Moving object segmentation using the 9/7&amp;#x2013;10/8 dual-tree complex filter bank

Baradarani, Aryaz; Wu, Q. M. Jonathan

doi:10.1109/icpr.2008.4761620

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…Huang et al [11,12] proposed an algorithm for moving object segmentation to solve the double-edge problem in the spatial domain using a change detection method with different thresholds in four wavelet sub-bands. Baradarani [13,14] refined the work of Huang et al [11,12] using dual tree complex filter bank in wavelet domain. These methods [11][12][13][14] suffer from the problem of noise disturbances and distortion of moving segmented object due to change in speed of objects.…”

Section: Related Workmentioning

confidence: 98%

“…Baradarani [13,14] refined the work of Huang et al [11,12] using dual tree complex filter bank in wavelet domain. These methods [11][12][13][14] suffer from the problem of noise disturbances and distortion of moving segmented object due to change in speed of objects. Khare et al [15] refine the work of Baradarani [13,14] and Huang et al [11,12] using Daubechies complex wavelet.…”

Section: Related Workmentioning

confidence: 98%

“…These methods [11][12][13][14] suffer from the problem of noise disturbances and distortion of moving segmented object due to change in speed of objects. Khare et al [15] refine the work of Baradarani [13,14] and Huang et al [11,12] using Daubechies complex wavelet. The method proposed by Khare et al [15] reduces the noise disturbance and speed change, but it suffers from the problem of dynamic background changes and shadow detection and due to this segmenting coherence occurs [16].…”

Section: Related Workmentioning

confidence: 99%

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A Framework of Moving Object Segmentation in Maritime Surveillance Inside a Dynamic Background

Kushwaha

Srivastava

2015

Lecture Notes in Computer Science

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Abstract. Maritime surveillance represents a challenging scenario for moving object segmentation due to the complexity of the observed scenes. The waves on the water surface, boat wakes, and weather issues contribute to generate a highly dynamic background. Moving object segmentation using change detection under maritime environment is a challenging problem for the maritime surveillance system. To address these issues, a fast and robust moving object segmentation approach is proposed which consist of seven steps applied on given video frames which include wavelet decomposition of frames using complex wavelet transform; use of change detection on detail coefficients (LH, HL, HH); use of background modeling on approximate co-efficient (LL sub-band); cast shadow suppression; strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. For dynamic background modeling in the water surface, we have used background registration, background difference, and background difference mask in the complex wavelet domain. For shadow detection and suppression problem in water surface, we exploit the high frequency sub-band in the complex wavelet domain. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for seven datasets. The various performance measures used for quantitative analysis include relative foreground area measure (RFAM), misclassification penalty (MP), relative position based measure (RPM), normalized cross correlation (NCC), Precision (PR), Recall (RE), shadow detection rate (SDR), shadow discrimination rate, execution time and memory consumption. Experimental results indicate that the proposed method is performing better in comparison to other methods in consideration for all the test cases as well as addresses all the issues effectively.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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A Framework of Moving Object Segmentation in Maritime Surveillance Inside a Dynamic Background

Kushwaha

Srivastava

2015

Lecture Notes in Computer Science

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“…Huang et al [13][14] proposed an algorithm for moving object segmentation using single change detection and double change detection applied in wavelet domain. Baradarani [15][16] refined the work of Huang et al [13][14] using dual tree complex filter bank in wavelet domain. Khare et al [17] also refine the work of Baradarani and Huang using Daubechies complex wavelet.…”

Section: Introductionmentioning

confidence: 98%

A framework for moving object segmentation under rapidly changing illumination conditions in complex wavelet domain

Kushwaha

Srivastava

2015

2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE)

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Moving object segmentation using change detection in wavelet domain under dynamic background changes is a challenging problem in video surveillance system. There are several literature surveys available in change detection using wavelet domain for moving object segmentation but most of the research work are based on static background changes. Change detection under background changes is a challenging task and it has not been addressed in effectively in literature. To address this issues, a fast and robust moving object segmentation approach is proposed in dynamic background changes which consist of six steps applied on given video frames which include: wavelet decomposition of frames using complex wavelet transform; use of change detection on detail coefficients (LH, HL, HH); use of background modeling on approximate co-efficient (LL sub-band); strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. For dynamic background modeling, we have improved the Gaussian mixture model and use mode value to find the variance of K-Gaussian. A comparative analysis of the proposed method is presented both quantitatively and qualitatively with other standard methods available in the literature. The various performance measure used for quantitative analysis include RFAM, RPM, NCC and MP. From the obtained result, it is observed that proposed approach is performing better in comparison to other methods in consideration.

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“…The method is based on determining the change detection mask in the multi-wavelet domain. In view of the success of the mentioned techniques and the celebrated achievements of DT-CWT in signal/image processing, we proposed a DT-CWT based method for moving object detection and segmentation Baradarani & Wu (2008). The method is based on determining the change detection mask in the complex wavelet domain.…”

mentioning

confidence: 99%

Wavelet-based Moving Object Segmentation: From Scalar Wavelets to Dual-tree Complex Filter Banks

Baradarani¹,

JonathanWu²

2010

Pattern Recognition Recent Advances

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Moving object segmentation using the 9/7–10/8 dual-tree complex filter bank

Cited by 6 publications

References 11 publications

A Framework of Moving Object Segmentation in Maritime Surveillance Inside a Dynamic Background

A Framework of Moving Object Segmentation in Maritime Surveillance Inside a Dynamic Background

A framework for moving object segmentation under rapidly changing illumination conditions in complex wavelet domain

Wavelet-based Moving Object Segmentation: From Scalar Wavelets to Dual-tree Complex Filter Banks

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