An electronic video camera image stabilizer operated on fuzzy theory

Egusa, Yuka; Akatsuka, Hiroshi; Morimura, A.; Wakami, Noboru

doi:10.1109/fuzzy.1992.258769

Cited by 16 publications

(2 citation statements)

References 3 publications

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“…15 Fuzzy sets have advanced in a wide variety of disciplines, such as control theory, topology, linguistics, optimization, category theory, and automata. 16 Perhaps the first paper explicitly linking the theories of fuzzy and statistical uncertainty is that ofZadeh.…”

Section: Fuzzv Logic Data Analysis Systemmentioning

confidence: 99%

Communication Interference Rejection with Fuzzy Logic

Tseng¹,

Kung²

1997

Intelligent Automation & Soft Computing

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The ever increasing use of wireless technology calls for an effective methodology to cope with the interference rejection problem. In this work, fuzzy logic is used to eliminate the undesirable signal while preserving the original transmitted one. A case study on the interference rejection in wireless temperature transmission is used to illustrate the performance of the fuzzy filtering methodology.

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Section: Fuzzv Logic Data Analysis Systemmentioning

confidence: 99%

Communication Interference Rejection with Fuzzy Logic

Tseng¹,

Kung²

1997

Intelligent Automation & Soft Computing

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“…If the object invaded into the scene for the first time, it will lead to mismatch. Some methods were proposed in [1][2][3][4]. The motion vectors(MV) are estimated in the sub-image of the frame, and median filter or fuzzy theory is applied to determine which is the best for global MV.…”

Section: Introductionmentioning

confidence: 99%

Motion classification for image stabilization with moving objects

Zhou

2008

SPIE Proceedings

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Feature based image stabilization is often undermined by moving object. A new method to classify motion vector based on K-Medoids cluster is proposed in this paper for removing the influence of moving objects in the scene while image stabilizing. In the proposed method, the image is divided into several small areas before features being extracted from the regions firstly. Features are tracked by finding the corresponding ones in the next frame along the image sequence. Then a K-Medoids cluster method is proposed to classify those features by their motion vector to distinguish them from background or from moving objects. A dispersivity value of internal distance among those features in one class is defined to confirm that which cluster is from background. The experiment shows that the result is good to remove the features from moving objects. The limitation number of total feature and cluster assures the running speed fast enough for real-time processing on PC.

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