Histogram-bin-shifting has been previously shown to be an effective method of reversibly watermarking greyscale images. For colour image reversible watermarking, histogram-bin-shifting technique can be extended trivially to RGB colour space. However, direct application of histogram-bin-shifting to the RGB colour image components, results in relatively poor performance of the watermarking algorithm. In order to improve the performance of the algorithm in terms of embedding capacity and distortion whereas preserving the inherent computational simplicity of the histogram-bin-shifting technique, the authors propose a technique of shifting frequency histogram bins of transformed colour components. In this study, the authors consider the YCbCr colour-space. Experimental results on standard test images, prove that the proposed technique achieves high embedding capacity with considerably low distortion.

Iris segmentation is critical for iris recognition. In this study, the authors present a circle-based iris segmentation method for non-ideally captured iris by employing anisotropic diffusion. Our proposal consists of two component steps by which interior and exterior boundaries are localised, respectively. To save computational load, Laplace pyramid (LP) framework is incorporated into both steps. During the first step, when iris has been decomposed into the coarse level by LP, reflections will be removed by anisotropic diffusion and morphologic operations. In the second step, the authors present the innovated curve evolution to detect exterior boundary. Moreover, order statistical filters are employed to enhance the contrast of iris and sclera. Experimental results depict a high correct ratio of segmentation that is more than 96.90% thereby validating the proposed approach.

Diabetic retinopathy (DR), that affects the blood vessels of the human retina, is considered to be the most serious complication prevalent among diabetic patients. If detected successfully at an early stage, the ophthalmologist would be able to treat the patients by advanced laser treatment to prevent total blindness. In this study, the authors propose a technique based on morphological image processing and fuzzy logic to detect hard exudates from DR retinal images. At the initial stage, the exudates are identified using mathematical morphology that includes elimination of the optic disc. Subsequently, hard exudates are extracted using an adaptive fuzzy logic algorithm that uses values in the RGB colour space of retinal image to form fuzzy sets and membership functions. The fuzzy output for all the pixels in every exudate is calculated for a given input set corresponding to red, green and blue channels of a pixel in an exudate. This fuzzy output is computed for hard exudates according to the proportion of the area of the hard exudates. By comparing the results with hand-drawn ground truths, the authors obtained sensitivity and specificity of detecting hard exudates as 75.43 and 99.99%, respectively.

In this study, the authors propose a new semi-supervised low-rank representation graph for pattern recognition. A collection of samples is jointly coded by the recently developed low-rank representation (LRR), which better captures the global structure of data and implements more robust subspace segmentation from corrupted samples. By using the calculated LRR coefficients of both labelled and unlabelled samples as the graph weights, a low-rank representation graph is established in a parameter-free manner under the framework of semi-supervised learning. Some experiments are taken on the benchmark database to investigate the performance of the proposed method and the results show that it is superior to other related semi-supervised graphs.

Random grids (RGs)-based visual secret sharing (VSS) has gained much attention in academia than before to avoid the potential drawbacks in visual cryptography-based VSS. With its still being in infancy, a growing number of researches to RG-based VSS demonstrate a great potential. Although there are some RG-based schemes proposed in the past years for different applications, most of them can still be improved to achieve a better performance. This study proposes a new threshold RG-based VSS scheme with better performance compared with the previous threshold RG-based VSS scheme. The experimental results illustrate the feasibility; the better performance is theoretically analysed.

In this study, the authors propose an adaptive scheme to improve motion estimation of a variational model based on image features and flow quality measurements. Using image features, the authors introduce adaptive functions and inject them into the energy function to fine-tune the estimation process. They propose a hybrid scheme to deal with large motions and improve the accuracy of the flow field. They introduce a trusted-map based on constraints to measure flow quality. They use this map as a reference for the proposed occlusion-aware filter. The proposed filter and hybrid scheme are integrated to correct the flow field iteratively, thus significantly improving the estimation results. The filter also enhances the flow field in occlusion areas. The authors experimental results demonstrate that their method provides sharp flow fields and significantly improved estimation accuracy.

Video text detection plays an important role in semantic-based video analysis. In this study, a new Farsi/Arabic text detection and localisation approach is proposed. First, with the help of edge extraction, artificial corners are obtained and font size estimation is performed. Second, by combining discrete cosine transform coefficients, texture intensity picture is created. Afterwards, a new Local Binary Pattern (LBP) picture is introduced to describe the obtained texture pattern. The input image is then divided into macro blocks and some features are extracted from them and fed into Support Vector Machone (SVM) classifier to categorize them into text and non-text groups. Finally, the candidate text blocks undergo project profile analysis and empirical rules for text localisation. Experimental results demonstrate that the proposed hybrid approach can be used as an automatic text detection system, which is robust to font size, font colour and background complexity.

One promising solution to reduce the computational complexity of discrete cosine transform (DCT) is to identify the redundant computations and to get rid of them. In this study, the authors present a new method to predict zero-quantised DCT coefficients for efficient implementation of intra-frame video encoding by identifying such redundant computations. Traditional methods use the Gaussian statistical model of residual pixels to predict all-zero or partial-zero blocks. The proposed method is based on two key ideas. At first, the bounds of DCT coefficients are derived from the intermediate signals of the Loeffler DCT algorithm instead of calculating the sum of absolute difference (SAD) of residual pixels. The sufficiency conditions are then suitably chosen to predict the zero-quantised coefficients to reduce the arithmetic complexity without degrading the video quality. Simulation results are found to validate the analytical model and show that the proposed prediction eliminates more redundant computations than the existing methods. Moreover, the authors have derived a pipelined VLSI architecture of the proposed prediction scheme which offers a saving of more than 63 and 91% of multiplications of the second stage of one-dimensional DCT for high and low bit-rate intra-video encoding, respectively.

In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor (F), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.