• Title/Summary/Keyword: temporal feature

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Spatial-temporal texture features for 3D human activity recognition using laser-based RGB-D videos

  • Ming, Yue;Wang, Guangchao;Hong, Xiaopeng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.3
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    • pp.1595-1613
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    • 2017
  • The IR camera and laser-based IR projector provide an effective solution for real-time collection of moving targets in RGB-D videos. Different from the traditional RGB videos, the captured depth videos are not affected by the illumination variation. In this paper, we propose a novel feature extraction framework to describe human activities based on the above optical video capturing method, namely spatial-temporal texture features for 3D human activity recognition. Spatial-temporal texture feature with depth information is insensitive to illumination and occlusions, and efficient for fine-motion description. The framework of our proposed algorithm begins with video acquisition based on laser projection, video preprocessing with visual background extraction and obtains spatial-temporal key images. Then, the texture features encoded from key images are used to generate discriminative features for human activity information. The experimental results based on the different databases and practical scenarios demonstrate the effectiveness of our proposed algorithm for the large-scale data sets.

Applying Lexical Semantics to Automatic Extraction of Temporal Expressions in Uyghur

  • Murat, Alim;Yusup, Azharjan;Iskandar, Zulkar;Yusup, Azragul;Abaydulla, Yusup
    • Journal of Information Processing Systems
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    • v.14 no.4
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    • pp.824-836
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    • 2018
  • The automatic extraction of temporal information from written texts is a key component of question answering and summarization systems and its efficacy in those systems is very decisive if a temporal expression (TE) is successfully extracted. In this paper, three different approaches for TE extraction in Uyghur are developed and analyzed. A novel approach which uses lexical semantics as an additional information is also presented to extend classical approaches which are mainly based on morphology and syntax. We used a manually annotated news dataset labeled with TIMEX3 tags and generated three models with different feature combinations. The experimental results show that the best run achieved 0.87 for Precision, 0.89 for Recall, and 0.88 for F1-Measure in Uyghur TE extraction. From the analysis of the results, we concluded that the application of semantic knowledge resolves ambiguity problem at shallower language analysis and significantly aids the development of more efficient Uyghur TE extraction system.

Manufacturing Feature Extraction for Sculptured Pocket Machining (Sculptured 포켓 가공을 위한 가공특징형상 추출)

  • 주재구;조현보
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.04a
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    • pp.455-459
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    • 1997
  • A methodology which supports the feature used from design to manufacturing for sculptured pocket is newly devlored and present. The information contents in a feature can be easily conveyed from one application to another in the manufacturing domain. However, the feature generated in one application may not be directly suitable for another whitout being modified with more information. Theobjective of the paper is to parsent the methodology of decomposing a bulky feature of sculptured pocket to be removed into compact features to be efficiently machined. In particular, the paper focuses on the two task: 1) to segment horizontally a bulky feature into intermediate features by determining the adequate depth of cut and cutter size and to generate the temporal precedence graph of the intermediate features and 2)to further decompose each intermediate feature vertical into smaller manufacturing features and to apply the variable feed rate to each small feature. The proposed method will provid better efficiency in machining time and cost than the classical method which uses a long string of NC codes necessary to remove a bulky fecture.

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Recognition of Korean Connected Digit Telephone Speech Using the Training Data Based Temporal Filter (훈련데이터 기반의 temporal filter를 적용한 4연숫자 전화음성 인식)

  • Jung, Sung-Yun;Bae, Keun-Sung
    • MALSORI
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    • no.53
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    • pp.93-102
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    • 2005
  • The performance of a speech recognition system is generally degraded in telephone environment because of distortions caused by background noise and various channel characteristics. In this paper, data-driven temporal filters are investigated to improve the performance of a specific recognition task such as telephone speech. Three different temporal filtering methods are presented with recognition results for Korean connected-digit telephone speech. Filter coefficients are derived from the cepstral domain feature vectors using the principal component analysis. According to experimental results, the proposed temporal filtering method has shown slightly better performance than the previous ones.

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A Real-time Dual-mode Temporal Synchronization and Compensation based on Reliability Measure in Stereoscopic Video (3D 입체 영상 시스템에서 신뢰도를 활용한 듀얼 모드 실시간 동기 에러 검출 및 보상 방법)

  • Kim, Giseok;Cho, Jae-Soo;Lee, Gwangsoon;Lee, Eung-Don
    • Journal of Broadcast Engineering
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    • v.19 no.6
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    • pp.896-906
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    • 2014
  • In this paper, a real-time dual-mode temporal synchronization and compensation method based on a new reliability measure in stereoscopic video is proposed. The goal of temporal alignment is to detect the temporal asynchrony and recover synchronization of the two video streams. The accuracy of the temporal synchronization algorithm depends on the 3DTV contents. In order to compensate the temporal synchronization error, it is necessary to judge whether the result of the temporal synchronization is reliable or not. Based on our recently developed temporal synchronization method[1], we define a new reliability measure for the result of the temporal synchronization method. Furthermore, we developed a dual-mode temporal synchronization method, which uses a usual texture matching method and the temporal spatiogram method[1]. The new reliability measure is based on two distinctive features, a dynamic feature for scene change and a matching distinction feature. Various experimental results show the effectiveness of the proposed method. The proposed algorithms are evaluated and verified through an experimental system implemented for 3DTV.

Automatic Co-registration of Cloud-covered High-resolution Multi-temporal Imagery (구름이 포함된 고해상도 다시기 위성영상의 자동 상호등록)

  • Han, You Kyung;Kim, Yong Il;Lee, Won Hee
    • Journal of Korean Society for Geospatial Information Science
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    • v.21 no.4
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    • pp.101-107
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    • 2013
  • Generally the commercial high-resolution images have their coordinates, but the locations are locally different according to the pose of sensors at the acquisition time and relief displacement of terrain. Therefore, a process of image co-registration has to be applied to use the multi-temporal images together. However, co-registration is interrupted especially when images include the cloud-covered regions because of the difficulties of extracting matching points and lots of false-matched points. This paper proposes an automatic co-registration method for the cloud-covered high-resolution images. A scale-invariant feature transform (SIFT), which is one of the representative feature-based matching method, is used, and only features of the target (cloud-covered) images within a circular buffer from each feature of reference image are used for the candidate of the matching process. Study sites composed of multi-temporal KOMPSAT-2 images including cloud-covered regions were employed to apply the proposed algorithm. The result showed that the proposed method presented a higher correct-match rate than original SIFT method and acceptable registration accuracies in all sites.

Spatio-Temporal Semantic Sensor Web based on SSNO (SSNO 기반 시공간 시맨틱 센서 웹)

  • Shin, In-Su;Kim, Su-Jeong;Kim, Jeong-Joon;Han, Ki-Joon
    • Spatial Information Research
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    • v.22 no.5
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    • pp.9-18
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    • 2014
  • According to the recent development of the ubiquitous computing environment, the use of spatio-temporal data from sensors with GPS is increasing, and studies on the Semantic Sensor Web using spatio-temporal data for providing different kinds of services are being actively conducted. Especially, the W3C developed the SSNO(Semantic Sensor Network Ontology) which uses sensor-related standards such as the SWE(Sensor Web Enablement) of OGC and defines classes and properties for expressing sensor data. Since these studies are available for the query processing about non-spatio-temporal sensor data, it is hard to apply them to spatio-temporal sensor data processing which uses spatio-temporal data types and operators. Therefore, in this paper, we developed the SWE based on SSNO which supports the spatio-temporal sensor data types and operators expanding spatial data types and operators in "OpenGIS Simple Feature Specification for SQL" by OGC. The system receives SensorML(Sensor Model Language) and O&M (Observations and Measurements) Schema and converts the data into SSNO. It also performs the efficient query processing which supports spatio-temporal operators and reasoning rules. In addition, we have proved that this system can be utilized for the web service by applying it to a virtual scenario.

Spatio-Temporal Analysis of Trajectory for Pedestrian Activity Recognition

  • Kim, Young-Nam;Park, Jin-Hee;Kim, Moon-Hyun
    • Journal of Electrical Engineering and Technology
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    • v.13 no.2
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    • pp.961-968
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    • 2018
  • Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

Capacitated Fab Scheduling Approximation using Average Reward TD(${\lambda}$) Learning based on System Feature Functions (시스템 특성함수 기반 평균보상 TD(${\lambda}$) 학습을 통한 유한용량 Fab 스케줄링 근사화)

  • Choi, Jin-Young
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.34 no.4
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    • pp.189-196
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    • 2011
  • In this paper, we propose a logical control-based actor-critic algorithm as an efficient approach for the approximation of the capacitated fab scheduling problem. We apply the average reward temporal-difference learning method for estimating the relative value functions of system states, while avoiding deadlock situation by Banker's algorithm. We consider the Intel mini-fab re-entrant line for the evaluation of the suggested algorithm and perform a numerical experiment by generating some sample system configurations randomly. We show that the suggested method has a prominent performance compared to other well-known heuristics.

Human Motion Recognition Based on Spatio-temporal Convolutional Neural Network

  • Hu, Zeyuan;Park, Sange-yun;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.23 no.8
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    • pp.977-985
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    • 2020
  • Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.