Spatio-temporal TEN model and the dynamic expression of moving object based on Clifford algebra

2015 ◽  
pp. 235-237
Author(s):  
Ke Xu ◽  
Dongyan Cao ◽  
Xuedou Yu
Keyword(s):  
Clifford Algebra ◽  
Moving Object ◽  
Dynamic Expression ◽  
Object Based ◽  
Spatio Temporal

Unoccupied aerial vehicle-assisted monitoring of benthic vegetation in the coastal zone enhances the quality of ecological data

2021 ◽  
pp. 030913332110520
Author(s):  
Niels Svane ◽  
Troels Lange ◽  
Sara Egemose ◽  
Oliver Dalby ◽  
Aris Thomasberger ◽  
...  
Keyword(s):  
Zostera Marina ◽  
Spatial Scales ◽  
Fucus Vesiculosus ◽  
Ecosystem Dynamics ◽  
Spatial Extent ◽  
Total Biomass ◽  
Ecological Data ◽  
Error Matrix ◽  
Object Based ◽  
Spatio Temporal

Traditional monitoring (e.g., in-water based surveys) of eelgrass meadows and perennial macroalgae in coastal areas is time and labor intensive, requires extensive equipment, and the collected data has a low temporal resolution. Further, divers and Remotely Operated Vehicles (ROVs) have a low spatial extent that cover small fractions of full systems. The inherent heterogeneity of eelgrass meadows and macroalgae assemblages in these coastal systems makes interpolation and extrapolation of observations complicated and, as such, methods to collect data on larger spatial scales whilst retaining high spatial resolution is required to guide management. Recently, the utilization of Unoccupied Aerial Vehicles (UAVs) has gained popularity in ecological sciences due to their ability to rapidly collect large amounts of area-based and georeferenced data, making it possible to monitor the spatial extent and status of SAV communities with limited equipment requirements compared to ROVs or diver surveys. This paper is focused on the increased value provided by UAV-based, data collection (visual/Red Green Blue imagery) and Object Based Image Analysis for gaining an improved understanding of eelgrass recovery. It is demonstrated that delineation and classification of two species of SAV ( Fucus vesiculosus and Zostera marina) is possible; with an error matrix indicating 86–92% accuracy. Classified maps also highlighted the increasing biomass and areal coverage of F. vesiculosus as a potential stressor to eelgrass meadows. Further, authors derive a statistically significant conversion of percentage cover to biomass ( R2 = 0.96 for Fucus vesiculosus, R2 = 0.89 for Zostera marina total biomass, and R2 = 0.94 for AGB alone, p < 0.001). Results here provide an example of mapping cover and biomass of SAV and provide a tool to undertake spatio-temporal analyses to enhance the understanding of eelgrass ecosystem dynamics.

scholarly journals Collision Avoidance with a Moving Object Based on Q-Learning.

2003 ◽  
Vol 69 (680) ◽  
pp. 1051-1057 ◽  
Author(s):  
Masashi FURUKAWA ◽  
Michiko WATANABE ◽  
Masaharu IKEDA ◽  
Masahiro KINOSHITA ◽  
Yukinori KAKAZU
Keyword(s):  
Collision Avoidance ◽  
Moving Object ◽  
Q Learning ◽  
Object Based

VIDEO CONTENT REPRESENTATION FOR SHOT RETRIEVAL AND SCENE EXTRACTION

2001 ◽  
Vol 01 (03) ◽  
pp. 507-526 ◽  
Author(s):  
TONG LIN ◽  
HONG-JIANG ZHANG ◽  
QING-YUN SHI
Keyword(s):  
Similarity Measure ◽  
Video Content ◽  
Object Based ◽  
Scene Structure ◽  
Color Histograms ◽  
Extraction Algorithm ◽  
Content Representation ◽  
Video Shot ◽  
Shot Boundary ◽  
Spatio Temporal

In this paper, we present a novel scheme on video content representation by exploring the spatio-temporal information. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similarity measure is formulated as subshot similarity measure that serves to shot retrieval. With this shot representation, scene structure can be extracted by analyzing the splitting and merging force competitions at each shot boundary. Experimental results on real-world sports video prove that our proposed approach for video shot retrievals achieve the best performance on the average recall (AR) and average normalized modified retrieval rank (ANMRR), and Experiment on MPEG-7 test videos achieves promising results by the proposed scene extraction algorithm.

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