Images In Color Space Research Articles

Establishment of emotional database and extraction of color psychology of children from images

<p>In the modern society, there is a rapid increase in the need for color psychotherapy for psychological stability and environmental adaption of children for whom the interests of the parents are lacking in the midst of their busy daily schedules. Accordingly, we need to assess the emotional state of children through their drawings, etc. In this thesis, identification is made on color harmony by utilizing information of single and mixed colors in the color image space. Moreover, system for extraction of color psychology from the drawings of children is developed. First, emotional database is established on the basis of color image space. Then, enormous color values are group by clustering the images through the use of K-Means clustering. Emotions are extracted through matching with the database and child’s color psychology is examined by using color distribution chart. Empirical researches on sensibilities that are currently in progress are being pursued largely from psychological and engineering perspectives. Sensibility being understood in the area of psychology can be deemed close to being emotions. Meanwhile, sensibility understood from the engineering perspective can be defined as highly advanced psychological experiences induced in the minds of human beings from perception or cognition by external physical stimulation. This thesis demonstrated the overall excellence through experiment.</p>

Multi -Focus Image Fusion Based on Stationary Wavelet Transform and PCA on YCBCR Color Space

The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this paper, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images in YCbCr color space. As the first step two-level stationary wavelet transform was applied on the Y channel of two source images. The fused Y channel is implemented by using many fusion rule techniques. The Cb and Cr channels of the source images are fused using principal component analysis (PCA). The proposed method performance is evaluated in terms of PSNR, RMSE and SSIM. The results show that the fusion quality of the proposed algorithm is better than obtained by several other fusion methods, including SWT, PCA with RGB source images and PCA with YCbCr source images.

UAV’s Agricultural Image Segmentation Predicated by Clifford Geometric Algebra

Image segmentation is widely used in the field of agriculture to improve the yields and protecting them from pests, herbs, shrubs, and weeds. Precision agriculture is also contributing to the inter and intra crop monitoring. Recently, unmanned aerial vehicles are used widely for acquiring images. In this paper, we purpose Clifford geometric algebra to enhance the segmented images acquired from the UAVs of different agricultural fields. The Clifford geometric algebra is also sometimes used as a collective term for the diverse range of mathematical fields, both classical and modern algebraic mathematics. Previous image segmentation approaches depend upon the intensity of red, green, and blue colors; but the complete perspective could not be obtained from these approaches. Geometric algebra overcomes this limitation and leads to a genuine color space image processing. It is mainly used in the processing of medical images. Subalgebra of the Clifford algebra is Quaternions. We have used this approach in agricultural images. The image segmentation of foreground and background is enhanced using Clifford geometric algebra; hence, the results obtained are fine-tuned segmented images. The anticipated result of our research would have a positive impact on the amelioration of the condition of the farmers and their livelihood.

A novel color space of fundus images for automatic exudates detection

Abstract This paper has compared the performance of different color spaces of fundus images for automatic detection of exudates. A convolutional neural network was employed to assess the performances of different color spaces generated by orthogonal transformation of the original colors in red/green/blue (RGB) space. Experiments were conducted on two publicly available databases: (1) DIARETDB1 and (2) e-Ophtha. Based on the experimental results, this study has proposed a new color space of fundus images with three channels: (i) second eigenchannel of the RGB space, (ii) hue and (iii) saturation channels of Hue/Saturation and Intensity (HSI) space. This achieved an accuracy, sensitivity and specificity of 98.2%, 0.99 and 0.98, respectively. Twenty times 20-fold cross validation technique confirmed that proposed color space obtained higher replicability compared with conventional color spaces.

Product color emotional design considering color layout

AbstractEmotional experience and demand for product colors are important factors in users' decisions to buy and use a product. Therefore, accurately characterizing users' emotional responses of the product's color has become a significant consideration for product color design. However, a product color design problem exists in which it is challenging to accurately and efficiently position users' color image space because consumers have completely different image perceptions when encountering a large number of color schemes generated by different color spatial distributions. For this reason, this article proposes a product color emotional design method that considers the color layout. A table is built of an elemental composition for product color design, which contains color layout forms. This article also establishes a mapping model based on the semantic difference and back propagation neural network between the users' color image perceptions and the elements of the product's color design. The system recommends a color layout form that matches the users' emotional image goal through the k‐nearest neighbor algorithm, and then the form is initialized using a genetic algorithm. The system can realize the solution to the optimal product color scheme by optimizing and evaluating the population. Designers can make intuitive choices and decisions through the product color recommendation system. Through an example of color design for industrial vacuum cleaners, this article shows that the method has satisfactory feasibility and applicability for solving the problem of the optimization of product color design with color layout forms.

Image segmentation based on adaptive K-means algorithm

Image segmentation is an important preprocessing operation in image recognition and computer vision. This paper proposes an adaptive K-means image segmentation method, which generates accurate segmentation results with simple operation and avoids the interactive input of K value. This method transforms the color space of images into LAB color space firstly. And the value of luminance components is set to a particular value, in order to reduce the effect of light on image segmentation. Then, the equivalent relation between K values and the number of connected domains after setting threshold is used to segment the image adaptively. After morphological processing, maximum connected domain extraction and matching with the original image, the final segmentation results are obtained. Experiments proof that the method proposed in this paper is not only simple but also accurate and effective.

Lip segmentation using automatic selected initial contours based on localized active contour model

With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour.

Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of cataract

Background and objectivesRetinal fundus images are extensively used in manually or without human intervention to identify and analyze various diseases. Due to the comprehensive imaging arrangement, there is a large radiance, reflectance and contrast inconsistency within and across images. MethodA novel method is proposed based on the cataract physical model to reduce the generated blurriness of the fundus image at the time of image acquisition through the thin layer of cataract by the fundus camera. After the blurriness reduction the method is proposed the enhancement procedure of the images with an objective on contrast perfection with no preamble of artifacts. Due to the uneven distribution of thickness of the cataract, the cataract surroundings are first predicted in the domain of frequency. Second, the resultant image of first step enhanced by the intensity histogram equalization in the adapted Hue Saturation Intensity (HSI) color image space such as the gamut problem can be avoided. The concluding image with suitable color and disparity is acquired by using the proposed max–min color correction approach. ResultsThe result indicates that not only the proposed method can more effectively enhanced the non-uniform image of retina obtain through thin layer of cataract, but also the resulting image show appropriate brightness and saturation and maintain complete color space information. The projected enhancement method has been tested on the openly available datasets and the result evaluated with the standard used image enhancement algorithms and the cataract removal method. Results show noticeable development over existing methods. ConclusionsCataract often prevents the clinician from objectively evaluating fundus feature. Cataract also affect subjective test. Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of Cataract has shown here to be potentially beneficial.

Detection and segmentation of retinal blood vessel in digital RGB and CIELUV color space fundus images

The identification of retinal blood vessels is very important but crucial task to analyze the severity of the retinal diseases such as diabetic retinopathy, macular degeneration, central retinal vein occlusion, central retinal artery occlusion, retinal detachment and branch retinal vein occlusion. It is evident that huge number of computer based automated algorithms are developed for the accurate detection of blood vessels and optical disc. Most of the work utilizes the retinal fundus images in RGB color space. The proposed work implements the detection and segmentation of retinal blood vessel in RGB and device independent CIELUV color space. The proposed work for the segmentation retinal blood vessel is based on adaptive histogram equalization, median filtering and morphological operations.

A Naturalness Preserved Fast Dehazing Algorithm Using HSV Color Space

Visibility of outdoor scenes is often degraded by haze. The atmosphere particles absorb and scatter the light, causing failure in various computer vision applications. In this paper, we propose a naturalness preserved fast dehazing algorithm using hue, saturation, and value (HSV) color space. First, we process hazy images in HSV color space instead of red, green, and blue (RBG) in order to preserve hue and reduce computational complexity. Second, we use the modified morphological opening operation for estimating the transmission map. In this way, the halo effects are greatly suppressed, and it costs less time than the specially designed filters. The experimental results demonstrate that the proposed algorithm can effectively remove haze. Also, our algorithm maintains naturalness by preserving hue and suppressing halo effects. Moreover, the computational complexity has been largely reduced, thus making our algorithm appropriate for real-time applications.

Estimation of Fractal Dimension in Different Color Model

This article describes how the term fractal dimension (FD) plays a vital role in fractal geometry. It is a degree that distinguishes the complexity and the irregularity of fractals, denoting the amount of space filled up. There are many procedures to evaluate the dimension for fractal surfaces, like box count, differential box count, and the improved differential box count method. These methods are basically used for grey scale images. The authors' objective in this article is to estimate the fractal dimension of color images using different color models. The authors have proposed a novel method for the estimation in CMY and HSV color spaces. In order to achieve the result, they performed test operation by taking number of color images in RGB color space. The authors have presented their experimental results and discussed the issues that characterize the approach. At the end, the authors have concluded the article with the analysis of calculated FDs for images with different color space.

Employing the Local Radon Transform for Melanoma Segmentation in Dermoscopic Images.

In recent years, the number of patients suffering from melanoma, as the deadliest type of skin cancer, has grown significantly in the world. The most common technique to observe and diagnosis of such cancer is the use of noninvasive dermoscope lens. Since this approach is based on the expert ocular inference, early stage of melanoma diagnosis is a difficult task for dermatologist. The main purpose of this article is to introduce an efficient algorithm to analyze the dermoscopic images. The proposed algorithm consists of four stages including converting the image color space from the RGB to CIE, adjusting the color space by applying the combined histogram equalization and the Otsu thresholding-based approach, border extraction of the lesion through the local Radon transform, and recognizing the melanoma and nonmelanoma lesions employing the ABCD rule. Simulation results in the designed user-friendly software package environment confirmed that the proposed algorithm has the higher quantities of accuracy, sensitivity, and approximation correlation in comparison with the other state-of-the-art methods. These values are obtained 98.81 (98.92), 94.85 (89.51), and 90.99 (86.06) for melanoma (nonmelanoma) lesions, respectively.

CBCRS: An open case-based color recommendation system

In this paper, a case-based color recommendation system (CBCRS) is proposed for online color ranges (CRs) recommendation. This system can help designers and consumers to obtain the most appropriate CR of consumer-products (e.g., garments, cars, architecture, furniture …) based on the color image perceptual data of each specific user. The proposed system is an open system, permitting to dynamically integrate new CRs by progressively learning from users’ and designers’ perceptual data. For this purpose, a Color Image Space (CIS) is initially established by using Basic Color Sensory Attributes (BCSAs) to obtain the color image perceptual data of both designers and consumers. Emotional Color Image Words (CIWs) representing CRs are measured in the proposed CIS through a knowledge-based Kansei evaluation process performed by designers using fuzzy aggregation operators and fuzzy similarity measurement tools. Using this method, new CIWs and related CRs from open resources (such as new color trends) can be integrated into the system. In a new recommendation, user's color image perceptual data measured in the proposed CIS regarding different BCSAs will be compared with those of CIWs previously defined in the system in order to recommend new CRs. CBCRS is an adaptive system, i.e. satisfied CRs will be further retained in a Successful Cases Database (SCD) so as to adapt recommended CRs to new consumers, who have similar user profiles. The general working process of the proposed system is based on case-based learning. Through repeated interactions with the proposed system by performing the cycle of Recommendation – Display - Evaluation – SCD adjustment, users (consumer or designer) will obtain satisfied CRs. Meanwhile, the quality of the SCD can be improved by integrating new recommendation cases. The proposed recommendation system is capable of dynamically generating new CIWs, CRs and new cases based on open resources.

The Development of Evaluation Algorithm for Blood Infection Degree

In this paper, a series of processes are performed to assess the degree of infection in a subject’s blood infected with malaria. This process improves the existing testing method’s inefficiencies to assess the results obtained of the infected blood. Image processing algorithm was constructed to establish a standardized test procedure as a way to process the information. The algorithm is built to process the images obtained from the microscopic image of the infected blood and detect the extent of Plasmodium infection which is the cause of Malaria Disease. The images obtained from the infected red blood cells and uninfected red blood cells are processed using the image processing algorithm to distinguish the number of cells infected by malaria in the infected blood. A quantitative analysis is carried out using the results to evaluate the extent of the infection in the blood cells. To establish the relationship between the number of infected and uninfected cells, the algorithm was constructed based on color image processing through HSV color space and morphological image processing techniques. The key configuration for the proposed algorithm is the morphological image processing technique which clearly divide the red blood cells in the micrograph based on the extent of penetration of plasmodium in the blood cells using the clamp splitting method and categorize the infected blood cells.

A PREALIMINARY STUDY OF SHIP DETECTION FROM UAV IMAGES BASED ON COLOR SPACE CONVERSION AND IMAGE SEGMENTATION

Abstract. Ship detection is an inherent process supporting tasks such as fishery management, ship search, marine traffic monitoring and control, and helps in the prevention of illegal activities. So far, sea and shore monitoring has been carried out by ship patrols and aircrafts along with sea vessel detection from data from space-borne platforms. Recently an increase interest in applying images delivered by UAV for marine application due to their advantages such as high spatial resolution, independence on time acquisition can be noticed. While investigating state of the art methods used for ship detection from different platforms using optical images, we found a significant problem with occurrence of a ship wake. This phenomena may prohibit correct detection of ship location and results in overestimating the ship size as the ship and its wake are often considered as being part of the same object in image or wakes are distinguished as a separate ship due to their possible similar brightness compared with sea vessel. In order to reduce the impact of ship wakes we investigated the behavior of images in different color spaces to provide data with little or almost no trace of ship wake. We took into consideration following color spaces: HSV, YCbCr, NTSC, XYZ and L*a*b and investigated each channel from new images. Finally we decided to use 2nd channel of L*a*b space where the ship wakes occurrence were significantly reduced. Object of interest were detected through the use of image segmentation. Applied method uses edge detection based on the gradient magnitude calculation. Afterwards several characteristics such as centroids, major and minor axis, size and orientation were calculated for later use to remove false positives and thus improve accuracy of the proposed method.

5-1 RGB透かし画像のM次元カラー空間における(2M/3)次元的平面(第5部門 画像・映像処理2)

5-1 RGB透かし画像のM次元カラー空間における(2M/3)次元的平面(第5部門 画像・映像処理2)

Hybrid imprinting process to fabricate a multi-layer compound eye for multispectral imaging.

We present a high-precision hybrid imprinting method to fabricate multi-layer micro-optical structures on nonplanar substrates using a custom-built vacuum imprinting system; with the application of kinematic couplings that align the flexible stamps in all six degrees of freedom, a cross-layer pattern registration precision of 400 nm has been achieved on nonplanar substrates. To demonstrate the precision and feasibility of the new process and instrument, we have designed and fabricated a multi-layer artificial compound eye (ACE) for multispectral imaging. The shapes and sizes of all 12 micro-lenses on the ACE are optimized and integrated with different color filers (red, green and blue) so that the light from different channels and of different spectral contents will focus to the same plane, where the photodetector is located. Next, the multi-layer ACE is installed in a portable optical system for simultaneous multispectral imaging, i.e., to perform pattern detection by looking at specific frequency windows. Imaging experiments are devised and performed on (1) color blindness test cards, (2) space image, and (3) breast and gastric tumor samples. The results confirm the system's capabilities of frequency separation, extraction of hidden information, and tumor identification.

Perceptual quality prediction on authentically distorted images using a bag of features approach.

Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore, they learn image features that effectively predict human visual quality judgments of inauthentic and usually isolated (single) distortions. However, real-world images usually contain complex composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images in different color spaces and transform domains. We propose a “bag of feature maps” approach that avoids assumptions about the type of distortion(s) contained in an image and instead focuses on capturing consistencies—or departures therefrom—of the statistics of real-world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features toward improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good-quality prediction power that is better than other leading models.

Fast Single-Image Dehazing Method Based on Luminance Dark Prior

Images captured in hazy weather are usually of poor quality, which has a negative effect on the performance of outdoor computer imaging systems. Therefore, haze removal is critical for outdoor imaging applications. In this paper, a quick single-image dehazing method based on a new effective image prior, luminance dark prior, was proposed. This new image prior arose from the observation that most local patches in the luminance image of a haze-free outdoor YUV color space image usually contain pixels of very low intensity, which is similar to the dark channel prior used with HE for RGB images. Using this new prior, a transmission map was used to estimate the thickness of the haze in an image directly from the luminance component of the YUV color image. To obtain a transmission map with a clear edge outline and depth layer of scene objects, a joint filter containing a bilateral filter and Laplacian operator was employed. Experimental results demonstrated that the proposed method unveiled details and recovered vivid colors even in heavily hazy regions, and provided superior visual effects to many other existing methods.

RGB color coded images in scanning electron microscopy of biological surfaces.

We present here a methodological approach for the creation of color images in scanning electron microscopy by processing grayscale images taken simultaneously from at least three different detectors in a scanning electron microscope. The final color images are then produced by merging together those grayscale images in RGB color space. We show the images from non-conductive standard sample together with those obtained from real microbiological samples. The first one represents a microbial biofilm naturally grown on fiber glass filter. The other shows individual Bacillus subtilis cells from batch culture. All the image handling was done in open source image processing software ImageJ or GNU Image Manipulation Program (Gimp) or, alternatively, in proprietary AnalySis 3.2 Pro software processing suite.