Extending F-Perceptory to model fuzzy objects with composite geometries for GIS

2017 ◽  
Vol 21 (6) ◽  
pp. 1364-1378 ◽  
Author(s):  
Besma Khalfi ◽  
Cyril de Runz ◽  
Sami Faiz ◽  
Herman Akdag
Keyword(s):  
Fuzzy Objects

scholarly journals Smoke recognition network based on dynamic characteristics

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092566
Author(s):  
Dahan Wang ◽  
Sheng Luo ◽  
Li Zhao ◽  
Xiaoming Pan ◽  
Muchou Wang ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
State Of The Art ◽  
The State ◽  
Detection Accuracy ◽  
Static Characteristics ◽  
Good Tool ◽  
Early Signal ◽  
Fuzzy Objects ◽  
The Difference ◽  
Smoke Recognition

Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.

scholarly journals A Model of Fuzzy Topological Relations for Simple Spatial Objects in GIS

2015 ◽  
Vol 21 (2) ◽  
pp. 389-408
Author(s):  
BO LIU ◽  
DAJUN LI ◽  
JIAN RUAN ◽  
LIBO ZHANG ◽  
LAN YOU ◽  
...  
Keyword(s):  
Fuzzy Topology ◽  
Topological Relations ◽  
Line Segments ◽  
New Model ◽  
Spatial Objects ◽  
Fuzzy Models ◽  
Fuzzy Topological Space ◽  
Fuzzy Objects ◽  
Fuzzy Regions ◽  
Geographic Information Sciences

The goal of this paper is to present a new model of fuzzy topological relations for simple spatial objects in Geographic Information Sciences (GIS). The concept of computational fuzzy topological space is applied to simple fuzzy objects to efficiently and more accurately solve fuzzy topological relations, extending and improving upon previous research in this area. Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on computational fuzzy topology. And then, we also propose a new model to compute fuzzy topological relations between simple spatial objects, an analysis of the new model exposes:(1) the topological relations of two simple crisp objects; (2) the topological relations between one simple crisp object and one simple fuzzy object; (3) the topological relations between two simple fuzzy objects. In the end, we have discussed some examples to demonstrate the validity of the new model, through an experiment and comparisons of existing models, we showed that the proposed method can make finer distinctions, as it is more expressive than the existing fuzzy models.

Fuzzy Objects

2013 ◽  
pp. 365-370
Author(s):  
Jonathan Lee ◽  
Nien-Lin Hsueh
Keyword(s):  
Fuzzy Objects

scholarly journals Spatial Co-Location Pattern Discovery from Fuzzy Objects

2017 ◽  
Vol 26 (02) ◽  
pp. 1750003 ◽  
Author(s):  
Zhiping Ouyang ◽  
Lizhen Wang ◽  
Pingping Wu
Keyword(s):  
Pattern Mining ◽  
Search Space ◽  
Pattern Generation ◽  
Optimization Techniques ◽  
Geographical Information ◽  
Location Pattern ◽  
Biomedical Image ◽  
The Past ◽  
Location Patterns ◽  
Fuzzy Objects

A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern mining (RCP), to mining co-location patterns at a membership threshold or within a membership range. For efficient SCP mining, we optimize the basic mining algorithm to accelerate the co-location pattern generation. To improve the performance of RCP mining, effective pruning strategies are developed to significantly reduce the search space. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments.

scholarly journals Lattices of fuzzy objects

1996 ◽  
Vol 19 (4) ◽  
pp. 759-766 ◽  
Author(s):  
Arturo A. L. Sangalli
Keyword(s):  
Complete Lattice ◽  
Binary Relations ◽  
Special Cases ◽  
Fuzzy Objects ◽  
Fuzzy Subgroups ◽  
Special Case ◽  
Fuzzy Subsets

The collection of fuzzy subsets of a setXforms a complete lattice that extends the complete lattice𝒫(X)of crisp subsets ofX. In this paper, we interpret this extension as a special case of the “fuzzification” of an arbitrary complete latticeA. We show how to construct a complete latticeF(A,L)–theL-fuzzificatio ofA, whereLis the valuation lattice– that extendsAwhile preserving all suprema and infima. The “fuzzy” objects inF(A,L)may be interpreted as the sup-preserving maps fromAto the dual ofL. In particular, each complete lattice coincides with its2-fuzzification, where2is the twoelement lattice. Some familiar fuzzifications (fuzzy subgroups, fuzzy subalgebras, fuzzy topologies, etc.) are special cases of our construction. Finally, we show that the binary relations on a setXmay be seen as the fuzzy subsets ofXwith respect to the valuation lattice𝒫(X).

Sign in / Sign up

Export Citation Format

Share Document