenomenon which can affect different levels of a language system: phonological, morphological, lexical, syntactic, etc. It is obvious that the scope of possible standard alternations within a particular language is limited by a variety of its norms and regulations which set more or less clear boundaries for what is possible and what is not possible for the speakers. The possibility of lexical variation (alternate usage of lexical items within the same contexts) is based on the fact that the meanings of words are not clearly and rigidly defined in the consciousness of the speakers. Therefore, lexical variation is usually connected with unstable relationship between words and their referents: a case when a particular lexical item refers to different types of referents, or when a particular referent can be named by various lexical items. We assume that the scope of lexical variation in bilingual speech is generally wider than that observed in monolingual speech due to the fact that, besides ‘lexical item – referent’ relations it involves the possibility of cross-language variation of L1 and L2 lexical items. We use the term ‘cross-language variation’ to denote a case when two equivalent words of different languages are treated by a bilingual speaker as freely interchangeable within the common linguistic context. As distinct from code-switching which is traditionally defined as the conscious use of more than one language within one communicative act, in case of cross-language lexical variation the speaker does not perceive the alternate lexical items as belonging to different languages and, therefore, does not realize the change of language code. In the paper, the authors present research of lexical variation of adult Komi-Permyak – Russian bilingual speakers. The two languages co-exist on the territory of the Komi-Permyak District in Russia (Komi-Permyak as the ethnic language and Russian as the official state language), are usually acquired from birth in natural linguistic environment and, according to the data of sociolinguistic surveys, are both identified by the speakers as coordinate mother tongues. The experimental research demonstrated that alternation of Komi-Permyak and Russian words within one utterance/phrase is highly frequent both in speech perception and production. Moreover, our participants estimated cross-language word combinations like ‘маленькая /Russian/ нывка /Komi-Permyak/’ (‘a little girl’) or ‘мунны /Komi-Permyak/ домой /Russian/’ (‘go home’) as regular/habitual, containing no violation of any linguistic rules and being equally possible in speech as the equivalent intra-language word combinations (‘учöтик нывка’ /Komi-Permyak/ or ‘идти домой’ /Russian/). All the facts considered, we claim that constant concurrent use of the two languages results in the fact that a large number of their words tend to be intuitively interpreted by the speakers as lexical variants not only related to the same referent, but also referring to both languages or, more precisely, to none of them in particular. Consequently, we can suppose that bilingual mental lexicon includes an extensive ‘fused’ zone of lexical representations that provide the basis for cross-language variation in bilingual speech.

Keywords: bilingualism, bilingual mental lexicon, code-switching, lexical variation

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: M. A. Ben Messaoud, A. Bouzid, N. Ellouze

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Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. We have evaluated our method on the Keele database. Experimental results show the effectiveness of our method presenting a good performance. It shows that the two simple features can find word boundaries, and extracted the segments of the clean speech.

Keywords: multiscale product, spectral centroid, speech segmentation, zero crossings rate

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

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Authors: Chamindi Dilkushi Senaratne

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Borrowing is the morphological, syntactic and (usually) phonological integration of lexical items from one language into the structure of another language. Borrowings show complete linguistic integration and due to the frequency of use become fossilized in the recipient language differentiating them from switches and mixes. Code mixes are different to borrowings. Code mixing takes place when speakers use lexical items in casual conversation to serve a variety of functions. This study presents an analysis of lexical items used in English newspapers in Sri Lanka in 2017 which reveal characteristics of borrowing or code mixes. Both phenomena arise due to language contact. The study will also use data from social media websites that comment on newspaper articles available on the web. The study reiterates that borrowings are distinguishable from code mixes and that they are two different phenomena that occur in language contact situations. The study also shows how existing morphological processes are used to create new vocabulary in language use. The study sheds light into how existing morphological processes are used by the bilingual to be creative, innovative and convey a bilingual identity.

Keywords: borrowing, code mixing, morphological processes

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Authors: Aileen F. Wang

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Mammography has been one of the most reliable methods for early detection and diagnosis of breast cancer. However, mammography misses about 17% and up to 30% of breast cancers due to the subtle and unstable appearances of breast cancer in their early stages. Recent computer-aided diagnosis (CADx) technology using Zernike moments has improved detection accuracy. However, it has several drawbacks: it uses manual segmentation, Zernike moments are not robust, and it still has a relatively high false negative rate (FNR)–17.6%. This project will focus on the development of a novel breast cancer detection algorithm to automatically segment the breast mass and further reduce FNR. The algorithm consists of automatic segmentation of a single breast mass using Point Region Growing Segmentation, reconstruction of the segmented breast mass using Pseudo-Zernike moments, and classification of the breast mass using the root mean square (RMS). A comparative study among the various algorithms on the segmentation and reconstruction of breast masses was performed on randomly selected mammographic images. The results demonstrated that the newly developed algorithm is the best in terms of accuracy and cost effectiveness. More importantly, the new classifier RMS has the lowest FNR–6%.

Keywords: computer aided diagnosis, mammography, point region growing segmentation, pseudo-zernike moments, root mean square

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

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Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

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Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: attention-based fully convolutional network, optic disc detection and segmentation, retinal fundus image, screening of ocular diseases

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm

Keywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm

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Authors: Peifei Zhu, Zisheng Li, Yasuki Kakishita, Mayumi Suzuki, Tomoaki Chono

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Segmentation of left ventricle (LV) from cardiac ultrasound images provides a quantitative functional analysis of the heart to diagnose disease. Active Shape Model (ASM) is a widely used approach for LV segmentation but suffers from the drawback that initialization of the shape model is not sufficiently close to the target, especially when dealing with abnormal shapes in disease. In this work, a two-step framework is proposed to improve the accuracy and speed of the model-based segmentation. Firstly, a robust and efficient detector based on Hough forest is proposed to localize cardiac feature points, and such points are used to predict the initial fitting of the LV shape model. Secondly, to achieve more accurate and detailed segmentation, ASM is applied to further fit the LV shape model to the cardiac ultrasound image. The performance of the proposed method is evaluated on a dataset of 800 cardiac ultrasound images that are mostly of abnormal shapes. The proposed method is compared to several combinations of ASM and existing initialization methods. The experiment results demonstrate that the accuracy of feature point detection for initialization was improved by 40% compared to the existing methods. Moreover, the proposed method significantly reduces the number of necessary ASM fitting loops, thus speeding up the whole segmentation process. Therefore, the proposed method is able to achieve more accurate and efficient segmentation results and is applicable to unusual shapes of heart with cardiac diseases, such as left atrial enlargement.

Keywords: hough forest, active shape model, segmentation, cardiac left ventricle

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Authors: Abdelkhalek Bakkari

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Image segmentation is a critical step in image processing and pattern recognition. In this paper, we proposed a new robust automatic image classification based on a dynamic fuzzy c-means algorithm and mathematical morphology. The proposed segmentation algorithm (DFCM_MM) has been applied to MR perfusion images. The obtained results show the validity and robustness of the proposed approach.

Keywords: segmentation, classification, dynamic, fuzzy c-means, MR image

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Authors: Fang Tan

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This research focuses on the appropriate use of lexical bundles in spoken discourse, particularly in English as a Foreign Language (EFL) classrooms in Northeast China. While previous studies have mainly examined lexical bundles in written discourse, there is a need to investigate their usage in spoken discourse due to the limited availability of spoken discourse corpora. English teachers’ use of lexical bundles is crucial for effective teaching and communication in the EFL classroom. The aim of this study is to investigate the functions of four-word lexical bundles in native English teachers’ EFL oral English classes in Northeast China. Specifically, the research focuses on the usage of stance bundles, which were found to be the most significant type of bundle in the analyzed corpus. By comparing the self-built university spoken English classroom discourse corpus with the other self-built university English for General Purposes (EGP) corpus, the study aims to highlight the difference in bundle usage between native and non-native teachers in EFL classrooms. The research employs a corpus-based study. The observed corpus consists of more than 300,000 tokens, in which the data has been collected in the past five years. The reference corpus is composed of over 800,000 tokens, in which the data has been collected over 12 years. All the primary data collection involved transcribing and annotating spoken English classes taught by native English teachers. The analysis procedures included identifying and categorizing four-word lexical bundles, with specific emphasis on stance bundles. Frequency counts, and comparisons with the Chinese English teachers’ corpus were conducted to identify patterns and differences in bundle usage. The research addresses the following questions: 1) What are the functions of four-word lexical bundles in native English teachers’ EFL oral English classes? 2) How do stance bundles differ in usage between native and non-native English teachers’ classes? 3) What implications can be drawn for English teachers’ professional development based on the findings? In conclusion, this study provides valuable insights into the usage of four-word lexical bundles, particularly stance bundles, in native English teachers’ EFL oral English classes in Northeast China. The research highlights the difference in bundle usage between native and non-native English teachers’ classes and provides implications for English teachers’ professional development. The findings contribute to the understanding of lexical bundle usage in EFL classroom discourse and have theoretical importance for language teaching methodologies. The self-built university English classroom discourse corpus used in this research is a valuable resource for future studies in this field.

Keywords: EFL classroom discourse, four-word lexical bundles, stance, implication

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Authors: Anthony D. Rhodes, Manan Goel

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We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a dense convolutional network with context-aware skip connections and compressed, 'hypercolumn' image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) we use a statistically-principled, tensor decomposition procedure to modulate the number of hypercolumn features and (2) we render these features in their native resolution using a convolutional tessellation technique. For improved pixel-level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including green screen and various composited scenes with corresponding, hand-crafted, pixel-level segmentations. Our work presents a improves state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.

Keywords: computer vision, object segmentation, interactive segmentation, model compression

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Authors: Kyujin Shim

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This study explores how individual motivations in political psychology will lead to political expression and online discourse, and how those online political discourses result in individuals’ exposure to extreme/ personally-entertaining/ disinhibiting content. This study argues that a new framework beyond the conventional paradigm (e.g., selective exposure based on partisanship/ ideology) is needed for better grasp of non-ideological/ anarchic, and/or of nonpartisan yet anonymity-/ extremity-/ disinhibition-related online behaviors regarding political conversations. Further, this study proposes a new definition of ‘selective exposure,’ with special attention to online efficacy and psychological motivations/gratifications sought in the online sphere.

Keywords: selective exposure, fear of isolation, political psychology, online discourse

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Authors: Lei Qin

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As shared accommodation has become one of the most important market developments in the tourism industry, numerous contributions have emerged on travelers’ motivations to choose shared accommodation. A debated question, however, resides in the heterogeneity of travelers based on motivations. This paper aims to reconcile opposing perspectives by comparing motivation segmentation at two distinct phases of innovation adoption of this new hospitality option: (i) before the first travel – potential users showing interest (n=420) and (ii) after the first travel – users (n=420). Interestingly, we find that travelers (including pre-and-post adopters) have a stronger agreement in experiential motivations than practical motivations. However, the heterogeneity of motivations among travelers is significantly higher in users, increasing from two to six clusters, which means travelers cluster into more and distinct motivation groups after adoption. Rather than invalidating specific assumptions used in the literature in terms of motivation heterogeneity, this paper reconciles opposing findings by putting them along with one another in the process of innovation adoption. A subsequent tourists’ segmentation based on motivations were conducted according to their innovation adoption stages.

Keywords: motivation, pre-and-post adoption, shared accommodation, segmentation

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

Abstract:

The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

Procedia PDF Downloads 334

Authors: Dean Raiyasmi, Elvi Citraresmana, Sutiono Mahdi

Abstract:

Language shapes the human mind and its concept toward things. Using image schemas, in nowadays technology, even AI (artificial intelligence) can concept things in response to their creator negativity or positivity. This is reflected inside one of the most selling game around the world in 2012 called Dragon Age Origins. The AI in form of NPC (Non-Playable Character) inside the game reflects on the creator of the game on negativity or positivity toward the lexical concept of mage. Through image schemas, shaping the lexical concept of mage deemed possible and proved the negativity or positivity creator of the game toward mage. This research analyses the cognitive-semantic process of image schema and shaping the concept of ‘mage’ by describing kinds of image schemas exist in the Dragon Age Origin Game. This research is also aimed to analyse kinds of image schemas and describing the image schemas which shaping the concept of ‘mage’ itself. The methodology used in this research is qualitative where participative observation is employed with five stages and documentation. The results shows that there are four image schemas exist in the game and those image schemas shaping the lexical concept of ‘mage’.

Keywords: cognitive semantic, image-schema, conceptual metaphor, video game

Procedia PDF Downloads 407

Authors: Pui-Lai To, E-Ping Sung

Abstract:

With the flourishing development of online shopping, an increasing number of customers see online shopping as an entertaining experience. Because the online consumer has a double identity as a shopper and an Internet user, online shopping should offer hedonic values of shopping and Internet usage. The purpose of this study is to investigate hedonic online shopping motivations from the perspectives of traditional hedonic value and flow theory. The study adopted a focus group interview method, including two online and two offline interviews. Four focus groups of shoppers consisted of online professionals, online college students, offline professionals and offline college students. The results of the study indicate that traditional hedonic values and dimensions of flow theory exist in the online shopping environment. The study indicated that online shoppers seem to appreciate being able to learn things and grow to become competitive achievers online. Comparisons of online hedonic motivations between groups are conducted. This study serves as a basis for the future growth of Internet marketing.

Keywords: flow theory, hedonic motivation, internet shopping

Procedia PDF Downloads 247