Segmentation

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  • In Chapter 1 we present in detail a framework for fully automated brain tissue classification. The framework consists of a sequence of fully automated state of the art image registration (both rigid and nonrigid) and image segmentation algorithms. Models of the spatial distribution of brain tissues are combined with models of expected tissue intensities, including correction of MR bias fields and estimation of partial voluming. We also demonstrate how this framework can be applied in the presence of lesions....

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  • The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. This new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years.

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  • Tham khảo bài thuyết trình 'chapter 6: market segmentation and the marketing mix: determinants of advertising strategy', kinh doanh - tiếp thị, internet marketing phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Bài giảng Hệ điều hành: Chương 7 - Paging Segmentation cung cấp cho các bạn những kiến thức về kỹ thuật phân trang (paging); kỹ thuật phân đoạn (segmentation); segmentation with paging trong hệ điều hành. Bài giảng phục vụ cho các bạn chuyên ngành Công nghệ thông tin và những ngành có liên quan.

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  • Using Firewalls to Segment Internal Resources Perhaps the most overlooked implementation of a firewall is on the internal network.

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  • CNG Hiệp Phước, tính kích thước đường ống, lựa chọn máy nén, lựa chọn van, PFD Công nghệ CNG, hệ nhiệt động PR là những nội dung chính trong bài giảng "CNG Trạm Hiệp Phước 50 triệu m3/năm: Sử dụng Pipe Segment". Mời các bạn cùng tham khảo nội dung bài giảng để nắm bắt thông tin chi tiết.

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  • After completing this lesson, you should be able to do the following: Describe the concept of automatic undo management Create and maintain the automatic managed undo tablespace Set the retention period Use dynamic performance views to check rollback segment performance Reconfigure and monitor rollback segments Define the number and sizes of rollback segments Allocate rollback segments to transactions

    ppt36p trinh02 28-01-2013 20 4   Download

  • This paper describes an unsupervised dynamic graphical model for morphological segmentation and bilingual morpheme alignment for statistical machine translation. The model extends Hidden Semi-Markov chain models by using factored output nodes and special structures for its conditional probability distributions. It relies on morpho-syntactic and lexical source-side information (part-of-speech, morphological segmentation) while learning a morpheme segmentation over the target language. Our model outperforms a competitive word alignment system in alignment quality. ...

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  • Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, an HMM is used to model text at the document level. This modelling might cause undesired redundancy in extraction in the sense that more than one filler is identified and extracted. We propose to use HMMs to model text at the segment level, in which the extraction process consists of two steps: a segment retrieval step followed by an extraction step. ...

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  • This chapter define the three steps of target marketing: market segmentation, market targeting, and market positioning; list and discuss the major bases for segmenting consumer and business markets; explain how companies identify attractive market segments and choose a target marketing strategy; discuss how companies position their products for maximum competitive advantage in the marketplace.

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  • Chapter 9: Segmentation, targeting, and positioning. After reading this chapter, you should be able to: Outline the different methods of segmenting a market; describe how firms determine whether a segment is attractive and therefore worth pursuing; articulate the differences among targeting strategies: undifferentiated, differentiated, concentrated, or micromarketing; determine the value proposition.

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  • The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efficient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efficiency and effectiveness.

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  • Lots of Chinese characters are very productive in that they can form many structured words either as prefixes or as suffixes. Previous research in Chinese word segmentation mainly focused on identifying only the word boundaries without considering the rich internal structures of many words. In this paper we argue that this is unsatisfying in many ways, both practically and theoretically. Instead, we propose that word structures should be recovered in morphological analysis.

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  • We describe a method for disambiguating Chinese commas that is central to Chinese sentence segmentation. Chinese sentence segmentation is viewed as the detection of loosely coordinated clauses separated by commas. Trained and tested on data derived from the Chinese Treebank, our model achieves a classification accuracy of close to 90% overall, which translates to an F1 score of 70% for detecting commas that signal sentence boundaries.

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  • We experiment with extending a lattice parsing methodology for parsing Hebrew (Goldberg and Tsarfaty, 2008; Golderg et al., 2009) to make use of a stronger syntactic model: the PCFG-LA Berkeley Parser. We show that the methodology is very effective: using a small training set of about 5500 trees, we construct a parser which parses and segments unsegmented Hebrew text with an F-score of almost 80%, an error reduction of over 20% over the best previous result for this task.

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  • In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words. We describe our strategies that yield good balance for learning the characteristics of known and unknown words and propose an errordriven policy that delivers such balance by acquiring examples of unknown words from particular errors in a training corpus.

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  • Manually annotated corpora are valuable but scarce resources, yet for many annotation tasks such as treebanking and sequence labeling there exist multiple corpora with different and incompatible annotation guidelines or standards. This seems to be a great waste of human efforts, and it would be nice to automatically adapt one annotation standard to another. We present a simple yet effective strategy that transfers knowledge from a differently annotated corpus to the corpus with desired annotation.

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  • This paper shows the results of an experiment in dialogue segmentation. In this experiment, segmentation was done on a level of analysis similar to adjacency pairs. The method of annotation was somewhat novel: volunteers were invited to participate over the Web, and their responses were aggregated using a simple voting method. Though volunteers received a minimum of training, the aggregated responses of the group showed very high agreement with expert opinion.

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  • Morphological processes in Semitic languages deliver space-delimited words which introduce multiple, distinct, syntactic units into the structure of the input sentence. These words are in turn highly ambiguous, breaking the assumption underlying most parsers that the yield of a tree for a given sentence is known in advance. Here we propose a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.

    pdf9p hongphan_1 15-04-2013 21 3   Download

  • We propose a cascaded linear model for joint Chinese word segmentation and partof-speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efficiently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved accuracies on both segmentation only and joint segmentation and part-of-speech tagging. On the Penn Chinese Treebank 5.0, we obtain an error reduction of 18.

    pdf8p hongphan_1 15-04-2013 26 3   Download

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