• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.1-6
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• 2022

Exploration has always been an instinct of humans, and underwater life is as fascinating as it seems. So, for studying flora and fauna below water, there is a need for high-quality images. However, the underwater images tend to be of impaired quality due to various factors, which calls for improved and enhanced underwater images. There are various Histogram Equalization (HE) based techniques which could aid in solving these issues. Classifying the HE methods broadly, there is Global Histogram Equalization (GHE), Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE). Each of these HE extensions have their own pros and cons and thus, by considering them we have considered BBHE, CLAHE, BPDHE, BPDFHE, and DSIHE enhancement algorithms, which are based on Mean Brightness Preserving HE and Local HE, for this study. The performance is evaluated with non-reference performance measures like Entropy, UCIQE, UICM, and UIQM. In this study, we apply the enhancement algorithms on 300 images from the UIEB benchmark dataset and then apply the techniques of cascading fusion on the best-performing algorithms.

• 한국공간정보시스템학회:학술대회논문집
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• 2000.06a
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• pp.109-122
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• 2000

기존의 통합 시스템은 이미 구축된 자윈을 재활용하여 데이타의 구축비용을 줄이고자 하는 의도에서 연구되었다. 특히 GIS에서는 공간 데이타의 규모가 방대하므로 그 구축비용이 더욱 증가한다. 이로 인해 공간 데이타를 위한 통합 시스템의 필요성이 대두되다. 최근 웹의 사용자 수와 그 응용의 수가 증가함에 따라 웹에서의 공간 데이타를 이용한 응용의 필요성이 승가하고 있다. 이에 따라 웹 환경에서 분산된 공간 데이타의 통합 시스템이 요구된다. 기존의 미디에이터(Mediator)를 사용한 통합에 비하여 웹을 기반한 공간 데이타의 통합은 웹의 환경적 특성과 공간 데이타의 크기와 형태 등을 고려해야 한다. 본 논문에서는 OGC(OpenGIS Consortium)의 웹 매핑 기술인 WMT(Web Mapping Testbed)의 중첩 맵 서버 (Cascading Map Server)를 사용하여 공간 데이타를 통합한다. 통합 과정에서 요구되는 표준화된 데이타 모델과 인터페이스는 OGC가 최근 제안한 GML(Geography Markup Language)과 웹 맵 서버 인터페이스를 이용한다. 본 논문은 XML의 XLink와 XPointer의 개념을 가진 융합 서비스(Fusion Service)기법을 중첩 맵 서버에 도입한 융합 맵 서버(Fusion Map Server)를 제안한다. 이러한 통합 방식은 데이터 사이의 관계성을 고려한 링킹 기반 통합이므로 통합 응용의 복잡성을 줄일 수 있을 뿐만 아니라 물리적 변환 등에서 생성되는 연산 비용을 줄일 수 있는 장점이 있다.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.