A hue linear color space based on multi-grid optimization and standard color-difference formulas

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

This paper proposes a color image segmentation algorithm based on affinity propagation clustering. Firstly, the color image is converted to other color space; and then color image is sampled, and the sampling data is clustered by affinity propagation algorithm with given the number, then we can get their class label of the rest data according to the maximum similarity rules; finally, using the regional combined methods ,we can remove independent small regions, and get revised image segmentation result. The simulation experiment shows that this method can achieve the application of affinity propagation algorithm in large-scale color image segmentation. And it has a faster processing speed and satisfactory segmentation results, more in line with the human whole visual perception.

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of thea* andb* color channels rejectingL* and (2) a probabilistic approach ona* andb* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parametersa* andb* are not independent of the luminance parameterL* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

Image segmentation is the process of dividing an image into homogenous regions. It is an essential step towards high-level image processing task such as image analysis, pattern recognition and computer vision. Processing of color images has become an important issue due to its huge usage in computer vision applications. It is observed that most of the color image segmentation techniques are derived from monochrome image segmentation. The techniques for segmentation of monochrome images are based on the principles of histogram thresholding, edge detection, region growing etc. Many color image segmentation algorithms using different color models and these principles are proposed. Extraction of objects within an image without a prior knowledge is one of the important issues in segmentation area. Novel approaches such as fuzzy set theory, neural network and neuro-fuzzy based segmentation are coming up to tackle this problem. This paper is an endeavor to review various algorithms and recent advances in color image segmentation.

Color space is one of the bases in the image processing area. Suitable color space can give the suitable description of colors for variant processing. However, in the image processing area, the existing color space cannot show the suitable distribution in color and lightness. In this paper, a novel color space based on RGB color barycenter (RGB-CB) is proposed to describe the color and lightness more intuitively. To prove the effectiveness of the proposed color space, YUV, HSV, L∗a∗b∗, and IPT color spaces are discussed and compared. Experimental results show the proposed color space can perform better effect than other color space in image processing.

This paper describes an efficient algorithm for color image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region projection and region merging. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Finally, region merging merges the segmented regions using fuzzy similarity. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.

In this paper we focus on ancient manuscripts, acquired in the RGB modality, which are degraded by the presence of complex background textures that interfere with the text of interest. Removing these artifacts is not trivial, especially with ancient originals, where they are usually very strong. Rather than applying techniques to just cancel out the interferences, we adopt the point of view of separating, extracting and classifying the various patterns superimposed in the document. We show that representing RGB images in different color spaces can be effective for this goal. In fact, even if the RGB color representation is the most frequently used color space in image processing, it does not maximize the information contents of the image. Thus, in the literature, several color spaces have been developed for analysis tasks, such as object segmentation and edge detection. Some color spaces seem to be particularly suitable to the analysis of degraded documents, allowing for the enhancement of the contents, the improvement of the text readability, the extraction of partially hidden features, and a better performance of thresholding techniques for text binarization. We present and discuss several examples of the successful application of both fixed color spaces and self-adaptive color spaces, based on the decorrelation of the original RGB channels. We also show that even simpler arithmetic operations among the channels can be effective for removing bleed-through, refocusing and improving the contrast of the foreground text, and to recover the original RGB appearance of the enhanced document.

Currently, researchers are developing numerous plant diseases recognition model using image processing and soft computing. The models are mainly based on the extraction of discolored features and applying in various soft computing approaches to automate plant diseases recognition process. The extracted features are statistical, frequency, spatial-frequency or hybrid features of captured images accessed in device-dependent or device-independent color spaces. The performance of diseases recognition system is significantly dependent upon the selection of color spaces and extracted features. This paper presents a comprehensive review of the impact of color spaces and feature sets on machine learning and rule base automated plant diseases classifier. The review performed with six categories of rice plant images with two machine learning and two rule base classifiers. Initially, a thorough literature review performed on the previous investigation based on color spaces and used feature sets for designing diseases recognition model. Then common conditions created to extract feature sets in different color spaces, and applied machine learning and rule base classifier to analyze the impact of color spaces with feature sets. The review presents a detailed discussion on the correlation between color spaces, feature sets, and performance of diseases recognition system. The review results reveal the most relevant features on specific color space for machine learning and rule base classifier. It also deduces that the performance of plant diseases classifier highly dependent upon used color space and extracted features.

This paper presents a segmentation approach for color natural images that performs an edge-preserving smoothing preprocess and a fuzzy-based region growing segmentation. The hue-chroma-intensity (HCI) color space is adopted for its perceptual uniformity. The edge-preserving smoothing preprocess is performed to smooth unnecessary details of an original image and obtain stable HCI information before transformation of color space and image segmentation. The proposed segmentation algorithm is performed in two processes: pixel-label assignment and region merging, which builds up fuzzy-based homogeneity measures for any two neighboring pixels, any pixel with its adjacent regions and any two adjacent regions by making a soft fusion of HCI information. We make some experiments to verify the necessity of the preprocess and the effectiveness of the proposed segmentation algorithm for rough segmentation of color natural images.

Segmentation is a low-level processing aimed at the partition of an image in disjoint regions, each one homogeneous with respect to some properties like intensity, texture, shape, etc. Such a task is needed in many high-level processing and applications in such diverse fields as remote-sensing, medical imaging, video coding, and industrial automation, just to name a few. In this work of thesis, we first deal with the problem of color based image segmentation, that is based on some form of homogeneity in the color/spectral properties of the image, and then move to the more complex task of texture based segmentation, where the aim is to recognize complex structures in the image which are typically non homogeneous in terms of spectral properties. Concerning color based segmentation, we focus on a specific class of Markov Random Field (MRF) probability models, namely the Tree-Structured Markov Random Fields (TS-MRF), recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. As a first contribution, we first extend the model to generic tree structures removing the unnecessary binary constraint, and then devise a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node, and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique, which makes use of a variable-bandwidth strategy based on the k-Nearest Neighbors (k-NN) technique, and is implemented with a speed-up strategy that cuts significantly the computational complexity. Experimental results on synthetic data and real life images from the remote sensing domain prove the potential of the proposed method. Turning to texture based segmentation, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ``fragmentation'' step allows one to find the elementary textures of the model, while the ``reconstruction'' step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural images from the Berkeley dataset. Finally, an application of the TFR algorithm for the unsupervised segmentation of multiresolution remotely sensed images is proposed, based on the significant presence of textural content in last generation high-resolution satellite images. The obtained results further assess the potential of the proposed method for such real life applications.

Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis

In this work, a machine learning methodology is proposed for the issue of color space Euclidization. Given a color difference formula as reference distance law, the Euclidization task consists in finding an injective transformation from the original color space into a real vector space and the corresponding inverse transformation, such that the Euclidean distances in the embedded color space align with the reference color distances. For this, artificial neural networks are devised as function approximators for the color space transformations being sought. Training these neural networks is accomplished through unsupervised learning, making use of random sampling and gradient descent. As key disagreement measure, either the (symmetric) relative isometric disagreement or the standardized residual sum of squares (STRESS) index is considered at a time and incorporated as part of the optimization criterion into the objective function. Comparative evaluation is carried out on well‐established color distance laws, including the CIELAB‐based DE2000 color difference formula. The evaluation results indicate significant performance advantages of the proposed approach over previous contributions.

Image analysis algorithm was developed to provide the classification parameters for defect of yellow apple fruits. The objectives of this work are: 1) To use and test an image processing system which could classify defects on apple surfaces based on color analysis,and2) Extraction of values of Red, Green and Blue (RGB) coordinates and color properties of Hue, Saturation, and Intensity (HSI) from the infected area. Experimental setup of color analysis was used for taking an image for each apple sample to find and identify apple defect. The results indicated the following points: (1) The value of red color component was decreased (162 - 73.4), (170.4 - 104.4), (210.8 - 98.4), and (202.6 - 113.1 values), and the value of green color component was decreased (148.6 - 73.5), (122.2 - 61.2), (181 - 62.4), and (180.1 - 90.3 values). Meanwhile, the blue color component value changed a little, it is ranged (61.6- 61.9), (61.1- 63.4), (61.4- 62.0), and (61.6- 62.7 values), for color, fungi, bruises and operation defects (which classified into four subclasses of each defect), respectively., (2) The value of red color component was decreased (134.6 - 84.4), (203 - 186.6), (143.8 - 98.4), and (202.8 - 174.9 values), and the value of green color component was decreased (100 - 75.2), (131.4 - 118.), (115.4 - 61.4), and (184.8 - 154.6 values), respectively. Meanwhile, the blue color component value changed a little, it is ranged (62.1- 66.8), (61.6- 62.0), (61.4- 63.4), and (62.4- 62.6 values) for insect, scare, mechanical and russet defects of apple fruit (which classified into three subclasses of each defect), respectively., (3) The value of color component (R,G and B) and color properties (H, S and I) were very important to classify defect of apple fruit., (4) Using of image analysis method in estimation of apple defects, to develop of a sorting system for sorting of apples fruit based on color analysis., and (5) Establishment measuring standard for defects of apple fruits according to image processing technique.

segmentation divides an image into several constituent components such as color, structure, shape, and texture. It forms a major research topic for many image processing researchers as the applications are endless. Its applications include image enhancement, object detection, image retrieval, image compression, and medical image processing to name a few. The segmentation of color images is necessary for efficient pattern recognition and feature extraction involving various color spaces such as RGB, HSV and CIE L*A*B*, etc. This paper describes the different cluster based segmentation techniques used for segmenting the different color images and the resultant is analyzed with subjective and objective measures. Initially, registered color images are considered as input. Then the cluster based segmentation techniques namely K-Means clustering, Pillar-Kmeans clustering and Fuzzy C-means (FCM) clustering techniques are applied. Further, the segmented image is analyzed with measures such as compactness and execution time. From the experimental results, it has been observed that K-means and Pillar-Kmeans are the most suitable techniques for RGB, HSV and LAB color spaces than the FCM technique.

We propose a color-image segmentation algorithm by unsupervised classification of pixels. The originality of the proposed approach consists in iteratively identifying pixel classes by taking into account both the pixel color distributions in several color spaces and their spatial arrangement in the image. In order to overcome the difficult problem of the color space choice, the algorithm selects the color space that is well suited to construct the class at each iteration step. The selection criterion is based on connectedness and color homogeneity measures of pixel subsets. In order to tune the sensitivity of segmentation, we introduce a hierarchical criterion that allows us to segment images with different numbers of regions as human observers do. Experiments carried out on the well-known Berkeley segmentation dataset show that this multicolor space approach succeeds in constructing classes that effectively correspond to regions in the image.