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Image Processing, IET

Issue 1 • Date February 2011

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Displaying Results 1 - 10 of 10
  • Noise gradient reduction based on morphological dual operators

    Page(s): 1 - 17
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3030 KB)  

    Noise gradient is reduced while image details gradient is also reduced by a filter. For the image corrupted by impulse noise, a novel approach of noise gradient reduction is proposed based on a pair of morphological dual operators. The noise image is filtered by a pair of morphological dual operators respectively, and then the two filtered images are provided with the complementary characteristics of the noise gradient position. This feature results from the unsymmetric behaviour of the pair of morphological dual operators, and it can be applied to reduce the noise gradient effectively. This approach is presented in detail and the experimental results show that the approach not only reduces noise gradient effectively, but also maintains image details gradient. Compared with the classical morphological dual operators, the generalised morphology dual operators have smaller root mean square error on the premise of the close computation and time. View full abstract»

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  • Fast encoding method for vector quantisation of images using subvector characteristics and Hadamard transform

    Page(s): 18 - 24
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (126 KB)  

    In a framework of vector quantisation (VQ), the encoding speed is a key issue for its practical applications. To speed up the VQ encoding process, a fast encoding method in the Hadamard transform domain is presented. In the proposed method, a five-step test flow based on two characteristic values, the first element and variance of the transformed subvector, is introduced to reject a large number of unlikely codewords. In order to make full use of the energy-compaction property of an orthogonal transform, the partial distance search (PDS) method is used in its Steps 4 and 5. Experimental results show that the proposed algorithm outperforms most of the existing algorithms, especially in case of larger codebook size and high-detail images. View full abstract»

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  • Use of matrix polar decomposition for illumination-tolerant face recognition in discrete cosine transform domain

    Page(s): 25 - 35
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (628 KB)  

    In this study, an illumination-tolerant face recognition algorithm is proposed. This work highlights the significance of matrix polar decomposition for illumination-invariant face recognition. The proposed algorithm has two stages. In the first stage, the authors reduce the effect of illumination changes by weakening the discrete cosine transform coefficients of block intensities using a new designed quantisation table. In the second stage, the unitary factor of polar decomposition of the reconstructed image is used as a feature matrix. In the recognition phase, a novel indirect method for measuring the similarities in feature matrices is proposed. The nearest-neighbour rule is applied to the matching. The authors have performed some experiments on several databases to evaluate the proposed method in its different aspects. Experimental results on recognition demonstrate that this approach provides a suitable representation for illumination invariant face recognition. View full abstract»

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  • New method for the fusion of complementary information from infrared and visual images for object detection

    Page(s): 36 - 48
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1063 KB)  

    Visual and infrared cameras have complementary properties and using them together may increase the performance of object detection applications. Although the fusion of visual and infrared information results in a better recall rate than using only one of those domains, there is always a decrease in the precision rate whereas the infrared domain on its own always has higher precision. Thus, the fusion of these domains is meaningful only for a better recall rate, which means that more foreground pixels are detected correctly. This study presents a new computationally more efficient and simpler method for extracting the complementary information from both domains and fusing them to obtain better recall rates than those previously achieved. The method has been tested using a well-known database and a database created for the study and compared with earlier fusion methods. View full abstract»

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  • Vector quantisation based on a quasi-binary search algorithm

    Page(s): 49 - 54
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (278 KB)  

    This study presents an efficient quasi-binary search algorithm for vector quantisation (VQ). The proposed algorithm adopts a tree-structured VQ (TSVQ) with overlapped codewords (TSOC) to reduce computational complexity and enhance quantisation quality. This algorithm uses overlapped codewords to expand the scope of the search path to traverse more appropriate codewords. In the authors' speech experiment, compared with the full search VQ (FSVQ), the average computational savings for triangle inequality elimination (TIE), TSVQ and TSOC are 23.65, 88.63 and 59.43%, respectively. In this experiment, the quantisation accuracy of TIE, TSVQ and TSOC are 100, 46.61 and 99.16%, respectively. To further evaluate computations at each stage of the proposed algorithm, both speech and images are considered. With codebook sizes of 256, 512 and 1024, the corresponding optimal computational savings for images are 84.59, 91.08 and 93.51%, respectively, compared with the FSVQ. For speech, the optimal computational savings reached 59.43% for a codebook size of 128. The results indicate that the proposed algorithm can save a significant number of computations, depending on the size of codebook. The TSOC algorithm is a trade-off between TSVQ and TIE, which provides a satisfactory computation quality. Moreover, unlike the TIE method, our algorithm does not depend on the high correlation characteristics of signals to reduce the amount of computation, but the TIE method can be incorporated into our algorithm to dramatically reduce the amount of computation. View full abstract»

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  • Combined significance map coding for still image compression

    Page(s): 55 - 62
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (720 KB)  

    Set partitioning in hierarchical trees (SPIHT) was known for its relatively simple implementation and flexible scalability when it is combined with discrete wavelet transform (DWT). The authors propose a method called combined significance map coding (CSMC) to improve the coding efficiency of SPIHT when used with block-based discrete cosine transform (DCT). CSMC groups some blocks and encodes the combined significance map of one to several blocks together. Lots of bits spent in significance map coding can be saved when the trees constructed with block DCT coefficients have similar locality. From our simulation results, CSMC improves significantly when in comparison with the original SPIHT coder using DWT and DCT. It also yields better performance than JPEG2000, and even outperforms the non-scalable H.264 intra-mode coder for some test images. No coding table is required, and fine rate/quality scalability property of SPIHT is still preserved. View full abstract»

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  • Fast two-step histogram-based image segmentation

    Page(s): 63 - 72
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (959 KB)  

    The authors propose a novel image segmentation technique based on the non-parametric clustering procedure in the discretised colour space. The discrete probability density function is estimated in two steps. Multidimensional colour histogram is created, which is afterwards used to acquire final density estimate using the variable kernel density estimation technique. Segmentation is obtained by mapping revealed range domain clusters to the spatial image domain. The proposed method is highly efficient, running in time linear to the number of the image pixels with low constant factors. The output of the algorithm can be accommodated for a particular application to simplify the integration with other image processing techniques. Quantitative evaluation on a standard test dataset proves that the quality of the segmentations provided by the proposed method is comparable to the quality of the segmentations generated by other widely adopted low-level segmentation techniques, while running times are several times faster. View full abstract»

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  • Fast arbitrary resizing of images in the discrete cosine transform domain

    Page(s): 73 - 86
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1231 KB)  

    This study presents a method to resize images by a rational factor of P/Q in the discrete cosine transform (DCT) domain, where P and Q are relatively prime integers larger than 1. Our method extends on the prior work of Mukherjee and Mitra, which utilises the spatial relationship of DCT coefficients between a block and its sub-blocks. To resize images by a factor of P/Q, the images are first up-sampled by a factor of P and then down-sampled by a factor of Q. Although this method produces resized images with good visual quality, it requires high computational cost. In this study, the authors generalise an observation found in the spatial relationship of the DCT coefficients between a block and its sub-blocks. Subsequently, a sparse matrix representation is derived from this observation to reduce the computational cost of the proposed method. To further reduce computational cost of the proposed method, a subset of up-sampled DCT coefficients is used in the down-sampling operation. From various experiments, the authors have determined the lowest number of up-sampled DCT coefficients to be used in the down-sampling operation without affecting the visual quality of the resized images. As compared to existing methods, the proposed method requires lower computational cost and produces resized images of good visual quality. View full abstract»

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  • Combined shape and feature-based video analysis and its application to non-rigid object tracking

    Page(s): 87 - 100
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1625 KB)  

    Many video object tracking systems use block matching algorithm (BMA) because of its simple computational structure and robust performance. The BMA, however, exhibits fundamental limitations resulting from non-rigid shapes and similar patterns to the background. The authors propose a combined shape and feature-based non-rigid object tracking algorithm, which is tightly coupled with an adaptive background generation to overcome the limit of block matching. The proposed algorithm is robust to the object's sudden movement or the change of features. This becomes possible by tracking both feature points and their neighbouring regions. Combination of background and shape boundary information significantly improves the tracking performance because the target object and the corresponding feature points on the boundary can be easily found. The shape control points (SCPs) are regularly distributed on the contour of the object, and the authors compare and update the centroid during the tracking process, where straying SCPs are removed, and the tracking continues with only qualified SCPs. As a result, the proposed method becomes free from potential failing factors such as spatio-temporal similarity between object and background, object deformation and occlusion, to name a few. Experiments have been performed using several in-house video sequences including various objects such as a moving robot, swimming fish and walking people. In order to demonstrate the performance of the proposed tracking algorithm, a number of experiments have been performed under noisy and low-contrast environment. For more objective comparison, performance evaluation of tracking surveillance 2002 data sets were also used. View full abstract»

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  • Video object pursuit by tri-tracker with on-line learning from positive and negative candidates

    Page(s): 101 - 111
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1444 KB)  

    Based on chain code, an improved Hough detection method for head detection is proposed, with which moving regions of objects are determined. During tracking process, we present a tri-tracking method (tri-tracker), on-line trained by positive and negative candidates, for tracking objects. The tracker trains three support vector machines (SVMs) initialised with a small number of labelled frames and updates the classifiers in a collaborative fashion, in which, an object is represented using a local binary pattern (LBP) histogram, RGB colour histogram and pixel-pattern-based texture feature (PPBTF) histogram, respectively. Based on the probability map created by each classifier, the final probability map forms by combing three individual probability maps. And then the peak of final probability map, which we consider as the object's position, is found by mean shift. Experiments on several video sequences show the robustness and accuracy of our proposed method. 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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