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Next Integrated Result Modelling for Stopping the Text Field Recognition Process in a Video Using a Result Model with Per-Character Alternatives 12

Next Integrated Result Modelling for Stopping the Text Field...

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12th International Conference on Machine Vision (ICMV)

作者： Bulatov, Konstantin Savelyev, Boris Arlazarov, Vladimir V. Moscow Inst Phys & Technol MIPT Moscow Russia Smart Engines Moscow Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia

ISBN: (纸本)9781510636446

In the field of document analysis and recognition using mobile devices for capturing, and the field of object recognition in a video stream, an important problem is determining the time when the capturing process should be stopped. Efficient stopping influences not only the total time spent for performing recognition and data entry, but the expected accuracy of the result as well. This paper is directed on extending the stopping method based on next integrated recognition result modelling, in order for it to be used within a string result recognition model with per-character alternatives. The stopping method and notes on its extension are described, and experimental evaluation is performed on an open dataset MIDV-500. The method was compares with previously published methods based on input observations clustering. The obtained results indicate that the stopping method based on the next integrated result modelling allows to achieve higher accuracy, even when compared with the best achievable configuration of the competing methods.

关键词： recognition in video stream mobile OCR stopping rules decision making mobile document recognition anytime algorithms

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Incorporating Linguistic Post-Processing Into Whole-Book Recognition

Incorporating Linguistic Post-Processing Into Whole-Book Rec...

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Conference on Document Recognition and Retrieval XVII

作者： Xiu, Pingping Baird, Henry S. Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18017 USA

ISBN: (纸本)9780819479273

We describe a technique of linguistic post-processing of whole-book recognition results. Whole-book recognition is a technique that improves recognition of book images using fully automatic cross-entropy-based model adaptation. In previous published works, word recognition was performed on individual words separately, without awaring passage-level information such as word-occurrence frequencies. Therefore, some rare words in real texts may appear much more often in recognition results;vice versa. Differences between word frequencies in recognition results and in prior knowledge may indicate recognition errors on a long passage. In this paper, we propose a post-processing technique to enhance whole-book recognition results by minimizing differences between word frequencies in recognition results and prior word frequencies. This technique works better when operating on longer passages, and it drives the character error rate down 20% from 1.24% to 0.98% in a 90-page experiment.

关键词： document image recognition book recognition isogeny adaptive classification anytime algorithms model adaptation

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An Adaptive Hierarchical Method for anytime Set-wise Clustering of Variable and High-Speed Data Streams

An Adaptive Hierarchical Method for Anytime Set-wise Cluster...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Aarti Challa, Jagat Sesh Darolia, Utkarsh Chandak, Mridul Goyal, Poonam Goyal, Navneet Birla Institute of Technology & Science Adapt Lab Dept. of Computer Science Pilani Campus Pilani333031 India

ISBN: (纸本)9798350324457

Set-wise Clustering is a clustering technique for data streams that groups sets of objects based on distribution patterns, applicable in contexts like retail chain clustering, text-based community clustering, restaurant categorization, etc. The existing set-wise clustering method cannot handle variable and high-speed streams with reasonable accuracy. This paper presents an anytime Set-wise Clustering method for data streams known as ANYSETCLUS. The method handles the variable inter-arrival rates of stream objects using a proposed indexing structure called AnySetClusTree, which stores a hierarchy of micro-clusters of multi-set entities at varying granularity. ANYSETCLUS is highly adaptive as it supports incremental model updates, segregates outliers, enables outlier-to-concept transition, and captures concept drift. The method also enables anytime offline clustering wherein it can generate multiple clusterings of varying granularity and purity depending upon the available time allowance for final clustering. The experimental results affirm the superior efficacy of the proposed method in handling variable and high-speed streams compared to the state-of-the-art method. The experimental results also showcase its effectiveness in achieving significantly higher micro-cluster purity for low and high-speed streams. This contrasts with the state-of-the-art method, which is unable to generate valid clustering results for high-speed streams. The experiments further validate the proposed method's capability for anytime offline clustering. © 2023 IEEE.

关键词： anytime algorithms Data Clustering Data Streams

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Gradual discovery with closure structure of a concept lattice 15th

Gradual discovery with closure structure of a concept lattic...

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15th International Conference on Concept Lattices and Their Applications, CLA 2020

作者： Makhalova, Tatiana Kuznetsov, Sergei O. Napoli, Amedeo Universite de Lorraine CNRS Inria LORIA NancyF-54000 France National Research University Higher School of Economics Moscow Russia

An approximate discovery of closed itemsets is usually based on either setting a frequency threshold or computing a sequence of projections. Both approaches, being incremental, do not provide any estimate of the size of the next output and do not ensure that "more interesting patterns" will be generated first. We propose to generate closed itemsets incrementally, w.r.t. the size of the smallest (cardinality-minimal or minimum) generators and show that this approach (i) exhibits anytime property, and (ii) first generates itemsets of better quality and then those of lower quality. © 2020 CEUR-WS. All rights reserved.

关键词： anytime algorithms Closed itemsets Pattern Mining

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anytime Computation of Cautious Consequences in Answer Set Programming

Anytime Computation of Cautious Consequences in Answer Set P...

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30th International Conference on Logic Programming

作者： Alviano, Mario Dodaro, Carmine Ricca, Francesco Univ Calabria Dept Math & Comp Sci I-87036 Arcavacata Di Rende CS Italy

Query answering in Answer Set Programming (ASP) is usually solved by computing (a subset of) the cautious consequences of a logic program. This task is computationally very hard, and there are programs for which computing cautious consequences is not viable in reasonable time. However, current ASP solvers produce the (whole) set of cautious consequences only at the end of their computation. This paper reports on strategies for computing cautious consequences, also introducing anytime algorithms able to produce sound answers during the computation.

关键词： answer set programming query answering cautious reasoning anytime algorithms

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Fast anytime retrieval with confidence in large-scale temporal case bases

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KNOWLEDGE-BASED SYSTEMS 2020年 206卷 106374-106374页

作者： Oguz Mulayim, Mehmet Lluis Arcos, Josep Spanish Natl Res Council Artificial Intelligence Res Inst IIIA CSIC Campus UAB Barcelona 08193 Spain

This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm in CBR retrieval. However, brute-force kNN is impossible for large CBs. As a contribution to efforts for speeding up kNN search, we introduce an anytime kNN search methodology and algorithm. anytime Lazy kNN finds exact kNNs when allowed to run to completion with remarkable gain in execution time by avoiding unnecessary neighbor assessments. For applications where the gain in exact kNN search may not suffice, it can be interrupted earlier and it returns best-so-far kNNs together with a confidence value attached to each neighbor. We describe the algorithm and methodology to construct a probabilistic model that we use both to estimate confidence upon interruption and to automatize the interruption at desired confidence thresholds. We present the results of experiments conducted with publicly available datasets. The results show superior gains compared to brute-force search. We reach to an average gain of 87.18% with 0.98 confidence and to 96.84% with 0.70 confidence. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Large-scale case-based reasoning Exact and approximate k-nearest neighbor search anytime algorithms

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Reconfiguration of IIR Filters in Response to Computer Resource Availability

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2009年第1期9卷 2-2页

作者： Ferri, B. H. Ferri, A. A. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

This article explores methods to reconfigure infinite impulse response (IIR) filters in processes that utilize computer resource management. A high-performance mode uses a full-length IIR filter, while low resources or a desire to conserve power triggers a low-performance mode where a smaller length filter is used. Two alternatives for algorithms are explored: switching between filter banks of varying lengths, and anytime filters where the later stages can be skipped if computational resources are scarce. Two main challenges are addressed in this article. The first is how to configure an IIR filter as an anytime algorithm, and the second is how to initialize the states of the filters in order to mitigate transients induced by reconfiguration. The performance and feasibility of several common transient management schemes are investigated for use in this application. It is found that an anytime IIR filter outperforms the filter banks in almost every case studied due to its inherently smaller transients and the fact that the reconfiguration happens at the speed of resource management and not at a lower speed typically associated with reconfiguration due to physical system properties.

关键词： algorithms Reconfigurable computing digital filters anytime algorithms resource management imprecise computing

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Game Theoretic Controller Synthesis for Multi-Robot Motion Planning-Part II: Policy-based algorithms ∗

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IFAC-PapersOnLine 2015年第22期48卷 168-173页

作者： Devesh K. Jha Minghui Zhu Asok Ray Mechanical & Nuclear Engineering Department University Park PA. 16802 USA Electrical Engineering Department University Park PA 16802 USA Mechanical & Nuclear Engineering Department University Park PA 16802 USA

This paper presents the problem of distributed feedback motion planning for multiple robots. The problem of feedback multi-robot motion planning is formulated as a differential non-cooperative game. We leverage the existing sampling-based algorithms and value iterations to develop an incremental policy synthesizer. The proposed algorithm makes use of an iterative best response algorithm to incrementally improve the estimate of value functions of the individual robots in the multi-robot motion-planning setting. We show the asymptotic convergence of the limiting policies induced by the proposed Feedback iNash-Policy algorithm for the underlying non-cooperative game. Furthermore, we show that the value iterations allow estimation of the cost-to-go functions for the robots without the requirement on convergence of the value functions for the sampled graph at any particular iteration.

关键词： Motion planning Multiple robots anytime algorithms Non-cooperative games

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Towards monitored tomographic reconstruction: algorithm-dependence and convergence

COMPUTER OPTICS

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COMPUTER OPTICS 2023年第4期47卷 658-667页

作者： Bulatov, K. B. Ingacheva, A. S. Gilmanov, M. I. Kutukova, K. Soldatova, Z. V. Buzmakov, A. V. Chukalina, M. V. Zschech, E. Arlazarov, V. V. Smart Engines Serv LLC Prospekt 60 Letiia Oktiabria 9 Moscow 117312 Russia FRC Comp Sci & Control RAS Prospekt 60 Letiia Oktiabria 9 Moscow 119333 Russia Inst Informat Transmiss Problems Kharkevich Inst R Bolshoi Karetnyi Pereulok 19-1 Moscow 127051 Russia DeepXscan GmbH Zeppelinstr 1 Dresden Germany FSRC Crystallog & Photon RAS Leninskii Prospekt 59 Moscow 119333 Russia

The monitored tomographic reconstruction (MTR) with optimized photon flux technique is a pioneering method for X-ray computed tomography (XCT) that reduces the time for data acquisition and the radiation dose. The capturing of the projections in the MTR technique is guided by a scanning protocol built on similar experiments to reach the predetermined quality of the reconstruction. This method allows achieving a similar average reconstruction quality as in ordinary tomography while using lower mean numbers of projections. In this paper, we, for the first time, systematically study the MTR technique under several conditions: reconstruction algorithm (FBP, SIRT, SIRT-TV, and others), type of tomography setup (micro-XCT and nano-XCT), and objects with different morphology. It was shown that a mean dose reduction for reconstruction with a given quality only slightlyvaries with choice of reconstruction algorithm, and reach up to 12.5 % in case of micro-XCT and 8.5 % for nano-XCT. The obtained results allow to conclude that the monitored tomographic reconstruction approach can be universally combined with an algorithm of choice to perform a controlled trade-off between radiation dose and image quality. Validation of the protocol on independent common ground truth demonstrated a good convergence of all reconstruction algorithms within the MTR protocol.

关键词： anytime algorithms monitored tomographic reconstruction micro X-ray computed tomography nano X-ray computed tomography dose reduction time reducing stopping rule

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Weighted combination of per-frame recognition results for text recognition in a video stream

COMPUTER OPTICS

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COMPUTER OPTICS 2021年第1期45卷 77-89页

作者： Petrova, O. Bulatov, K. Arlazarov, V. V. Arlazarov, V. L. FRC CSC RAS Moscow Russia Smart Engines Serv LLC Moscow Russia State Univ Moscow Inst Phys & Technol Moscow Russia

The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.

关键词： mobile OCR video stream anytime algorithms weighted combination ensemble methods

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