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image of Volume 5, Issue 3
Online ISSN 1751-9667 Print ISSN 1751-9659

IET Image Processing

Volume 5, Issue 3, April 2011

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Volume 5, Issue 3

April 2011

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    • Improved bi-dimensional empirical mode decomposition based on 2D-assisted signals: analysis and application
      • Author(s): G.L. Xu ; X.T. Wang ; X.G. Xu
      • Source: IET Image Processing, Volume 5, Issue 3, p. 205 –221
      • DOI:  10.1049/iet-ipr.2009.0158
      • Type: Article
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      • p. 205 –221 (17)
        Mode mixing, boundary effects, necessary extrema lacking and so on are the main problems involved in bi-dimensional empirical mode decomposition (BEMD). The study presents an improved BEMD based on 2D-assisted signals: 2D Gaussian noises. Firstly, the given 2D Gaussian noise and its negative counterpart are added to the original image, respectively, to construct the two images to be decomposed. Secondly, the decomposed intrinsic mode functions (IMFs) from the two images are added together to obtain the IMFs, in which the added noises are cancelled out with less mode mixing and boundary effects. The other contribution of the method lies in its overcoming of the problem of necessary extrema lacking that the previous BEMD fails. Some instructive conclusions are obtained in the improved BEMD. Lastly, the efficiency and performance of the method are given through theoretical analysis and its application in image enhancement, which outperforms some previous approaches.
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      Semi-supervised maximum a posteriori probability segmentation of brain tissues from dual-echo magnetic resonance scans using incomplete training data
      • Author(s): W. Li ; P. Ogunbona ; C. deSilva ; Y. Attikiouzel
      • Source: IET Image Processing, Volume 5, Issue 3, p. 222 –232
      • DOI:  10.1049/iet-ipr.2009.0082
      • Type: Article
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      • p. 222 –232 (11)
        This study presents a stochastic framework in which incomplete training data are used to boost the accuracy of segmentation and to optimise segmentation when images under consideration are corrupted by inhomogeneities. The authors propose a semi-supervised maximum a posteriori probability (ssMAP) segmentation method that is able to utilise any amount of training data that are usually insufficient for supervised segmentation. The ssMAP unifies supervised and unsupervised segmentation and takes the two as its special cases. To deal with inhomogeneities, the authors propose to incorporate a bias field into the ssMAP and present an algorithm (referred to as ssMAPe) for simultaneous maximum a posteriori probability (MAP) estimation of the inhomogeneity field and segmentation of brain tissues. Experiments on both simulated and real magnetic resonance (MR) images have shown that ssMAP with only a very small quantity of training data improves the segmentation accuracy substantially (up to 30%) compared to both fully supervised and unsupervised methods. The proposed ssMAPe estimates the inhomogeneity field effectively and further improves the segmentation if the MR images are corrupted by inhomogeneity.
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      Removal of non-uniform complex and compound shadows from textured surfaces using adaptive directional smoothing and the thin plate model
      • Author(s): R. McFeely ; C. Hughes ; E. Jones ; M. Glavin
      • Source: IET Image Processing, Volume 5, Issue 3, p. 233 –248
      • DOI:  10.1049/iet-ipr.2009.0198
      • Type: Article
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      • p. 233 –248 (16)
        In this study, the authors propose a novel algorithm for removing complex shadows from textured surfaces within a single image. Previous shadow removal methods have attempted to solve this problem under a range of criteria, such as the type and number of light sources and shadow structure. Some methods require that the shadow umbra and penumbra be well defined. They have been shown to work best when the umbra is relatively large and when the penumbra is of a limited size and shape. However, these methods can fail if the shadow shape or form diverges from the norm. Many methods also require user intervention to locate whole or parts of the shadow. The authors propose a flexible shadow removal model which is capable of functioning without these limiting assumptions, or the need for user intervention. It is also capable of dealing with shadows cast on a wide range of textured surfaces. It uses a directional differential filter along with directional smoothing to find the shadow and a thin plate reconstruction model to remove shadows from an image surface. Results have shown that the proposed algorithm generates high-quality shadow-free images over a range of scenarios, such as multiple light sources, occlusions and textures.
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      Robust adaptive directional lifting wavelet transform for image denoising
      • Author(s): X.T. Wang ; G.M. Shi ; Y. Niu ; L. Zhang
      • Source: IET Image Processing, Volume 5, Issue 3, p. 249 –260
      • DOI:  10.1049/iet-ipr.2009.0112
      • Type: Article
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      • p. 249 –260 (12)
        Recent researches have shown that the adaptive directional lifting (ADL) can represent edges and textures in images effectively. This makes it possible to separate noise from image signal distinctly in image denoising. However, a key issue named orientation estimation for ADL becomes inefficient and error prone in the noised circumstance. The authors propose a robust adaptive directional lifting-based (RADL) wavelet transform for image denoising by constructing ADL in an anti-noise way. In our method, a simple model of pixel pattern classification is incorporated into orientation estimation module to strengthen the robustness of this algorithm. Moreover, instead of determining the transform strategy based on sub-blocks, RADL is performed on pixel-level to pursue better denoising results. Experimental results show that the proposed technique demonstrates both PSNR and visual quality improvement on images with rich textures.
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      Edge detection of colour image based on quaternion fractional differential
      • Author(s): C.B. Gao ; J.L. Zhou ; J.R. Hu ; F.N. Lang
      • Source: IET Image Processing, Volume 5, Issue 3, p. 261 –272
      • DOI:  10.1049/iet-ipr.2009.0409
      • Type: Article
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      • p. 261 –272 (12)
        According to the development of the real fractional differential and its applications in the modern signal processing, the authors extend it to quaternion body and put forward a new concept: quaternion fractional differential (QFD), and apply it to edge detection of colour image. This method is called edge detection based on QFD. Simulation experiments indicate that this method has special advantages. Furthermore, the authors give an indicator to evaluate the effectiveness of different edge filters. Comparing with Sobel and mix edges of real fractional differential to every channels of colour image, they discover that QFD has fewer false negatives in the textured regions and is also better at detecting edges that are partially defined by texture, which means the authors can obtain much better results in the interesting regions using QFD and is more consistent with the characteristics of human visual system.
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      Detection and amendment of shape distortions based on moment invariants for active shape models
      • Author(s): S.Y. Chen ; J. Zhang ; Q. Guan ; S. Liu
      • Source: IET Image Processing, Volume 5, Issue 3, p. 273 –285
      • DOI:  10.1049/iet-ipr.2007.0110
      • Type: Article
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      • p. 273 –285 (13)
        The active shape model (ASM) is an ever-increasingly important method for object modelling, shape recognition and target localisation. During the process of shape fitting, however, distortions and displacements often occur when the target is not clear or with defects, and there is a lack of effective amendment strategies in ASM. In this study, inspired by physics, the boundary moment invariants are employed to resolve this difficulty. Moment invariants have been introduced into ASM for the first time for distortion detection and shape amendment. Using the proposed strategy, distortions are effectively avoided and the accuracy of the fitting result is obviously increased with a little extra time. Finally, the results of the authors' practical implementation prove its satisfactory work.
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      Efficient intra-mode decision algorithm for inter-frames in H.264/AVC video coding
      • Author(s): J.-H. Kim  and  B.-G. Kim
      • Source: IET Image Processing, Volume 5, Issue 3, p. 286 –295
      • DOI:  10.1049/iet-ipr.2009.0097
      • Type: Article
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      • p. 286 –295 (10)
        An efficient intra-mode search algorithm is proposed to reduce the computational complexity of inter-frames for the H.264/AVC video encoding system. Each intra-mode search procedure usually causes a high degree of complexity in an inter-frame because of the inter-frame prediction modes that operate with the inter-mode decision procedure. To decrease this computational burden, the authors propose an adaptive thresholding algorithm based on distribution characteristics of the sum of the absolute differences (SAD) of the best inter-mode when the intra-mode is the final coding mode. The authors also include a simple refinement process by using the spatial correlation between neighbouring macro-blocks (MBs) and the current MB. The performance of the proposed algorithm is verified through comparative analysis of encoding time, loss of quality and the bit increment.
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