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Morphological edge detection is a principal component in pattern recognition and machine vision. Traditional edge detection operators only take pixel mutual into consideration. However, the edges are influenced not only by pixel mutual but also by the boundary characteristics. Here, the vector co‐occurrence morphological edge detection operator is proposed, which takes the pixel and boundary information both into consideration. The vector co‐occurrence algorithm is exploited to resist the influence of the noise points and detect the edges from the colour image rather than the grey image. And, we lead to define a precise definition of the manner of sorting high‐dimensional data for the colour image. The experiment results always illustrate the advancement and practicability of our methods against the baseline method. In terms of experiments, the BSDS500 dataset is introduced to compare and analyse with other algorithms. Based on the standard benchmark index evaluation in the BSDS500 dataset, the ODS and AP of various algorithms are compared and analysed.

Image segmentation is a fundamental and challenging task in image processing and computer vision. The colour image segmentation is attracting more attention as the colour image provides more information than the grey image. A variational model based on a convex K‐means approach to segment colour images is proposed. The proposed variational method uses a combination of l1 and l2 regularizers to maintain edge information of objects in images while overcoming the staircase effect. Meanwhile, our one‐stage strategy is an improved version based on the smoothing and thresholding strategy, which contributes to improving the accuracy of segmentation. The proposed method performs the following steps. First, the colour set which can be determined by human or the K‐means method is specified. Second, a variational model to obtain the most appropriate colour for each pixel from the colour set via convex relaxation and lifting is used. The Chambolle–Pock algorithm and simplex projection are applied to solve the variational model effectively. Experimental results and comparison analysis demonstrate the effectiveness and robustness of the method.

Segmentation of the colour image is challenging because the colour information is lost after being projected into three channels of the colour space. Many state-of-the-art colour image segmentation methods are based on monochrome segmentation in one channel of the colour space. However, the optimal performance of a segmentation method usually could not be achieved in a single colour space due to the complexity and diversity of the colour images. In this study, the authors propose to segment the colour image by fusing the slope difference distribution (SDD) clustering results in different colour spaces. For simplicity, the segmentation approach is designed as two-label segmentation and it could be easily generalised to be multiple-label segmentation. The proposed approach is compared with the state-of-the-art colour image segmentation methods both quantitatively and qualitatively. Experimental results verified the effectiveness of the proposed approach.

Colour vision deficiency (CVD) is a genetic condition that has troubled people for a long time. This study proposes an improved colour-to-grey method for CVD using image segmentation and a colour difference model. In this method, the colour image is first segmented using a region growing method so that each region corresponds to one colour. Next, the colour difference is computed between arbitrary segmented region pairs. Finally, the greyscale image is obtained by minimising a target function. Experimental results show that compared with state-of-the-art colour-to-grey methods, the proposed algorithm can improve the E-score by about 10.99%.

In this study, a novel underwater colour image enhancement approach based on hue preserving is presented by combining hue-saturation-intensity (HSI) and HS-value (HSV) colour models. In this study, the proposed wavelet-domain filtering (WDF) and constrained histogram stretching (CHS) algorithms are operated on HSI and HSV colour models, respectively. The degraded image is first converted from red-green-blue colour model into the HSI colour model, wherein the hue component H is preserved and WDF algorithm is executed on the S and I components. Similarly, the image is further converted into the HSV colour model, wherein H component is kept invariant as well and CHS algorithm is applied on the S and V components. The authors' key contribution is that the H preserving method can improve image quality in terms of contrast, colour rendition, non-uniform illumination, and denoising. In addition, experimental results show that the proposed approach outperforms several other state-of-the-art algorithms.

This study introduces a colour-to-greyscale conversion method using colour clustering and multidimensional scaling that preserves region integrity, that is, it preserves edge structure without low-frequency distortion. Edges can be extracted from the resulting greyscale images and the results will be close to those obtainable from the original colour image. We compare with prior-art colour-to-greyscale conversion and a recent colour-edge extractor and show superior performance in each case.

In this paper, a new adaptive vector quantisation with variable block size based on orthogonal polynomials is proposed for colour image coding. The input colour image is partitioned into fixed larger block sizes and the homogeneity/complexity of the local region is tested with the mean-normalised variance classifier. The homogeneous regions are encoded with fixed larger blocks and the complex regions are further divided into smaller block sizes for encoding as it gives the wealth of low-level information such as the high detail and high number of contrast edges. The integer-based orthogonal polynomials transform is then applied on the variable block sizes utilising the inter-correlation property of individual colour planes and interactions among the colour planes so as to reduce the vector dimension and hence the less computational cost. The proposed technique generates two dynamic codebooks and two backup codebooks adaptively and use rate-distortion cost criteria to encode both the homogeneous and complex regions separately. Since the proposed method encodes all the three colour components in single pass, the colour image encoding time is slightly higher than that of grey scale image coding but in contrast to three times in the existing colour image coding techniques.