Relevant bibliographies by topics / Segmentation technology

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Segmentation technology'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Segmentation technology"

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Zhu, Ke. "Analysis of Chinese Word Segmentation Technology." Applied Mechanics and Materials 687-691 (November 2014): 1540–43. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1540.

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Under the influence of network information resources in exponentially growing, we are entering the information society, and all aspects of our lives has permeated with information technology. But the Chinese word segmentation technology for processing Chinese information becomes more and more important. Therefore, this paper sets out a series of Chinese word segmentation techniques, which mainly consists of Chinese word segmentation technology based on statistic, Chinese word segmentation techniques based on dictionary and hybrid techniques of Chinese word segmentation and segmentation technology based on knowledge and understanding.
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Hu, Chang Jie, and Hong Li Xu. "Face Image Segmentation Technology Research." Advanced Materials Research 846-847 (November 2013): 1339–42. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1339.

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Face contains the very rich information, which is a typical biological feature .It has a wide application prospect in personal identification, intelligent video surveillance and human-computer interaction. Face detection is to determine the number, the location, size and other information of all the faces among the color images that have been input. Firstly, skin color model is established, and then we use the skin color model to convert color image to gray image, and then we can denoise gray image, at last use the Fisher criterion to obtain the dynamic threshold segmentation of the face image, so as to lay a good foundation for the location of the face region. Through the experiment we can see, the selection of dynamic threshold, for different detecting images, obtained better color segmentation.
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Han, Dan, and Zhi Han Yu. "The Critical Technology Development Status of Machine Translation." Advanced Materials Research 791-793 (September 2013): 1622–25. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1622.

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In this article, we mainly introduce some basic concepts about machine translation. Machine translation means translating a natural language text to another by software. It can be divided into two categories: rule-based and corpus-based. IBM's statistical machine translation, Microsoft's multi-language machine translation project, AT & T's voice translation system and CMUs PANGLOSS system are three typical machine translation systems. Due to sentences are constructed by words continuously in Chinese. Chinese word segmentation is very essential. Three methods of Chinese word segmentation: segmentation methods based on string matching, segmentation method based on the understanding and segmentation method based on the statistics.
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Hu, Guangdong, Fengyuan Qian, Longgui Sha, and Zilong Wei. "Application of Deep Learning Technology in Glioma." Journal of Healthcare Engineering 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/8507773.

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A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily magnetic resonance imaging (MRI) oriented, is considered as one of common tools developed for doctors. These doctors use this system to examine, analyse, and diagnose appearance of the glioma’s outward for both patients, i.e., indoor and outdoor. In the literature, a widely utilized approach for the segmentation of glioma is the deep learning-oriented method. To cope with this issue, a segmentation of glioma approach, i.e., primarily on the convolution neural networks, is developed in this manuscript. A DM-DA-enabled cascading approach for the segmentation of glioma, which is 2DResUnet-enabled model, is reported to resolve the problem of spatial data acquisition of insufficient 3D specifically in the 2D full CNN along with the core issue of memory consumption of 3D full CNN. For gliomas segmentation at various stages, we have utilized multiscale fusion approach, attention, segmentation, and DenseBlock. Moreover, for reducing three dimensionalities of the Unet model, a sampling of fixed region is used along with multisequence data of the glioma image. Finally, the CNN model has the ability of producing a better segmentation of tumor preferably with minimum possible memory. The proposed model has used BraTS18 and BraTS17 benchmark data sets for fivefold cross-validation (local) and online evaluation preferably official, respectively. Evaluation results have verified that edema’s Dice Score preferable average, enhancement, and core areas of the segmentation of the glioma with DM-DA-Unet perform exceptionally well on the validation set of BraTS17. Finally, average sensitivity was observed to be high as well, which is approximately closer to the best segmentation model and its effect on the validation set of BraTS1 and has segmented gliomas accurately.
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Yang, Zi, Mingli Chen, Mahdieh Kazemimoghadam, Lin Ma, Strahinja Stojadinovic, Robert Timmerman, Tu Dan, Zabi Wardak, Weiguo Lu, and Xuejun Gu. "Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation." Physics in Medicine & Biology 67, no. 2 (January 19, 2022): 025004. http://dx.doi.org/10.1088/1361-6560/ac4667.

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Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.
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Huo, Chun Bao, Shuai Tong, Li Hui Zhao, and Xiang Yun Li. "Research on Image Segmentation Technology with Tissue Section Cell Segmentation Algorithm." Advanced Materials Research 1046 (October 2014): 88–91. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.88.

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Generally, the effect of cell image that segmented via the threshold value method is not ideal generally; the found cell boundary cannot conform to the cell edge in the original picture well. In this paper, the threshold value segmentation method is improved; apply the judging criterion of gray level difference maximum interval to be the minimum, and conduct secondary treating on the image, and the image’s segmentation effect is more ideal.
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Johansson, David, Patrik Jönsson, Bodil Ivarsson, and Maria Christiansson. "Information Technology and Medical Technology Personnel´s Perception Regarding Segmentation of Medical Devices: A Focus Group Study." Healthcare 8, no. 1 (January 21, 2020): 23. http://dx.doi.org/10.3390/healthcare8010023.

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Objective: Segmentation is one way of improving data protection. The aim of this study was to investigate Information Technology (IT) and Medical Technology (MT) personnel’s perception in relation to ongoing segmentation of medical devices and IT infrastructure in the healthcare sector. Methods: Focus group interviews with 9 IT and 9 MT personnel in a county council in southern Sweden were conducted. The interviews focused on two areas: Positive expectations and misgivings. Digital recordings were transcribed verbatim and analyzed using qualitative content analysis. Results: Responses related to 2 main areas: Information security and implementation of segmentation. Informants stated that network segmentation would increase the overall level of cybersecurity for medical devices, addressing both insider and outsider threats. However, it would also increase the need for administration and the need for knowledge of the communication patterns of medical devices from the manufacturer’s perspective. Conclusion: IT and MT personnel in a county council in southern Sweden believed that segmentation would increase cybersecurity but also increase administration and resource needs, which are important opinions to take into consideration. The present study can be used as a model for others to increase awareness of opinions of healthcare organizations.
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Ramírez-Correa, Patricio E., F. Javier Rondán-Cataluña, and Jorge Arenas-Gaitán. "A Posteriori Segmentation of Personal Profiles of Online Video Games’ Players." Games and Culture 15, no. 3 (April 18, 2018): 227–47. http://dx.doi.org/10.1177/1555412018766786.

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There are diverse segmentations of online players in the literature. Most of them are proposed a priori, and there are no segmentations based on the acceptance of technology and the personal values of the players. The foremost purpose of this study is to obtain a posteriori segmentation of online video games’ players, founded on unified theory of acceptance and use of technology model, and to describe the subsequent segments consistent with the personal values of Schwartz. The measurement model and the structural model were analyzed with partial least squares (PLS). Subsequently, the PLS-prediction-oriented segmentation technique has been devoted to inspecting unobserved heterogeneity and to find players’ segments. Four segments are obtained from the statistical analysis, and data analysis shown that in each of the segments, the explained variance of both use and behavioral intention (which are the endogenous variables of the model) is significantly improved by comparing the results of the global sample.
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Ma, Ling, Xiaomao Hou, and Zhi Gong. "Image Segmentation Technology Based on Attention Mechanism and ENet." Computational Intelligence and Neuroscience 2022 (August 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/9873777.

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With the development of today’s society, medical technology is becoming more and more important in people’s daily diagnosis and treatment and the number of computed tomography (CT) images and MRI images is also increasing. It is difficult to meet today’s needs for segmentation and recognition of medical images by manpower alone. Therefore, the use of computer technology for automatic segmentation has received extensive attention from researchers. We design a tooth CT image segmentation method combining attention mechanism and ENet. First, dilated convolution is used with the spatial information path, with a small downsampling factor to preserve the resolution of the image. Second, an attention mechanism is added to the segmentation network based on CT image features to improve the accuracy of segmentation. Then, the designed feature fusion module obtains the segmentation result of the tooth CT image. It was verified on tooth CT image dataset published by West China Hospital, and the average intersection ratio and accuracy were used as the metric. The results show that, on the dataset of West China Hospital, Mean Intersection over Union (MIOU) and accuracy are 83.47% and 95.28%, respectively, which are 3.3% and 8.09% higher than the traditional model. Compared with the multiple watershed algorithm, the Chan–Vese segmentation algorithm, and the graph cut segmentation algorithm, our algorithm increases the calculation time by 56.52%, 91.52%, and 62.96%, respectively. It can be seen that our algorithm has obvious advantages in MIOU, accuracy, and calculation time.
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Fang, Jie, QingBiao Zhou, and Shuxia Wang. "Segmentation Technology of Nucleus Image Based on U-Net Network." Scientific Programming 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/1892497.

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To solve the problems of rough edge and poor segmentation accuracy of traditional neural networks in small nucleus image segmentation, a nucleus image segmentation technology based on U-Net network is proposed. First, the U-Net network is used to segment the nucleus image, which stitches the feature images in the channel dimension to achieve feature fusion, and the skip structure is used to combine the low- and high-level features. Then, the subregional average pooling is proposed to improve the global average pooling in the attention module, and an attention channel expansion module is designed to improve the accuracy of image segmentation. Finally, the improved attention module is integrated into the U-Net network to achieve accurate segmentation of the nuclear image. Based on the Python platform, the experimental results show that the proposed segmentation technology can achieve fast convergence, and the mean intersection over union (MIoU) is 85.02%, which is better than other comparison technologies and has a good application prospect.
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Dissertations / Theses on the topic "Segmentation technology"

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Lundström, Claes. "Segmentation of Medical Image Volumes." Thesis, Linköping University, Linköping University, Computer Vision, 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54357.

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Segmentation is a process that separates objects in an image. In medical images, particularly image volumes, the field of application is wide. For example 3D visualisations of the anatomy could benefit enormously from segmentation. The aim of this thesis is to construct a segmentation tool.

The project consist three main parts. First, a survey of the actual need of segmentation in medical image volumes was carried out. Then a unique three-step model for a segmentation tool was implemented, tested and evaluated.

The first step of the segmentation tool is a seed-growing method that uses the intensity and an orientation tensor estimate to decide which voxels that are part of the project. The second step uses an active contour, a deformable “balloon”. The contour is shrunk to fit the segmented border from the first step, yielding a surface suitable for visualisation. The last step consists of letting the contour reshape according to the orientation tensor estimate.

The use evaluation establishes the usefulness of the tool. The model is flexible and well adapted to the users’ requests. For unclear objects the segmentation may fail, but the cause is mostly poor image quality. Even though much work remains to be done on the second and third part of the tool, the results are most promising.

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Farnebäck, Gunnar. "Motion-based segmentation of image sequences." Thesis, Linköping University, Linköping University, Computer Vision, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54351.

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This Master's Thesis addresses the problem of segmenting an image sequence with respect to the motion in the sequence. As a basis for the motion estimation, 3D orientation tensors are used. The goal of the segmentation is to partition the images into regions, characterized by having a coherent motion. The motion model is affine with respect to the image coordinates. A method to estimate the parameters of the motion model from the orientation tensors in a region is presented. This method can also be generalized to a large class of motion models.

Two segmentation algorithms are presented together with a postprocessing algorithm. All these algorithms are based on the competitive algorithm, a general method for distributing points between a number of regions, without relying on arbitrary threshold values. The first segmentation algorithm segments each image independently, while the second algorithm recursively takes advantage of the previous segmentation. The postprocessing algorithm stabilizes the segmentations of a whole sequence by imposing continuity constraints.

The algorithms have been implemented and the results of applying them to a test sequence are presented. Interesting properties of the algorithms are that they are robust to the aperture problem and that they do not require a dense velocity ¯eld.

It is finally discussed how the algorithms can be developed and improved. It is straightforward to extend the algorithms to base the segmentations on alternative or additional features, under not too restrictive conditions on the features.

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Anusha, Anusha. "Word Segmentation for Classification of Text." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396969.

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Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. Word segmentation is the problem of splitting a string of written language into its component words. The purpose of this research is to do a comparative study on different techniques of word segmentation and to identify the best technique that would aid in the extraction of keyword from the text. English was chosen as the language. Dictionary-based and Machine learning approaches were used to split the compound words. This research also aims at evaluating the quality of a word segmentation by comparing it with the segmentation of reference. Results indicated that Dictionary-based word segmentation showed better results in segmenting a compound word compared to the Machine learning segmentation when technical words were involved. Also, to improve the results for the text classification, improving the quality of the text alone is not the key
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Akinyemi, Akinola Olanrewaju. "Atlas-based segmentation of medical images." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2623/.

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Atlas-Based Segmentation of medical images is an image analysis task which involves labelling a desired anatomy or set of anatomy from images generated by medical imaging modalities. The overall goal of atlas-based segmentation is to assist radiologists in the detection and diagnosis of diseases. By extracting the relevant anatomy from medical images and presenting it in an appropriate view, their work-flow can be optimised. This portfolio-style thesis discusses the research projects carried out in order to evaluate the applicability of atlas-based methods to a variety of medical imaging problems. The thesis describes how atlas-based methods have been applied to heart segmentation, to extract the heart for further cardiac analysis from cardiac CT images, to kidney segmentation, to prepare the kidney for automated perfusion measurements, and to coronary vessel tracking, in order to improve on the quality of tracking algorithms. This thesis demonstrates how state of the art atlas-based segmentation techniques can be applied successfully to a range of clinical problems in different imaging modalities. Each application has been tested using not only standard experimentation principles, but also by clinically-trained personnel to evaluate its efficacy. The success of these methods is such that some of the described applications have since been deployed in commercial products. While exploring these applications, several techniques based on published literature were explored and tailored to suit each individual application. This thesis describes in detail the methods used for each application in turn, recognising the state of the art, and outlines the author's contribution in every application.
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Aziz, Andrew. "Customer Segmentation basedon Behavioural Data in E-marketplace." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-330461.

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In the past years, research in the fields of big data analysis, machine learning anddata mining techniques is getting more frequent. This thesis describes a customersegmentation approach in a second hand vintage clothing E-marketplace Plick.These customer groups are based on user interactions with items in themarketplace such as views and "likes". A major goal of this thesis was to constructa personal feed for each user where the items are derived from the user groups.The customer segmentation method discussed in this paper is based on theclustering algorithm K-means using cosine similarity as the similarity measure. Theinput matrix used by the K-means algorithm is a User-Brand ratings matrix whereeach brand is given a rating by each user. A visualization tool was also constructedin order to get a better picture of the data and the resulting clusters. In order tovisualize the highly dimensional User-Brand matrix, Principal Component Analysis isused as a dimensionality reduction algorithm.
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Samuels, Mark Lee. "Reconsidering the superstore workplace : a Sheffield case study of segmentation and technology." Thesis, Sheffield Hallam University, 2002. http://shura.shu.ac.uk/20321/.

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Retailing is back on the research agenda and the analysis of consumption processes is providing a fertile source of insightful geographical literature. Yet despite this interest, the retail workplace remains on the margins of disciplinary concerns. Given this situation, it is time that the retail workplace was reconsidered. The reconsideration within this thesis concentrates on the superstore workplace and attempts to challenge existing applications of labour market segmentation theory. This challenge is driven by an interest in information and communication technology (ICT) and a realisation that these technologies must be understood with reference to human interaction. The empirical analysis centres on one case study, a food-selling superstore in Sheffield. As an empirical link between theory and qualitative analysis, secondary human resource statistics are analysed to provide a guide to segmentation within the store. Qualitative research techniques are used to build an in-depth understanding of different employees activities and experiences. The secondary data suggests that segmentation remains an important framework for organisation within the retail superstore. However, qualitative research illustrates how existing theoretical conceptualisations of the segmented superstore might be problematised by a series of power relationships (dictation, delegation and authority) that are, in part, facilitated by the use of ICTs. These power relationships are in turn reinterpreted within individual worker strategies of manipulation and resistance. Here, workers regularly use ICTs in different ways than the remote head office might have originally intended. It is also suggested that the consent to work for many disadvantaged workers has to be understood by reference to a series of social concerns from outside the workplace (childcare, other domestic relationships, financial survival, lifestyle choice, social experience and self-esteem). These findings suggest a rich vein for additional research and the retail workplace should be pushed to the centre of geographical debate for further analysis.
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Fang, Jian. "Optical Imaging and Computer Vision Technology for Corn Quality Measurement." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/733.

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The official U.S. standards for corn have been available for almost one hundred years. Corn grading system has been gradually updated over the years. In this thesis, we investigated a fast corn grading system, which includes the mechanical part and the computer recognition part. The mechanical system can deliver the corn kernels onto the display plate. For the computer recognition algorithms, we extracted common features from each corn kernel, and classified them to measure the grain quality.
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Grönberg, Axel. "Image Mosaicking Using Vessel Segmentation for Application During Fetoscopic Surgery." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-438422.

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Twin-to-twin-transfusion syndrome is a condition where there is an imbalance in the shared blood circulation between monochorionic twin fetuses due to certaininter-twin vascular connections (anastomoses) in the placenta which has very high morbidity and mortality rate for both fetuses. Fetoscopic laser occlusive coagulation(FLOC) surgery is commonly used to treat the condition which uses a fetoscope to explore the placenta and a laser to occlude the anastomoses causing the imbalance inblood circulation. In order to deal with the navigational difficulties caused by the limited field of view of the fetoscope, this thesis is part of a work towards an application which main purpose is to build a global map of the placenta as well as display position of the fetoscope on that map. A combination of segmentation by neural networks are combined with direct sequential registration techniques are applied to fetoscopic data from FLOC surgeries at Karolinska University Hospital Huddinge and resulting in a proof-of-concept of this mosaicking pipeline setup for the creation of a global map of the placenta during such a surgery. It was however also found that more work is needed to make the system more reliable and among other things less sensitive to poor visual conditions and drift, which can result in low quality mosaics with artifacts due to misaligned images.
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Holmberg, Joakim. "Targeting the zebrafish eye using deep learning-based image segmentation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-428325.

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Researchers studying cardiovascular and metabolic disease in humans commonly usecomputer vision techniques to segment internal structures of the zebrafish animalmodel. However, there are no current image segmentation methods to target theeyes of the zebrafish. Segmenting the eyes is essential for accurate measurement ofthe eyes' size and shape following the experimental intervention. Additionally,successful segmentation of the eyes functions as a good starting point for futuresegmentation of other internal organs. To establish an effective segmentation method,the deep learning neural network architecture, Deeplab, was trained using 275 imagesof the zebrafish embryo. Besides model architecture, the training was refined withproper data pre-processing, including data augmentation to add variety and toartificially increase the training data. Consequently, the results yielded a score of95.88 percent when applying augmentations, and 95.30 percent withoutaugmentations. Despite this minor improvement in accuracy score when using theaugmented training dataset, it also produced visibly better predictions on a newdataset compared to the model trained without augmentations. Therefore, theimplemented segmentation model trained with augmentations proved to be morerobust, as the augmentations gave the model the ability to produce promising resultswhen segmenting on new data.
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Li, Yong. "Topic-based segmentation of web pages." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1445895.

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Books on the topic "Segmentation technology"

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Handbook of market segmentation: Strategic targeting for business and technology firms. 3rd ed. Mumbai: Jaico Publishing, 2007.

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1956-, Solimini Sergio, ed. Variational methods in image segmentation: With seven image processing experiments. Boston: Birkhäuser, 1995.

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Naumov, Vladimir. Markets information and communication technology and sales organization. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/21026.

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In the textbook sets out the basic information about the structure of markets, information and communication technologies (ICT), the methods of their research, assessing the attractiveness and forecasting, criteria and methods of segmentation. Deals with the organization of the sales Department of an IT company, involving analysis of organizational forms, population division, methods of remuneration and non-material incentives for experts dealing with sales of ICT products. Sets out the methodology for strategic sales of complex IT solutions, the technique of negotiation and the basics of neurolinguistic programming. The textbook pays attention to the peculiarities of the sales and promotion of ICT products through the Internet, the possibilities of the use of CRM systems. The principles of the organization of partnerships with clients. This methodical approaches to the assessment of the efficiency of the sales Department of an IT company and its sales staff. Discusses the economic evaluation of the project implementation in selling IT solutions. The textbook is prepared in accordance with the requirements of Federal state educational standard of higher education of the last generation. Designed for students enrolled in training 38.03.05 "Business-Informatics", but it can be useful to students from other disciplines and practitioners working in the field of information and communication technologies.
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Peake, Linda. The impact of new technology on women's employment: Labour market segmentation and women's service sector employment in the Reading area in the 1970's. [Kingston upon Thames]: School of Geography, Kingston Polytechnic, 1986.

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Gans, Joshua. When does funding research by smaller firms bear fruit?: Evidence from the SBIR program. Cambridge, MA: National Bureau of Economic Research, 2000.

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Ypie, Veenstra, ed. The long tail: Waarom we in toekomst minder verkopen van meer. [Amsterdam]: Nieuw Amsterdam, 2006.

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Anderson, Chris. The Long Tail. New York: Hyperion, 2006.

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The Long Tail: Why the Future of Business is Selling Less of More. New York: Hyperion, 2006.

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Jiangtao, Qiao, ed. Chang wei li lun: The long tail. Beijing Shi: Zhong xin chu ban she, 2006.

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Anderson, Chris. The long tail: The revolution changing small markets into big business. New York: Hyperion, 2006.

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Book chapters on the topic "Segmentation technology"

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Phillips, Fred Y. "Technology Life Cycle and Market Segmentation." In Market-Oriented Technology Management, 35–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-08500-4_2.

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Ponte, Jay M., and W. Bruce Croft. "Text segmentation by topic." In Research and Advanced Technology for Digital Libraries, 113–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0026725.

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Zainuddin, Roziati, Sinan Naji, and Jubair Al-Jaafar. "Suppressing False Nagatives in Skin Segmentation." In Future Generation Information Technology, 136–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17569-5_15.

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Asad, Muhammad Hamza, and Abdul Bais. "Weed Density Estimation Using Semantic Segmentation." In Image and Video Technology, 162–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39770-8_13.

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Žagar, Martin, Mario Kovač, Josip Knezović, Hrvoje Mlinarić, and Daniel Hofman. "3D Object Classification and Segmentation Methods." In Signals and Communication Technology, 331–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12802-8_14.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Speech Processing Technology: Basic Consideration." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 21–45. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_2.

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Lo, Kuo-Hua, Mau-Tsuen Yang, and Rong-Yu Lin. "Shadow Removal for Foreground Segmentation." In Advances in Image and Video Technology, 342–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_34.

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Dongre, Vikas J., and Vijay H. Mankar. "Segmentation of Printed Devnagari Documents." In Advances in Computing and Information Technology, 211–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22555-0_23.

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Arora, Kumud, and Poonam Garg. "Quality Assessment Based Fingerprint Segmentation." In Advances in Computing and Information Technology, 569–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31552-7_58.

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Zhang, Kaixu, Maosong Sun, and Ping Xue. "A Local Generative Model for Chinese Word Segmentation." In Information Retrieval Technology, 420–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_41.

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Conference papers on the topic "Segmentation technology"

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Alqahtani, Hussain, Naif Alqahtani, Ryan T. Armstrong, and Peyman Mostaghimi. "Segmentation of X-Ray Images of Rocks Using Supervoxels Over-Segmentation." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22131-ms.

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Abstract Digital core analysis has gained the interest of many scientific communities because of its impact on our understanding of flow in porous media. A typical workflow in digital core analysis includes scanning, reconstruction, denoising, segmentation, and modeling. Image analysis and modeling highly depend on the quality of the segmentation step. In this regard, conventional image segmentation methods often require user input/interference. This results in user bias and may produce a range of possible segmentation outcomes. To address this, we propose an unsupervised machine learning framework that offers multiple functionalities including improved mineral and micro-porosity identification. Superpixel (2D) and (3D) work by over-segmenting greyscale images using a family of over-segmentation algorithms. Simple Linear Iterative Clustering (SLIC) is one of these algorithms that is recognized for its speed and memory efficiency. The proposed framework utilizes SLIC and unsupervised clustering methods for segmenting greyscale images. SLIC divides the 2D and 3D images into segments having pixels (or voxels) with similar features (i.e., intensity range). Statistical features of each segment are computed and used for identifying the segment label through unsupervised clustering techniques. The unsupervised voting clustering implements a majority voting policy from multiple clustering algorithms including Hierarchical clustering and k-means clustering. A North Sea sandstone 2D X-ray image along with its SEM image were used to validate this framework. Different metrics were used to measure the accuracy of the X-ray segmentation with SEM segmentation. Our results show a mean Jaccard index of 70% and a mean Dice index of 81%. The same workflow is applied using supervoxels on a high-resolution 3D Indiana Limestone image and the results show similar accuracy margins compared to watershed segmentation. Comparison with other segmentation methods shows an average Jaccard score of 74% and an average Dice index score of 83%. To the best of our knowledge, this is the first application of superpixels over-segmentation algorithms in semantic segmentation of X-ray micro-CT images of porous media. The findings of this study highlighted the advantage of these algorithms in detecting sub-resolution porosity regions in greyscale images and obtaining accurate multi-label segmentation.
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Gao, Fei, and Jiangjiang Liu. "Face Recognition Using Segmentation Technology." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00102.

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Zhu, Donglin, Lei Li, Rui Guo, and Shifan Zhan. "Fault Detection by Using Instance Segmentation." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21249-ms.

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Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.
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Korabelnikov, Alexandr N., Alexandr V. Kolsanov, Sergey S. Chaplygin, Pavel M. Zelter, Konstantin V. Bychenkov, and Artem V. Nikonorov. "LIVER TUMOR SEGMENTATION CT DATA BASED ON ALEXNET-LIKE CONVOLUTION NEURAL NETS." In Information Technology and Nanotechnology-2016. IP Zaitsev V.D., 2016. http://dx.doi.org/10.18287/1613-0073-2016-1638-348-356.

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Anatomical structure segmentation on computed tomography (CT) is the key stage in medical visualization and computer diagnosis. Tumors are one of types of internal structures, for which the problem of automatic segmentation today has no solution fully satisfying by quality. The reason is high variance of tumor’s density and inability of using a priori anatomical information about shape. In this paper we propose automatic method of liver tumors segmentation based on convolution neural nets (CNN). Studying and validation have been performed on set of CT with liver and tumors segmentation ground truth. Average error (VOE) by cross-validation is 17.3%. Also there were considered algorithms of pre- and post-processing which increase accuracy and performance of segmentation procedure. Particularly the acceleration of the segmentation procedure with negligible decrease of quality has been reached 6 times.
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Wang, Jinguo, Na Wang, and Rui Wang. "Research on medical image segmentation technology." In 2015 3rd International Conference on Mechatronics and Industrial Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icmii-15.2015.118.

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Wang, Li, Xingxing Chen, Liangyuan Hu, and Hui Li. "Overview of Image Semantic Segmentation Technology." In 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2020. http://dx.doi.org/10.1109/itaic49862.2020.9338770.

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Doronicheva, Anna V., and Sergey Z. Savin. "WEB-technology for Medical Images Segmentation." In 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC). IEEE, 2018. http://dx.doi.org/10.1109/rpc.2018.8482234.

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Chang, Victor. "Cloud Computing for Brain Segmentation Technology." In 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2013. http://dx.doi.org/10.1109/cloudcom.2013.110.

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Cao, Fude, and Xueyun Lu. "Self-attention technology in image segmentation." In 2021 International Conference on Intelligent Traffic Systems and Smart City, edited by Fengxin Cen and Guoping Tan. SPIE, 2022. http://dx.doi.org/10.1117/12.2628135.

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Shreyas, M. S., Ashish M. Bhat, Aman Singh, and V. Shubha Rao. "Pneumothorax Segmentation." In 2020 IEEE International Conference for Innovation in Technology (INOCON). IEEE, 2020. http://dx.doi.org/10.1109/inocon50539.2020.9298200.

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Reports on the topic "Segmentation technology"

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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