Classification of Building Images in Video Sequences

2014 ◽  
pp. 64-68
Keyword(s):  
Fuzzy Rule ◽  
Weather Conditions ◽  
Severe Weather ◽  
Video Sequences ◽  
Rule Based ◽  
Region Detection ◽  
Lighting Conditions ◽  
Illumination Changes ◽  
Outdoor Environments

A technique for detection of building images in real-world video sequences is presented. The proposed technique uses information extracted from video features to improve precision in classification results. It combines fuzzy rule-based classification with a method for changing region detection in outdoor environments, which is invariant to extreme illumination changes and severe weather conditions. It has been tested on sequences under various lighting conditions. Satisfactory and promising results have been achieved.

Application of Improved Illumination Invariance Algorithm in Building Detection

2014 ◽  
pp. 23-32
Keyword(s):  
Fuzzy Rule ◽  
Video Sequences ◽  
Building Detection ◽  
Rule Based ◽  
Region Detection ◽  
Lighting Conditions ◽  
Outdoor Scenes ◽  
Edge Distribution ◽  
Edge Based

The problem of edge-based classification of natural video sequences containing buildings and captured under changing lighting conditions is addressed. The introduced approach is derived from two empiric observations: In static regions the likelihood of finding features that match the patterns of “buildings” is high because buildings are rigid static objects, and misclassification can be reduced by filtering out image regions changing or deforming in time. These regions may contain objects semantically different to buildings but with a highly similar edge distribution (e.g., high frequency of vertical and horizontal edges). Using these observations, a strategy is devised in which a fuzzy rule-based classification technique is combined with a method for changing region detection in outdoor scenes. The efficiency of the described techniques is implemented and tested with sequences showing changes in the lighting conditions.

scholarly journals Fuzzy Rule–Based Approach for Detection of Bounded Weak-Echo Regions in Radar Images

2006 ◽  
Vol 45 (9) ◽  
pp. 1304-1312 ◽  
Author(s):  
Nikhil R. Pal ◽  
Achintya K. Mandal ◽  
Srimanta Pal ◽  
Jyotirmay Das ◽  
V. Lakshmanan
Keyword(s):  
Fuzzy Rule ◽  
Radar Data ◽  
Severe Weather ◽  
Training Set ◽  
Rule Based ◽  
Radar Images ◽  
Spatial Properties ◽  
Linguistic Rules ◽  
Rule Based Approach

Abstract A method for the detection of a bounded weak-echo region (BWER) within a storm structure that can help in the prediction of severe weather phenomena is presented. A fuzzy rule–based approach that takes care of the various uncertainties associated with a radar image containing a BWER has been adopted. The proposed technique automatically finds some interpretable (fuzzy) rules for classification of radar data related to BWER. The radar images are preprocessed to find subregions (or segments) that are suspected candidates for BWERs. Each such segment is classified into one of three possible cases: strong BWER, marginal BWER, or no BWER. In this regard, spatial properties of the data are being explored. The method has been tested on a large volume of data that are different from the training set, and the performance is found to be very satisfactory. It is also demonstrated that an interpretation of the linguistic rules extracted by the system described herein can provide important characteristics about the underlying process.

Fuzzy rule based classification of polarimetric SAR data

2002 ◽  
Vol 6 (3) ◽  
pp. 217-232 ◽  
Author(s):  
Martin Hellmann ◽  
Gunther Jäger
Keyword(s):  
Fuzzy Rule ◽  
Polarimetric Sar ◽  
Rule Based ◽  
Sar Data

scholarly journals Self-Organizing Fuzzy Rule-Based Approach for Dealing with the Classification of Indoor Environments for IoT Applications

2019 ◽  
Author(s):  
Ualison Dias ◽  
Eduardo Aguiar ◽  
Michel Hell ◽  
Alvaro Medeiros ◽  
Daniel Silveira
Keyword(s):  
Fuzzy Rule ◽  
Indoor Environments ◽  
Rule Based ◽  
Iot Applications ◽  
Rule Based Approach ◽  
Self Organizing

Atualmente, grande parte dos sensores utilizados em Internet das Coisas adota tecnologia sem fio, a fim de facilitar a construção de redes de sensoriamento. Neste sentido, a classificação do tipo de ambiente no qual estes sensores estão localizados exerce um importante papel no desempenho de tais redes de sensoriamento, uma vez que pode ser utilizada na determinação de níveis mais eficientes de consumo de energia dos sensores que as compõe. Assim, neste trabalho é apresentada uma abordagem baseada em Classificadores Fuzzy Auto-organizáveis para a classificação de ambientes internos a partir de medições em tempo real do sinal de radiofrequência de uma rede de sensoriamento sem fio em um ambiente real. Os resultados experimentais apresentados mostram que a abordagem proposta obteve alto desempenho com baixo custo computacional na solução do problema apresentado.

Fuzzy rule-based classification of atmospheric circulation patterns

1995 ◽  
Vol 15 (10) ◽  
pp. 1087-1097 ◽  
Author(s):  
Andras Bardossy ◽  
Lucien Duckstein ◽  
Istvan Bogardi
Keyword(s):  
Atmospheric Circulation ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns

Multi-criteria classification of spare parts inventories – a web based approach

2015 ◽  
Vol 21 (4) ◽  
pp. 456-477 ◽  
Author(s):  
S. P. Sarmah ◽  
U. C. Moharana
Keyword(s):  
Fuzzy Rule ◽  
Spare Parts ◽  
Multiple Criteria ◽  
Classification Model ◽  
Web Based ◽  
Rule Based ◽  
Content Type ◽  
Total Cost ◽  
Rule Based Approach

Purpose – The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation. Design/methodology/approach – Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models. Findings – Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs. Practical implications – The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications. Originality/value – The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

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