Results 31 to 40 of about 286,362 (268)
MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The task of crop type classification with multitemporal imagery is nowadays often done applying classifiers that are originally developed for single images like support vector machines (SVM).
T. Hoberg, S. Müller
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Conditional Random Fields for Pattern Recognition Applied to Structured Data
Algorithms, 2015 Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building ...
Tom Burr, Alexei Skurikhin
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Shallow parsing with conditional random fields [PDF]
Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03, 2003 Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods.
Fei Sha, Fernando C. N. Pereira
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Combined Conditional Random Fields Model for Supervised PolSAR Images Classification
Leida xuebao, 2017 More features and contextual information can be extracted and exploited to improve classification accuracy in complex Polarimetric Synthetic Aperture Radar (PolSAR) imagery classification.
Zou Huanxin +3 more
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Image Labeling with Markov Random Fields and Conditional Random Fields
CoRR, 2018 Most existing methods for object segmentation in computer vision are formulated as a labeling task. This, in general, could be transferred to a pixel-wise label assignment task, which is quite similar to the structure of hidden Markov random field. In terms of Markov random field, each pixel can be regarded as a state and has a transition probability ...
Shangxuan Wu, Xinshuo Weng
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Remote Sensing, 2021 In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather.
Yingying Kong +4 more
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Word Recognition with Deep Conditional Random Fields
, 2016 Recognition of handwritten words continues to be an important problem in document analysis and recognition. Existing approaches extract hand-engineered features from word images--which can perform poorly with new data sets.
Chen, Gang +2 more
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Conditional Random Fields for Metaphor Detection [PDF]
Proceedings of the Workshop on Figurative Language Processing, 2018 We present an algorithm for detecting metaphor in sentences which was used in Shared Task on Metaphor Detection by First Workshop on Figurative Language Processing. The algorithm is based on different features and Conditional Random Fields.
Anna Mosolova +2 more
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AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA).
A. R. Soares +3 more
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Conditional random fields for activity recognition [PDF]
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, 2007 Activity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we compare two models for temporal classification: hidden Markov models (HMMs), which have long been applied to the activity recognition problem, and conditional random fields ...
Douglas L. Vail +2 more
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