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Fast algorithm for multiple-circle detection on images using learning automata

IET image processing 2012年第8期6卷 1124-1135页

作者： Cuevas, E. Wario, F. Osuna-Enciso, V. Zaldivar, D. Perez-Cisneros, M. Univ Guadalajara Dept Elect CUCEI Guadalajara 44430 Jalisco Mexico IPN Ctr Invest Computac Mexico City 07738 DF Mexico

Hough transform has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimisation procedures for detecting multiple circles under the inconvenience that only one circle can be marked at each optimisation cycle demanding a longer execution time. In contrast, learning automata ( LA) is a heuristic method to solve complex multi-modal optimisation problems. Although LA converges to just one global minimum, the final probability distribution holds valuable information regarding other local minima which have emerged during the optimisation process. The detection process is considered as a multi-modal optimisation problem, allowing the detection of multiple circular shapes through only one optimisation procedure. The algorithm uses a combination of three edge points as parameters to determine circles candidates. A reinforcement signal determines whether such circle candidates are actually present at the image. Guided by the values of such reinforcement signal, the set of encoded candidate circles are evolved using the LA so that they can fit into actual circular shapes over the edge-only map of the image. The overall approach is a fast multiple-circle detector despite facing complicated conditions.

关键词： probability multiple-circle detector iterative methods execution time Optimisation techniques edge-only map Hough transform Automata theory iterative optimisation procedures computer vision and image processing techniques global minimum multimodal optimisation problems Optical, image and video signal processing probability distribution optimisation cycle Integral transforms multiple-circle image detection computational load reinforcement signal robustness object detection optimisation Hough transforms multiple circular shapes detection Interpolation and function approximation (numerical analysis) heuristic methods encoded candidate circles detection process learning automata optimisation process Other topics in statistics

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Multiple face detection algorithm using colour skin modelling

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IET image processing 2012年第8期6卷 1093-1101页

作者： Amjad, A. Griffiths, A. Patwary, M. N. Staffordshire Univ FCET Stafford ST18 0AD England

A variety of skin tones along with other constraints on image capturing within a given background make face detection a challenging task of image processing for surveillance and security applications. In this study, a colour-based skin model is proposed for a diverse range of skin tones. It is found that the proposed skin model outperforms several existing skin models. The proposed skin model is used to develop a soft decision-based multiple face detection framework. The robustness of the proposed framework has been evaluated for a wide range of poses, facial expressions, complex backgrounds and blur noises. On the basis of the proposed skin model and the soft decision-based scheme, the range of values of Cb and Cr has been compressed to obtain reduced false detections and missed detections as compared with the existing algorithms.

关键词： facial expression skin image colour analysis computer vision and image processing techniques image processing blur noise Optical, image and video signal processing colour-based skin model image capturing missed detection poses skin tone object detection false detection security application face recognition soft decision-based multiple face detection framework colour skin modelling pose estimation surveillance

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Fast super-resolution algorithms using one-dimensional patch-based training and directional interpolation

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IET image processing 2012年第5期6卷 548-557页

作者： Kang, Y. U. Jeong, S-C. Song, B. C. Inha Univ Sch Elect Engn Inchon 402751 South Korea

This study proposes fast super-resolution algorithms to up-scale an input low-resolution image into a high-resolution image. Conventional learning-based super-resolution algorithms require large memory space to store a huge amount of synthesis information, and they require significant computation because of the large number of two-dimensional matching operations. To mitigate this problem, the authors train a dictionary using one-dimensional patch-based training and K-means clustering at the learning phase, and they use one-dimensional matching and interpolation based on the trained dictionary at the synthesis phase. Such one-dimensional content-adaptive interpolation is applied separately in horizontal and vertical directions. In addition, the authors propose a hybrid algorithm in which directional interpolation is utilised for vertical interpolation to further reduce the dictionary size and the so called staircase artefact. Simulation results show that the proposed algorithm has higher peak-to-peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) values while providing significantly smaller dictionary size and faster computation than the latest learning-based super-resolution algorithm.

关键词： image resolution one-dimensional patch-based training synthesis information SSIM fast super-resolution algorithms directional interpolation learning-based super-resolution algorithms one-dimensional content-adaptive interpolation computer vision and image processing techniques one-dimensional matching Optical, image and video signal processing image matching two-dimensional matching operations horizontal directions high-resolution image synthesis phase Interpolation and function approximation (numerical analysis) learning phase interpolation input low-resolution image peak-to-peak signal-to-noise ratio memory space vertical directions K-means clustering structure similarity PSNR vertical interpolation

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New system to fingerprint extensible markup language documents using winnowing theory

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IET SIGNAL processing 2012年第4期6卷 348-357页

作者： Darwish, S. M. Univ Alexandria Inst Grad Studies & Res Dept Informat Technol Alexandria Egypt

Today, with the fast development of extensible markup language (XML) and increasing amount of data that is published in the form of XML, copyright protection of these data is becoming an important requirement for numerous applications. This study is proposing a system that uses fingerprinting to trace illegal copies and detect any modification made to an XML data. However, the flexible construction of XML data poses a number of challenges to fingerprinting, such as reorganisation and alteration. To overcome these challenges, the proposed system has to be based on the winnowing theory, which selects fingerprint from hashes of XML elements. This system is distortion free since it does not introduce any deformation to the XML data and also preserves usability constraint that is not optimised by the current fingerprinting systems. Experimental results show that the probability of missing fingerprint matching is very low and the chance to detect and locate changes in the XML data is very high.

关键词： computer vision and image processing techniques copyright fingerprint matching miss extensible markup language documents Data security XML winnowing theory XML data deformation flexible construction security of data illegal copy tracing current fingerprinting systems usability constraint Document processing techniques copyright protection fingerprint identification XML elements High level languages

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vision-based hand pose estimation through similarity search using the earth mover's distance

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IET computer vision 2012年第4期6卷 285-295页

作者： de Villiers, H. A. C. van Zijl, L. Niesler, T. R. Univ Stellenbosch Digital Signal Proc Grp Dept Elect & Elect Engn ZA-7600 Stellenbosch South Africa Univ Stellenbosch Dept Comp Sci ZA-7600 Stellenbosch South Africa

vision-based hand pose estimation presents unique challenges, particularly if high-fidelity reconstruction is desired. Searching large databases of synthetic pose candidates for items similar to the input offers an attractive means of attaining this goal. The earth mover's distance is a perceptually meaningful measure of dissimilarity that has shown great promise in content-based image retrieval. It is in general, however, a computationally expensive operation and must be used sparingly. The authors investigate a way of economising on its use while preserving much of its accuracy when applied naively in the context of searching for hand pose candidates in large synthetic databases. In particular, a two-tier search method is proposed which achieves similar accuracy with a speed increase of two orders of magnitude. The system performance is evaluated using real input and the results obtained using the different approaches are compared.

关键词： image recognition contour-based similarity search large synthetic databases large database searching high-fidelity reconstruction content-based retrieval vision-based hand pose estimation system performance evaluation image retrieval Information retrieval techniques image reconstruction performance evaluation computationally expensive operation computer vision pose estimation computer vision and image processing techniques earth mover distance synthetic pose candidates content-based image retrieval two-tier search method

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Acceleration of fractal image compression using fuzzy clustering and discrete-cosine-transform-based metric

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IET image processing 2012年第7期6卷 1024-1030页

作者： Jaferzadeh, K. Kiani, K. Mozaffari, S. Semnan Univ Elect & Comp Dept Semnan Iran

The encoding step in a fractal image compression is very time consuming, because a large numbers of sequential search through a list of domains are needed to find the best match for a given range block. Adaptive domain clustering is one solution to overcome this computational burden. The use of a new metric with fewer operations for domain-range blocks comparison is fruitful. In this study, range and domain blocks are categorised by fuzzy c-mean-clustering approach and compared with the use of new metric based on discrete cosine transform coefficient. Experimental results show that by clustering image pixels into five clusters, the encoding step was 8.88 times faster than the full-search method (no clustering) at the expense of some reduction in the decoded image's quality. But the proposed method with the same number of clusters speeds up the encoding process by 45 with lower PSNR decay.

关键词： adaptive domain clustering discrete cosine transforms domain-range blocks comparison fractal image compression acceleration sequential search image quality decoding encoding process computer vision and image processing techniques image and video coding image coding fuzzy set theory Integral transforms pattern clustering discrete-cosine-transform-based metric fuzzy clustering decoding image pixels clustering encoding step Combinatorial mathematics fuzzy c-mean-clustering approach domain blocks fractals

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Enhancing the predictive coding efficiency with control technologies for lossless compression of images

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IET image processing 2012年第3期6卷 251-263页

作者： Lee, C. -H. Kau, L. -J. Natl Chung Hsin Univ Dept Mech Engn Taichung 402 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Grad Inst Comp & Commun Engn Taipei 10608 Taiwan

This study applies techniques commonly used in control systems to enhance the efficiency of predictive coding in lossless compression of images for pixels around boundaries. Actually, the predictive coding system behaves just like a multi-input single-output system with the predictor itself can be regarded as the system model. Besides, the prediction error is usually feedback for the adaptation of predictor coefficients so that the prediction error of consecutive pixels can be minimised. When compared with a control system, which is to follow the system command as precisely as possible, the authors find the objective of both systems are the same. Moreover, a boundary among image pixels can be considered a step command in control systems. These observations lead to the idea of using control technologies to improve the prediction result around boundaries. To realise this idea, an adaptive Takagi-Sugeno fuzzy neural network and a proportional controller in control systems are applied as the predictor and the error compensator, respectively. To accelerate the run-time performance of the proposed system under limited resources, the online training area is even not used for network adaptation, but the performance is still comparable with state-of-the-art predictors and coders as the authors will see in the experiment.

关键词： adaptive Takagi-Sugeno fuzzy neural network predictor Neural computing techniques error compensator control technology computer vision and image processing techniques image and video coding proportional control image coding multiinput single-output system predictive coding lossless image compression proportional controller neural nets

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Graph cut-based binarisation of noisy check images

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IET image processing 2012年第9期6卷 1256-1261页

作者： Dawoud, A. Netchaev, A. Univ So Mississippi Sch Comp Hattiesburg MS 39406 USA

Binarisation of document images with poor contrast, strong noise, complex patterns and variable modalities in the greyscale histograms is a challenging problem. This study proposes an algorithm for the binarisation of noisy check images to extract handwriting text using normalised graph cuts (GCs). The proposed algorithm uses a normalised GC measure as a thresholding principle to distinguish the handwriting characters from the noisy background. The authors propose a factor to penalise extracting objects that do not have the elongated shape of the characters. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results performed on 560 check images showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

关键词： noisy check images segmentation algorithms OCR recognition object recognition normalised graph cuts computer vision and image processing techniques feature classification optical character recognition skeleton facilitates feature extraction document images binarisation variable modalities graph cut-based binarisation noisy check images binarisation Optical, image and video signal processing image segmentation

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Automatic detection of exudates and optic disk in retinal images using curvelet transform

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IET image processing 2012年第7期6卷 1005-1013页

作者： Esmaeili, M. Rabbani, H. Dehnavi, A. M. Dehghani, A. Isfahan Univ Med Sci Dept Biomed Engn Med Image & Signal Proc Res Ctr Esfahan Iran Isfahan Univ Med Sci Dept Ophthalmol Sch Med Esfahan Iran

This work presents a curvelet-based algorithm for detection of optic disk (OD) and exudates on low contrast images. This algorithm which is composed of three main stages does not require user initialisation and is robust to the changes in the appearance of retinal fundus images. At first, bright candidate lesions in the image are extracted by employing DCUT and modification of curvelet coefficients of enhanced retinal image. For this purpose, the authors apply a new bright lesions enhancement on green plane of retinal image to obtain adequate illumination normalisation in the regions near the OD, and to increase brightness of lesions in dark areas such as fovea. Following this step, the authors introduce a new OD detection and boundary extraction method based on DCUT and level set method. Finally, bright lesions map (BLM) image is generated and to distinguish between exudates and OD (i.e. a false detection for the final exudates detection), the extracted candidate pixels in BLM that are not in OD regions (detected in previous step) are considered as actual bright lesions. The sensitivity and specificity of the authors exudates detection method are 98.4 and 90.1%, respectively, and the average accuracy of their OD boundary extraction method is 94.51%.

关键词： curvelet coefficient Patient diagnostic methods and instrumentation bright lesions map boundary extraction method image enhancement DCUT image recognition Biology and medical computing computer vision and image processing techniques optic disk detection curvelet transforms Integral transforms retinal recognition eye automatic exudate detection pixel extraction object detection feature extraction level set method illumination normalisation retinal fundus image enhancement curvelet transform medical image processing Biomedical measurement and imaging BLM bright candidate lesion enhancement

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Ripplet transform type II transform for feature extraction

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IET image processing 2012年第4期6卷 374-385页

作者： Xu, J. Wu, D. Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Current image representation schemes have limited capability of representing two-dimensional (2D) singularities (e.g. edges in an image). Wavelet transform has better performance in representing one-dimensional (1D) singularities than Fourier transform. Recently invented ridgelet and curvelet transform achieve better performance in resolving 2D singularities than wavelet transform. To further improve the capability of representing 2D singularities, this study proposes a new transform called ripplet transform type II (ripplet-II). The new transform is able to capture 2D singularities along a family of curves in images. In fact, ridgelet transform is a special case of ripplet-II transform with degree 1. Ripplet-II transform provides the freedom in parameter settings, which can be optimised for specific problems. Ripplet-II transform can be used for feature extraction because of its efficiency in representing edges and textures. Experiments in texture classification and image retrieval demonstrate that the ripplet-II transform-based scheme outperforms wavelet and ridgelet transform-based approaches.

关键词： Fourier transform curvelet transform image representation two-dimensional singularities image representation scheme wavelet transform computer vision and image processing techniques image classification 2D singularities curvelet transforms wavelet transforms feature extraction texture classification ripplet transform type II transform Integral transforms image retrieval Optical, image and video signal processing

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