PATTERN RECOGNITION AND IMAGE ANALYSIS最新文献

英文中文

Theoretical Foundations, Methods, and Algorithms for Lossless-in-Sense Text Compression 无损文本压缩的理论基础、方法和算法

IF 1 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

PATTERN RECOGNITION AND IMAGE ANALYSIS

Pub Date : 2023-12-01 DOI: 10.1134/s1054661823040144

Abstract

The article is devoted to the scientific school developed by the first author in 1995–2012 in Yaroslav-the-Wise Novgorod State University (Veliky Novgorod, Russia). The finite practical goal of the research carried out by the school can be denoted here as the revelation of the most rational variant for sense transfer in a knowledge unit defined by a set of semantically equivalent natural-language phrases. One phrase here corresponds to the simple spread natural-language sentence (according to the “Meaning–Text” theory terminology). Knowledge formed herewith about synonymy and forms of language expression of relationships between concepts of some topical area are in demand in tasks requiring the establishment of full or partial equivalence in the meaning of both complete sentences of natural language and their combinations, and individual fragments of phrases. The results are both theoretical and practical in nature. Offered methods and their software implementations can be used for decision of a wide range of tasks of recognition and analysis of semantics of complex information objects (texts and images at first), and for lossless-in-sense information compression.

摘要本文介绍了第一作者于1995-2012年在雅罗斯拉夫-智者诺夫哥罗德国立大学（俄罗斯大诺夫哥罗德）建立的科学学院。该学院所开展研究的有限实践目标在此可以表示为，在由一组语义等同的自然语言短语定义的知识单元中，揭示意义转移的最合理变体。这里的一个短语对应于简单的传播自然语言句子（根据 "意义-文本 "理论术语）。在要求建立完整的自然语言句子及其组合以及短语单个片段的全部或部分等同意义的任务中，需要用到由此形成的有关同义词和某些专题领域概念之间关系的语言表达形式的知识。这些成果既有理论性，也有实用性。所提供的方法及其软件实现可用于对复杂信息对象（首先是文本和图像）的语义进行识别和分析，以及进行无损意义信息压缩。

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引用次数: 0

Application of Cascade Methods as a Universal Object Detection Tool 应用级联方法作为通用物体检测工具

IF 1 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

PATTERN RECOGNITION AND IMAGE ANALYSIS

Pub Date : 2023-12-01 DOI: 10.1134/s1054661823040302

Abstract

This paper is devoted to a review of the achievements of the Moscow scientific school of image recognition, formed under the leadership of Professor Vladimir L’vovich Arlazarov, in the field of development and application of the Viola–Jones method. One of the main areas of research at the school is the development of computationally efficient recognition algorithms, which requires a deep understanding of the problem and a wide expertise in the field of existing classical algorithms. Such classic method as the Viola—Jones method became an essential tool to solve a wide range of image recognition problems. This paper provides an overview of the modifications of the original method developed by the scientific school and describes in detail the experience of solving many different practical problems that arise in the development of modern energy-efficient image recognition systems.

摘要本文旨在回顾在弗拉基米尔-列沃维奇-阿尔拉扎罗夫教授领导下成立的莫斯科图像识别科学学院在开发和应用 Viola-Jones 方法领域所取得的成就。该学院的主要研究领域之一是开发计算效率高的识别算法，这需要对问题有深刻的理解，并在现有经典算法领域拥有广泛的专业知识。Viola-Jones 方法等经典方法已成为解决各种图像识别问题的基本工具。本文概述了科学院开发的原始方法的修改情况，并详细介绍了解决现代高能效图像识别系统开发过程中出现的许多不同实际问题的经验。

引用次数: 0

Assessing the Quality of Customer Service Based on the Emotional Satisfaction of Clients Using Artificial Immune System Technologies 基于人工免疫系统技术的客户情感满意度评价客户服务质量