IET Image Processing
Volume 10, Issue 6, June 2016
Volumes & issues:
Volume 10, Issue 6
June 2016
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- Author(s): Arpad Gellert and Remus Brad
- Source: IET Image Processing, Volume 10, Issue 6, p. 429 –437
- DOI: 10.1049/iet-ipr.2015.0702
- Type: Article
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p.
429
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A new image denoising method for impulse noise in greyscale images using a context-based prediction scheme is presented. The algorithm replaces the noisy pixel with the value occurring with the highest frequency, in the same context as the replaceable pixel. Since it is a context-based technique, it preserves the details in the filtered images better than other methods. In the aim of validation, the authors have compared the proposed method with several existing denoising methods, many of them being outperformed by the proposed filter.
- Author(s): Ahmed Medhat ; Ahmed Shalaby ; Mohammed Sharaf Sayed ; Maha Elsabrouty ; Farhad Mehdipour
- Source: IET Image Processing, Volume 10, Issue 6, p. 438 –447
- DOI: 10.1049/iet-ipr.2015.0666
- Type: Article
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p.
438
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High quality videos became an essential requirement in recent applications. High efficiency video coding (HEVC) standard provides an efficient solution for high quality videos at lower bit rates. On the other hand, HEVC comes with much higher computational cost. In particular, motion estimation (ME) in HEVC, consumes the largest amount of computations. Therefore, fast ME algorithms and hardware accelerators are proposed in order to speed-up integer ME in HEVC. This study presents a fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC. In addition, centre adaptive search algorithm, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance. Experimental results show notable speed-up in terms of encoding time and bit rate saving with tolerable peak signal-to-noise ratio (PSNR) quality degradation. The proposed fast search algorithms reduce the computational complexity of the HEVC encoder by 57%. This improvement is accompanied with a modest average PSNR loss of 0.014 dB and an increase by 0.6385% in terms of bit rate when compared with related works.
- Author(s): Reda Kasmi and Karim Mokrani
- Source: IET Image Processing, Volume 10, Issue 6, p. 448 –455
- DOI: 10.1049/iet-ipr.2015.0385
- Type: Article
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448
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The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.
- Author(s): Wang Fan ; Zhao Qiyang ; Yin Baolin ; Xu Tao
- Source: IET Image Processing, Volume 10, Issue 6, p. 456 –463
- DOI: 10.1049/iet-ipr.2015.0507
- Type: Article
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In this study, the authors address the problem of parsing fashion images into mid-level semantic parts including upper-clothing, lower-clothing, skin, hair and background. These mid-level parts provide the regional information of fashion items and have potential value in high-level parsing process. The key idea of the method is to parse the mid-level parts by region expanding. Owing to the co-occurrence of pose skeleton and the proposed parts, the region expanding process starts from the super-pixels crossed by specific segments of pose skeleton. The super-pixels are then merged with their neighbours by conditional inference based on their position and perceptual similarity. To avoid the difficulties of training on arbitrary graph structures, conditional random fields (CRFs) are constructed on super-pixel chains, which are extracted from the generated expanding trees. This is followed by a voting stage to mix up the probabilities estimated by the chain-CRFs to obtain the final result. Experiments on two datasets show that the new method outperforms related approaches in regional accuracy and has good generalisation capability. Furthermore, the method can be easily employed to improve the performance of high-level parsing. Its effectiveness has been verified by another group of experiments on two state-of-the-art high-level parsing approaches.
- Author(s): Abed Heshmati ; Maryam Gholami ; Abdolreza Rashno
- Source: IET Image Processing, Volume 10, Issue 6, p. 464 –473
- DOI: 10.1049/iet-ipr.2015.0738
- Type: Article
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p.
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The process of partitioning an image into some different meaningful regions with the homogeneous characteristics is called the image segmentation which is a crucial task in image analysis. This study presents an efficient scheme for unsupervised colour–texture image segmentation using neutrosophic set (NS) and non-subsampled contourlet transform (NSCT). First, the image colour and texture information are extracted via CIE Luv colour space model and NSCT, respectively. Then, the extracted colour and texture information are transformed into the NS domain efficiently by the authors’ proposed approach. In the NS-based image segmentation, the indeterminacy assessment of the images in the NS domain is notified by the entropy concept. The lower quantity of indeterminacy in the NS domain, the higher confidence and easier segmentation could be achieved. Therefore, to achieve a better segmentation result, an appropriate indeterminacy reduction operation is proposed. Finally, the K-means clustering algorithm is applied to perform the image segmentation in which the cluster number K is determined by the cluster validity analysis. To show the effectiveness of their proposed method, its performance is compared with that of the state-of-the-art methods. The experimental results reveal that their segmentation scheme outperforms the other methods for the Berkeley dataset.
- Author(s): Ssu-Han Chen
- Source: IET Image Processing, Volume 10, Issue 6, p. 474 –482
- DOI: 10.1049/iet-ipr.2015.0780
- Type: Article
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Automated cutting process may produce superficial defects such as scratches and depressions on lens collars which are caused by cutter offset or scrap accumulation. Additionally, electroplating defects, such as dots and uneven electroplating occurs if the surface is rough or contaminated with foreign matters. As the inclined camber of lens collar contributes to external appearances of a single-lens camera, customers extremely concern about its surface quality. Relying on human inspection to ensure the quality is time-consuming, labor-intensive and produces occupational injuries. Therefore, the implementation of auto-inspection can help overcome these problems. The inspection system is composed of charge coupled device (CCD), coaxial light and motor. After an image is taken, it goes through a segmentation sub-function to obtain a region of interesting (ROI). Since the texture of inclined camber of a lens collar is statistically distributed, an image restoration sub-function based on discrete cosine transformation (DCT) is used to map the texture onto high-energy components on a spectrum. They are then compressed by a notch-rejecting filter. In contrast with the defects, the grey value of texture is limited within a certain range. Statistical process control binarisation, curve fitting and binary large object (blob) analysis are used to highlight defects.
- Author(s): Toan Duc Bui ; Chunsoo Ahn ; Jitae Shin
- Source: IET Image Processing, Volume 10, Issue 6, p. 483 –494
- DOI: 10.1049/iet-ipr.2015.0489
- Type: Article
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p.
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The localised active contour framework has been widely used for image segmentation because it provides reliable results for inhomogeneous images. However, its computational complexity remains an issue. In this study, the authors introduce a fast algorithm based on the localised active contour framework. A key concept of the proposed algorithm is its consideration of the curve evolution based on the speed function only at active points that change across time, rather than at all points in a narrow band. This approach reduces computational time in the localised active contour. The authors additionally propose a modified speed function to address inhomogeneous image segmentation. The experimental results demonstrate significant advantages of the proposed method over existing methods, both in terms of computational efficiency and segmentation accuracy, for homogeneous and inhomogeneous images.
- Author(s): Chengwu Lu and Minghua Wang
- Source: IET Image Processing, Volume 10, Issue 6, p. 495 –504
- DOI: 10.1049/iet-ipr.2015.0573
- Type: Article
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p.
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This study presents a novel method for separating images into piecewise smooth (cartoon) and texture parts, exploiting both the variational mechanism and Yves Meyer's modelling principle for oscillating patterns. The basic idea presented in this study is the use of total generalised variation (TGV) to model the cartoon components, and its dual TGV* for the oscillation components. Moreover then, the proposed model is numerically implemented by using alternating direction method. Comparative experiments show that the proposed model can better separate cartoon from texture and well preserve small-scale texture information, at the same time reduce efficiently the staircase effects caused by the classical total variation regularisation in the cartoon components.
Context-based prediction filtering of impulse noise images
Adaptive low-complexity motion estimation algorithm for high efficiency video coding encoder
Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule
Parsing fashion image into mid-level semantic parts based on chain-conditional random fields
Scheme for unsupervised colour–texture image segmentation using neutrosophic set and non-subsampled contourlet transform
Inspecting lens collars for defects using discrete cosine transformation based on an image restoration scheme
Fast localised active contour for inhomogeneous image segmentation
Alternating direction method for TGV-TGV* based cartoon-texture image decomposition
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