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J. Imaging
Special Issues
Advancing Color Image Processing
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Advancing Color Image Processing

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 6097

Special Issue Editors

Prof. Dr. Donatella Giuliani

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40126 Bologna, Italy
Interests: image processing; image segmentation; pattern recognition; edge detection; shape analysis
Dr. Dina Khattab

E-Mail Website
Guest Editor
Scientific Computing Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Interests: computer vision; pattern recognition; machine learning and deep learning; image and video processing

Special Issue Information

Dear Colleagues,

Color image processing embraces two decades of extraordinary growth in its technological applications. Color images contain a wide variety of information and are more complicated than gray-scale images. As human vision is more sensitive to color than gray levels, the importance of color information in digital image processing is therefore greater than ever. However, it does require more memory to store data and longer execution times to process them. Despite its challenges, it provides a path for analyzing images with numerous practical applications, such as automation systems, robotics, visual communications, video surveillance, face recognition, object detection, content-based image retrieval, and medical imaging. Currently, the majority of methods for color image processing are based on gray level images, while some are exclusive to color images. With regard to the latter type of analysis, the main techniques treat each pixel as a three-tuple vector, and apply vector-processing techniques without decoupling color components. Moreover, color representation is sensitive to illumination, so two colors, with the same chromaticity, can be recognized as different if they have a different lighting intensity. Because of the undoubtedly higher level of complexity, the latter category has received much less attention from the scientific community. The transition from scalar- to vector-valued image functions has not yet been generally covered in the scientific literature. Key future contributions to this field could ideally aim to fill this gap, in order to contribute to the literature and improve current color image processing techniques and their practical applications.

We therefore look forward to receiving contributions aimed at deepening the discussion on this important topic. 

Prof. Dr. Donatella Giuliani
Dr. Dina Khattab
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • color image processing
  • multispectral image analysis
  • color space representation
  • color segmentation
  • color edge detection
  • clustering validation indices

Published Papers (2 papers)

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Research

25 pages, 11730 KiB  
Article
A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances
by Giacomo Aletti, Alessandro Benfenati and Giovanni Naldi
J. Imaging 2021, 7(10), 208; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7100208 - 07 Oct 2021
Cited by 11 | Viewed by 1874
Abstract
Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of [...] Read more.
Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8–neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and k−means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace. Full article
(This article belongs to the Special Issue Advancing Color Image Processing)
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36 pages, 9461 KiB  
Article
Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity
by Seena Joseph and Oludayo O. Olugbara
J. Imaging 2021, 7(9), 187; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7090187 - 16 Sep 2021
Cited by 3 | Viewed by 3190
Abstract
Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process [...] Read more.
Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics. Full article
(This article belongs to the Special Issue Advancing Color Image Processing)
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