An application of fuzzy set theory for an electronic video camera image stabilizer

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

doi:10.1109/91.413239

Cited by 41 publications

(20 citation statements)

References 4 publications

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“…There are many algorithms to calculate the LMV such as Block matching algorithm [10], Edge pattern matching [11], Bit-plane matching [12] [13], representative point matching [1] [14] and others [15] [16].After that, the GMV can be calculated by using the summation of absolute difference [1] or another way, for example the median-based motion correction [17]. The description for each method follows as:…”

Section: The Motion Estimation Unitmentioning

confidence: 99%

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Visual quality improvement of digital video by stabilization using adaptive CMAC filtering

Zahoor

Koodtalang

Shahid

et al. 2012

2012 6th International Conference on Signal Processing and Communication Systems

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A Digital Video Stabilization (DV S) system removes the unwanted shaking in the videos acquired by hand-held cameras and preserves the panning. In this thesis work, a digital video stabilization model is proposed based upon adaptive cerebellar model articulation controller (CM AC) filtering. A CMAC is a manifestation of the associative memory learning structure present in the cerebellum of human being. Adaptive CMAC filtering has favorable properties of small size, good generalization, rapid learning and dynamic response. So, it is more suitable for high-speed signal processing applications. The adaptive CMAC is used to adjust the coefficients of IIR filter employed in the proposed model. The training of CMAC is based upon a fuzzy rule. The efficiency of the proposed adaptive CMAC filtering has been validated by evaluating it on a set of test videos sequences. We have done performance comparison of CMAC algorithm with already existing algorithms such Kalman filter, Modified proportional integrated (MPI) controller and adaptive fuzzy filtering in terms of smooth index (SI), peak signal to noise ratio (P SN R), mean square error (M SE) and point spread function (P SF ).

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Section: The Motion Estimation Unitmentioning

confidence: 99%

“…We have a shaky video sequence as input of the proposed model. Local motion estimation determines the local motion vector by representative point matching algorithm which has been proposed in [1] and [14]. The RPM can greatly reduce the complexity of computation in comparison with other methods.…”

Section: Chapter 4 4 the Proposed Modelmentioning

confidence: 99%

Visual quality improvement of digital video by stabilization using adaptive CMAC filtering

Zahoor

Koodtalang

Shahid

et al. 2012

2012 6th International Conference on Signal Processing and Communication Systems

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“…However, cameras mounted on vehicles frequently suffer from image shaking caused by the vehicles' motion [1,2]. In particular, serious image shake occurs in complex terrains or under strenuous motions, thereby blurring the video sequences captured by cameras.…”

Section: Introductionmentioning

confidence: 99%

Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

Hao

2017

Entropy

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Cameras mounted on vehicles frequently suffer from image shake due to the vehicles' motions. To remove jitter motions and preserve intentional motions, a hybrid digital image stabilization method is proposed that uses variational mode decomposition (VMD) and relative entropy (RE). In this paper, the global motion vector (GMV) is initially decomposed into several narrow-banded modes by VMD. REs, which exhibit the difference of probability distribution between two modes, are then calculated to identify the intentional and jitter motion modes. Finally, the summation of the jitter motion modes constitutes jitter motions, whereas the subtraction of the resulting sum from the GMV represents the intentional motions. The proposed stabilization method is compared with several known methods, namely, medium filter (MF), Kalman filter (KF), wavelet decomposition (MD) method, empirical mode decomposition (EMD)-based method, and enhanced EMD-based method, to evaluate stabilization performance. Experimental results show that the proposed method outperforms the other stabilization methods.

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“…In this paper, we propose a CNN-based chip which could greatly reduce the heavy computation problem for location motion estimation processing. The representative point matching (RPM) [2] method is used to find the local motion vectors. The pre-processing image information will first pass a digital-to-analog (D/A) converter and store incoming data to memories.…”

mentioning

confidence: 99%

CNN-Based Local Motion Estimation for Image Stabilization Processing and its Implementation

Lin

Chen

Cheng

et al. 2006

2006 IEEE International Conference on Systems, Man and Cybernetics

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The objective of this paper is to investigate a novel design for local motion vectors (LMVs) of image sequences, which are often used in a digital image stabilization (IS) system. The IS technique removes unwanted shaking phenomenon in image sequences captured by hand-held camcorders. It includes two main parts such as motion estimation and compensation. Most of computation power occurs in the part of motion estimation. In order to reduce this complexity, an idea, which integrates an adaptive-threshold method and cellular neural networks (CNN) architecture, is designed to improve this problem. The design only implements the most important local motion estimation with the array size of 19x25 pixels. Experimental results with HSPICE simulation and CNNUM are shown that the proposed architecture fast searches the location of possible LVMs and has the capability of real-time operations.

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An application of fuzzy set theory for an electronic video camera image stabilizer

Cited by 41 publications

References 4 publications

Visual quality improvement of digital video by stabilization using adaptive CMAC filtering

Visual quality improvement of digital video by stabilization using adaptive CMAC filtering

Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

CNN-Based Local Motion Estimation for Image Stabilization Processing and its Implementation

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