Image denoising using common vector approach

Özkan, Kemal; Seke, Erol

doi:10.1049/iet-ipr.2014.0979

Cited by 13 publications

(7 citation statements)

References 30 publications

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“…CVA is a popular subspace based classification algorithm as applied for face recognition [11], spam classification [12], image denoising [13] and edge detection [14] tasks. The motivation of CVA is inspired from theory behind the PCA.…”

Section: Cva With Application To Background Modellingmentioning

confidence: 99%

A new subspace based solution to background modelling and change detection

Işık

Özkan

Gerek

2016

ijisae

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For surveillance system, the background subtraction plays an important role for moving object detection with an algorithm embedded in the camera. Since the existence algorithms cannot satisfy the good accuracy on complex backgrounds including illumination change and dynamic objects, we have put forward the concept of Common Vector Approach (CVA) as a new idea for background modelling. Effectiveness of proposed method is presented through the experiments on popular Wallflower dataset. The obtained visual outputs are compared with well-known methods based on the subjective and objective criteria. From the overall evaluation, we can note the proposed method is not only exhibit successful foreground detection results, but also promises an effective and efficient system for background modelling.

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Section: Cva With Application To Background Modellingmentioning

confidence: 99%

A new subspace based solution to background modelling and change detection

Işık

Özkan

Gerek

2016

ijisae

Self Cite

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“…The CMA algorithm is an extended form of Common Vector Approach, which is a subspace based method and utilized for face recognition [10], spam classification [11], image denoising [12] and edge detection [13] tasks. However, the ability of CMA for background modelling has not been realized in literature of computer vision.…”

Section: Principle Of Cma and Its Application To Background Subtractionmentioning

confidence: 99%

A Note on Background Subtraction by Utilizing a New Tensor Approach

Işık¹,

Özkan

Doğan

2016

ijisae

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This study deals with determining the foreground region by background subtraction based on a new tensor decomposition method. With this aim, the concept of Common Matrix Approach (CMA) is utilized with a purpose of background modelling. The performance of proposed method is validated by making experiments on real videos provided by Wallflower dataset. The obtained results are compared with well-known methods based on subjective on objective evaluation measures. The obtained good results indicate that using the CMA algorithm for background modelling is a simple and effective technique in terms computational cost and implementation.As an eventual result, we have observed that the superior results are determined on complex backgrounds including dynamic objects and illumination variation in image sets.

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“…CVA is a subspace-based recognition method that has been used in many pattern recognition tasks, e.g., voice [16] and face recognition [17], spam e-mail classification [18], image denosing [34]. A common vector is what is left when the differences between training vectors are removed from class members, and the common vector is invariant throughout the class.…”

Section: Overview Of the Common Vector Approachmentioning

confidence: 99%