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Image Processing, IET

Issue 8 • Date November 2013

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Displaying Results 1 - 8 of 8
  • Automated photo-consistency test for voxel colouring based on fuzzy adaptive hysteresis thresholding

    Publication Year: 2013 , Page(s): 713 - 724
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (819 KB)  

    Voxel colouring is a popular method for reconstructing a three-dimensional surface model from a set of a few calibrated images. However, the reconstruction quality is largely dependent on a thresholding procedure allowing the authors to decide, for each voxel, whether it is photo-consistent or not. Nevertheless, in addition to the absence of any information on the neighbouring voxels during the photo-consistency test, it is extremely difficult to define the appropriate thresholds, which should be precise and stable on all surface voxels. In this study, the authors propose an automated photo-consistency test based on fuzzy hysteresis thresholding. The proposed method allows the incorporation of the spatial coherence during volume reconstruction, while avoiding `floating voxels' and holes. Moreover, the ambiguity of choosing the thresholds is extremely minimised by defining a fuzzy degree of membership of each voxel into the class of consistent voxels. Also, there is no need for preset thresholds since the hysteresis ones are defined automatically and adaptively depending on the number of images that the voxel is projected onto. Preliminary results are very promising and demonstrate that the proposed method performs automatically precise and smooth volumetric scene reconstruction. View full abstract»

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  • Difference theoretic feature set for scale-, illumination- and rotation-invariant texture classification

    Publication Year: 2013 , Page(s): 725 - 732
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (874 KB)  

    Texture identification and classification under varying scale, rotation and illumination conditions is a challenging task in pattern recognition and grey level difference statistics have been extensively used for this purpose. This study presents a new set of features for scale-, rotation- and illumination-invariant texture classification derived from the correlated distributions of local and global grey level differences of intensities in the texture image. The authors analyse the terms in the correlation formula for determining the difference-based feature set that is invariant and unique for a texture class. A comprehensive evaluation is performed on a huge database of digitally created texture samples of varying scale, orientation and brightness. The one-nearest neighbour classifier is used in the authors' experiments and the results indicate high classification accuracy for the proposed feature vector under varying scale, rotation and brightness conditions. The proposed method is compared with the highly efficient rotation- and illumination-invariant local binary pattern (LBP) and LBP variance techniques and the scale- and rotation-invariant MRS4 technique and is found superior in performance with an additional advantage of reduced feature dimension. View full abstract»

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  • Minimisation of image watermarking side effects through subjective optimisation

    Publication Year: 2013 , Page(s): 733 - 741
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (715 KB)  

    This study investigates the use of structural similarity index (SSIM) on the minimised side effect to image watermarking. For the fast implementation and more compatibility with the standard discrete cosine transform (DCT)-based codecs, watermark insertion is carried out on the DCT coefficients and hence an SSIM model for DCT-based watermarking is developed. For faster implementation, the SSIM index is maximised over independent 4 × 4 non-overlapped blocks, but the disparity between the adjacent blocks reduces the overall image quality. This problem is resolved through optimisation of overlapped blocks, but, the higher image quality is achieved at a cost of high computational complexity. To reduce the computational complexity while preserving the good quality, optimisation of semi-overlapped blocks is introduced. The authors show that while SSIM-based optimisation over overlapped blocks has as high as 64 times the complexity of the 4 × 4 non-overlapped method, with semi-overlapped optimisation the high quality of overlapped method is preserved only at a cost of less than 8 times the non-overlapped method. View full abstract»

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  • Data-mining process: application for hand detection in contact free settings

    Publication Year: 2013 , Page(s): 742 - 750
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (719 KB)  

    Hand detection is the first step of any hand biometric recognition process, which determines the outcome of the following treatments. In this study, the authors propose a robust method for hand detection without contact and without constraints on the capture environment. This method is based on a data-mining process for skin-colour modelling. The presented data-mining process offers several advantages like the choice of the most relevant colour axes and the automatic choice of the decision rules. To improve the achieved results of skin detection and to determine the hand region in the image, a succession of postprocessings was proposed. The authors hand detection method was evaluated experimentally on a real database, namely, `Sfax-Miracl hand database'; the outcomes of this evaluation show promising results and demonstrate the effectiveness of the proposed method. View full abstract»

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  • Pixel-wise skin colour detection based on flexible neural tree

    Publication Year: 2013 , Page(s): 751 - 761
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1284 KB)  

    Skin colour detection plays an important role in image processing and computer vision. Selection of a suitable colour space is one key issue. The question that which colour space is most appropriate for pixel-wise skin colour detection is not yet concluded. In this study, a pixel-wise skin colour detection method is proposed based on the flexible neural tree (FNT) without considering the problem of selecting a suitable colour space. A FNT-based skin model is constructed by using large skin data sets which identifies the important components of colour spaces automatically. Experimental results show improved accuracy and false positive rates (FPRs). The structure and parameters of FNT are optimised via genetic programming and particle swarm optimisation algorithms, respectively. In the experiments, nine FNT skin models are constructed and evaluated on features extracted from RGB, YCbCr, HSV and CIE-Lab colour spaces. The Compaq and ECU datasets are used for constructing FNT-based skin model and evaluating its performance compared with other skin detection methods. Without extra processing steps, the authors method achieves state of the art performance in skin pixel classification and better performance in terms of accuracy and FPRs. View full abstract»

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  • Adaptive sampling approach for volumetric shadows in dynamic scenes

    Publication Year: 2013 , Page(s): 762 - 767
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (377 KB)  

    Ray marching is an important technique to generate volumetric lighting effects. However, it is very expensive for each pixel on the screen, especially in dynamic scenes. The authors propose an adaptive approach to reduce samples for volumetric shadows in the time domain. In dynamic scenes, shadow volumes of moving objects are created and are rasterised to decide pixels that cannot be reused. The authors use a stencil buffer to maintain the information of screen pixels by recomputing or just using the previous information. Experimental results show that the proposed approach is simple to implement. Moreover, compared to the previous method, the proposed approach achieves a specific speedup and maintains similar visual quality. View full abstract»

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  • Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model

    Publication Year: 2013 , Page(s): 768 - 776
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1024 KB)  

    The new techniques of three-dimensional (3D)-optical coherence tomography (OCT) imaging is very useful for detecting retinal pathologic changes in various diseases and determining retinal thickness `abnormalities'. Fundus colour images have been used for several years for detecting retinal abnormalities too. If the two image modalities were combined, the resulted image would be more informative. The first step to combine these two modalities is to register colour fundus images with an en face representation of OCT. In this study, curvelet transform is used to extract vessels for both modalities. Then the extracted vessels from two modalities are registered together in two stages. At first, images are registered using scaling and translation transformations. Then a quadratic transformation model is assumed between two pairs of images; because retina is imaged as a second-order surface. Twenty-two eyes (17 macular and 5 prepapillary), from random patients, were imaged in this study with Topcon 3D OCT1000 instrument. A new registration error is defined which averages the distance between all the corresponding points in two sets of vessels. Results show that registration error after stage one is 6.01 ± 1.82 pixels and after stage two is 1.02 ± 0.02 pixels. View full abstract»

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  • Interpolation-based impulse noise removal

    Publication Year: 2013 , Page(s): 777 - 785
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1125 KB)  

    Interpolation-Based Impulse Noise Removal (IBINR), a fast and simple algorithm is proposed to remove fixed valued impulse noise in this study. The proposed method removes all noisy pixels from the image and determines their values using non-linear interpolation. Compared with the state-of-the-art noise removal algorithms, IBINR has the highest or comparable performance in terms of photographic image denoising power and resource efficiency: it runs in shorter amount of time and does not have any significant additional memory requirements due to the fact that costly sorting operations are avoided. View full abstract»

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Aims & Scope

The range of topics covered by IET Image Processing includes areas related to the generation, processing and communication of visual information.

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