Color Space

Digital image capture devices cannot be used directly as instruments for color measurements because the RGB digital data are not XYZ tristimulus values. The main stages to characterize a digital image capture device as an instrument for color measurements are the spectral and spatial characterization and the color transformation from the RGB device dependent space to a device independent space as CIE-XYZ. In this work we analyze the performance of different optoelectronic imaging sensors in order to transform them into instruments for color measurements. We have compared CCD cameras with different bit-dephts (8, 10 and 12 bits), different color architectures (3 sensors and 1 sensor) and a CMOS camera. Spectral characterization was obtained experimentally measuring the optoelectronic conversion spectral functions, that is, the digital response versus spectral exposure curves. From these curves it is possible to obtain the spectral sensitivities and the color-matching functions. Spatial characterization was obtained experimentally applying a new flat-field correction procedure. Finally, a transformation between the color-matching functions of the image capture device and the color matching functions of the CIE-XYZ color space was applied. The results obtained show the influence of the different characteristics of the optoelectronic imaging sensors on the color measurements.

Automated blood vessel segmentation is an important issue for assessing retinal abnormalities and diagnoses of many diseases. The segmentation of vessels is complicated by huge variations in local contrast, particularly in case of the minor vessels. In this paper, we propose a new method of texture based vessel segmentation to overcome this problem. We use Gaussian and L * a * b * perceptually uniform color spaces with original RGB for texture feature extraction on retinal images. A bank of Gabor energy filters are used to analyze the texture features from which a feature vector is constructed for each pixel. The Fuzzy C-Means (FCM) clustering algorithm is used to classify the feature vectors into vessel or non-vessel based on the texture properties. From the FCM clustering output we attain the final output segmented image after a post processing step. We compare our method with hand-labeled ground truth segmentation of five images and achieve 84.37% sensitivity and 99.61% specificity.

Facial skin detection is an important step in facial surgical planning like as many other applications. There are many problems in facial skin detection. One of them is that the image features can be severely corrupted due to illumination, noise, and occlusion, where, shadows can cause numerous strong edges. Hence, in this papHence, in this pappper, we present an automatic Expectation-Maximization (EM) algorithm for facial skin color segmentation that uses knowledge of chromatic space and varying illumination conditions to correct and segment frontal and lateral facial color images, simultaneously. The proposed EM algorithm leads to a method that allows for more robust and accurate segmentation of facial images. The initialization of the model parameters is very important in convergence of algorithm. For this purpose, we use a method for robust parameter estimation of Gaussian mixture components. Also, we use an additional class, which includes all pixels not modeled explicitly by Gaussian with small variance, by a uniform probability density, and amending the EM algorithm appropriately, in order to obtain significantly better results. Experimental results on facial color images show that accurate estimates of the Gaussian mixture parameters are computed. Also, other results on images presenting a wide range of variations in lighting conditions, demonstrate the efficiency of the proposed color skin segmentation algorithm compared to conventional EM algorithm.

We present a method for figure-ground segregation of moving objects from monocular video sequences. The approach is based on tracking extracted contour fragments, in contrast to traditional approaches which rely on feature points, regions, and unorganized edge elements. Specifically, a notion of similarity between pairs of curve fragments appearing in two adjacent frames is developed and used to find the curve correspondence. This similarity metric is elastic in nature and in addition takes into account both a novel notion of transitions in curve fragments across video frames and an epipolar constraint. This yields a performance rate of 85% correct correspondence on a manually labeled set of frame pairs. Color/intensity of the regions on either side of the curve is also used to reduce the ambiguity and improve efficiency of curve correspondence. The retrieved curve correspondence is then used to group curves in each frame into clusters based on the pairwise similarity of how they transform from one frame to the next. Results on video sequences of moving vehicles show that using curve fragments for tracking produces a richer segregation of figure from ground than current region or feature-based methods.

This paper provides a novel approach towards face area localization through analyzing the shape characteristics of human body. The face region is extracted by determining the sharp increase in body pixels in the shoulder area from neck region. For ensuring face area skin color information is also analyzed. The experimental analysis shows that the proposed algorithm detects the face area effectively and it’s performance is found to be quite satisfactory.

This paper presents algorithms based on image processing for detecting internal blood spots and eggshell dirt by processing acquired images from eggs under different illuminations. The algorithm can also detect the severity of dirt on eggshell. In order to carry out image processing and extract useful features of captured images of eggs by machine vision we developed an algorithm in

This paper presents a color object recognition scheme which proceeds in three sequential steps: segmentation, features extraction and classification. We mainly focus on the first and the third steps here. A color watershed using global and local criteria is first described. A color contrast value is defined to select the best color space for segmenting color objects. Then, an architecture of binary neural networks is described. Its properties relies on the simplification of the recognition problem, leading to a noticeable increase in the classification rate. We conclude with the abilities of such a recognition scheme and present an automated cell sorting system.

The Image segmentation plays an important role in computer vision and image processing areas. In this paper, the use of color segmentation for segmenting acute leukemia images is proposed. The segmentation technique segments each leukemia image into two regions: blast and background. In our approach, the segmentation is based on HSI and RGB color space. The performance comparison between the segmentation algorithms based on HSI and RGB color space is carried out to choose a better color image segmentation for blast detection. The results show that the proposed segmentation technique based on HSI has successfully segmented the acute leukemia images while preserving significant features and removing background noise.

A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. Initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to approach its shape. Based on the output grid of neurons produced by the neural network, palm morphologic characteristics are extracted. These characteristics, in accordance with powerful finger features, allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a likelihood-based classification technique. The proposed system has been extensively tested with success.

Content-based image retrieval (CBIR) is a collection of techniques for retrieving images on the basis of features, such as color, texture and shape. An efficient tool, which is widely used in CBIR, is that of color image histograms. The classic method of color histogram creation results in very large histograms with large variations between neighboring bins. Thus, small changes in the image might result in great changes in the histogram. Moreover, the fact that each color space consists of three components leads to 3-dimensional histograms. Manipulating and comparing 3D histograms is a complicated and computationally expensive procedure. The need, therefore, for reduction of the three dimensions to one could lead to efficient approaches. This procedure of projecting the 3D histogram onto one single-dimension histogram is called histogram linking. In this paper, a new fuzzy linking method of color histogram creation is proposed based on the L*a*b* color space and provides a histogram which contains only 10 bins. The histogram creation method in hand was assessed based on the performances achieved in retrieving similar images from a widely diverse image collection. The experimental results prove that the proposed method is less sensitive to various changes in the images (such as lighting variations, occlusions and noise) than other methods of histogram creation.

The superficial appearance and color of food are the first parameters of quality evaluated by consumers, and are thus critical factors for acceptance of the food item by the consumer. Although there are different color spaces, the most used of these in the measuring of color in food is the L*a*b* color space due to the uniform distribution of colors, and because it is very close to human perception of color. In order to carry out a digital image analysis in food, it is necessary to know the color measure of each pixel on the surface of the food item. However, there are at present no commercial L*a*b* color measures in pixels available because the existing commercial colorimeters generally measure small, non-representative areas of a few square centimeters. Given that RGB digital cameras obtain information in pixels, this article presents a computational solution that allows the obtaining of digital images in L*a*b* color units for each pixel of the digital RGB image. This investigat...

Color space based modulation (CSBM) schemes have the potential to replace conventional intensity based modulation in visible-light communication (VLC) systems. This study attempts to address three major issues of the CSBM scheme in VLC systems, which are (1) performance improvement of CSBM as compared to conventional intensity based modulation, (2) intensity control, and (3) adaption to color variation. We improve the bit error rate (BER) performance of the CSBM scheme by incorporating an error boundary decision method. We analyze our proposed scheme by varying color during communication and introduce a color lookup table in order to obtain a more accurate target color at the receiver. We evaluate the CSBM scheme when the transmitter sends and does not send the target color information, and we also consider the effect of channel coding on the system performance. The computer simulation results, assuming an additive white Gaussian noise (AWGN) channel, demonstrate an uninterrupted communication system under intensity and color variation.

The sensor response of a camera can be represented as the stimulus multiplied by the spectral distribution of an ambient illuminant, the surface reflectance of an object, and camera sensitivity. Surface reflectance is one of the most significant factors that indicates an object's color; therefore its estimation has received widespread attention. Among conventional methods for estimating surface reflectance, principal component analysis (PCA) has an advantage because it uses only one set of principal components for an entire reflectance population. There are limitations, however, in estimating all reflectance using this PCA method with only one set of principal components. In this article, an algorithm is proposed to estimate surface reflectance by using principal components determined by subgroups with similar colors, which are classified from the entire reflectance population. In order to compose a subgroup with similar colors, the Macbeth ColorChecker is utilized to obtain initial representative surface reflectance values for an entire reflectance population; then the Munsell chips are divided into subgroups with different principal components. Moreover, initial representatives have to be modified to avoid biased representations for the population because the Macbeth ColorChecker does not provide optimal representations for the entire reflectance population, even though it is evenly spaced in the CIELAB color space. Therefore, the mean value of each subgroup is used to obtain new representatives, and the new subgroups of reflectance are composed by using the Lloyd quantizer design algorithm. Then, the PCA method is applied for the principal components of the subgroup including surface reflectance. To evaluate its performance, the proposed estimation method was compared with that of a conventional three-band principle component analysis. The proposed method provided better results in its performance.

Due to the significant increase in the already huge collection of digital images that we have today, it has become imperative to find efficient methods for the archival and retrieval of these images. In this research, a content basedhuman facial image detection and retrieval model is proposed for retrieving facial images of humans based on their visual content from an image database. The research proposes a technique of face segmentation based on which a new method of features extraction from the human face is devised. The capability and effectiveness of the color space models (RGB, HSV, and HSI) on facial image retrieval technique are also investigated. Eigenfaces features are used as a domain specific visual content to extract the characteristic feature images of the human facial images, while the color histogram of the facial image is used as a general visual content. Viola-Jones face detection method is employed to obtain the location, extent and dimensions of each face. Moreover, for the measurement of distance and classification purposes, Euclidean distance is utilized. The sample image database consists of 1500 local facial images of one hundred and fifty participants from the University of Malaya (UM), Kuala Lumpur, and some of their friends and families outside the UM. Several experiments based on precision and recall approach were conducted to evaluate the proposed methods. The retrieval result of the facial image given by the proposed method showed excellent improvement comparing to those achieved when using the traditional method of visual features extraction.

Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well. ᭧

During the mining operation, ore sorting and directing different grade ores to different processing circuits is a manual task in most of working mines, but this work puts a step forward toward automation of this process. The radical development in the area of image and data processing allows speedy processing of the full color digital images for the preferred investigations. In this paper, an approach has been proposed to classify the ores for blast furnace feed, based on the visual texture of the ore particles. The visual texture of ore particles vary with the mineral contents, for example, blue dust, hard ore, soft ore, etc. This information can be quantified by using image processing technique in red, green, and blue color space and first- and second-order statistical analysis. Commonly used Hartlics textural features were calculated along with red, green, and blue color values for 5 × 5-pixel size windowpanes extracted from five separate images. Results obtained show encouraging accuracy to apply the approach to develop an expert system for online ore quality monitoring to control the ore blending in the feed ore circuits as well as separating gangue minerals present in the feed ores. Matlab 6.5 was used for visual textural analysis and classification. أثناء عملية التعدين ، وخام الفرز وتوجيه الصف خامات مختلفة لمعالجة مختلف الدوائر المهمة هو دليل في معظم المناجم العاملة. ولكن هذا العمل يضع خطوة إلى الأمام نحو أتمتة هذه العملية. تطوير جذري في مجال معالجة الصور والبيانات يسمح للمعالجة السريعة لون الصور الرقمية الكاملة لتحقيقات المفضل. في هذه الورقة نهجا وقد اقترحت لتصنيف خامات تغذية لالفرن العالي ، على أساس الملمس البصرية للجسيمات الخام. نسيج البصرية للجسيمات خام تختلف مع محتويات المعدنية مثلا الغبار الأزرق ، وخام الثابت ، يمكن أن كمية خام الناعمة الخ هذه المعلومات باستخدام تقنية معالجة الصور في RGB لون الفضاء ، والأول والثاني من أجل التحليل الإحصائي. يشيع استخدامها Hartlics السمات التكوينية حسبت مع الأحمر والأخضر والأزرق قيم اللون لمدة 5 × 5 بكسل وحجم النوافذ المستخرج من خمس صور منفصلة. النتائج المشجعة التي تم الحصول عليها تظهر دقة لتطبيق نهج لتطوير نظام خبير لخام على الخط لرصد نوعية للسيطرة على مزج خام في دوائر خام تغذية ، وكذلك فصل الغث المعادن الموجودة في خامات تغذية. ماتلاب 6.5 كانت تستخدم لتحليل التكوينية البصرية والتصنيف.

Spot color is widely used in commercial, product or packaging printing to obtain a colorful appearance. With the combination of the right software, inks and media, an inkjet printer can be treated as a digital proofer for spot color printing, providing significant time and cost savings compared to conventional procedures for jobs approval. An Epson Stylus Pro 4000 digital printer combined with two commercially available RIPs were tested and compared. ICC profiles were generated for the Epson Stylus Pro 4000 printer, using the actual production printing substrate and manufacturer recommended proofing paper. Color gamuts of different output combinations were compared and the quality of spot color reproduction was evaluated in terms of the ΔE in L*a*b* color space for selected spot colors.

Rendering packages are used by visual psychophysicists to produce complex stimuli for their experiments, tacitly assuming that the simulation results accurately reflect the light-surface interactions of a real scene. RA-DIANCE is a physically based, freely available, and commonly used rendering software. We validated the calculation accuracy of this package by comparing simulation results with measurements from real scenes. RADIANCE recovers color gradients well but the results are shifted in color space. Currently, there is no better simulation alternative for achieving physical accuracy than by combining a spectral rendering method with RADIANCE.

We present a paint model for use in interactive painting systems that captures a wide range of styles similar to oils or acrylics. The model includes both a numerical simulation to recreate the physical flow of paint and an optical model to mimic the paint appearance.Our physical model for paint is based on a conservative advection scheme that simulates the

A shelf-life model based on the storage temperature was developed for a homogenized fruit-based baby food. Bottles of apple compote for infant feeding were collected from a food company and stored at three different temperatures (23, 30 and 37 C) during 420 days. CIELAB color space parameters, vitamin C, 5-hydroxymethylfurfural were measured and sensory analysis (sensory attributes and overall acceptability) was carried out during the length of the study. Weibull Hazard Method was utilized to set the shelf-life end-point of the product at 37 C according to overall acceptability score given by the sensory panel. Considering a 50% probability of panellists to find the product as being unacceptable, the end of shelf-life for the apple-based beikost stored at 37 C was achieved after 346 days. The statistical analysis of the data enabled us to select the most adequate zero-and first-order kinetic equations for both physicochemical and sensory attributes in samples stored at 37 C. Color CIELAB parameters, vitamin C and sensory attributes (color and taste) were selected as the critical parameters. Their rejection times at 23 and 30 C storage temperatures were obtained by extrapolation of the results given by Weibull method at 37 C. Finally, rejection times for critical parameters were used to propose a shelf-life equation that showed 4.5 and 3.4 years of shelf-life when stored at 20 and 23 C, respectively.

Through a comprehensive literature review, this article identifies and discusses barriers to recruitment and retention of faculty of color. Marginalization, racism and sexism manifested as unintended barriers are presented as a few of the barriers faculty of color face in successfully navigating the tenure process. Informed by this literature review, we conducted a self-study that presents the experiences of four faculty of color navigating the tenure process in a predominately white Research Institution. The purpose of this study was to share the experiences of three junior faculty of color as they navigate the tenure process, and one tenured faculty of color who is informally mentoring them through the process. This article highlights the findings of one component of a broader study: focus group discussions about how diversity efforts and activities are subsequently evidenced in teaching, research agendas and service. Four themes are presented: Academic Identity; Confronting Diversity, Mentoring, and Safe Spaces. A discussion of the consequences of these findings on faculty of color retention and recruitment is included. Recommendations are made to other predominately white institutions on how to address issues facing faculty of color.

Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well. ᭧

In this paper, we present a new approach for color texture classification by use of Haralick features extracted from co-occurrence matrices computed from Local Binary Pattern (LBP) images. These LBP images, which are different from the color LBP initially proposed by Mäenpää and Pietikäinen, are extracted from color texture images, which are coded in 28 different color spaces. An iterative procedure then selects among the extracted features, those which discriminate the textures, in order to build a low dimensional feature space. Experimental results, achieved with the BarkTex database, show the interest of this method with which a satisfying rate of wellclassified images (85.6%) is obtained, with a 10-dimensional feature space.

The work is done based on a minor project as a part of Master of Science (Data Science & Analytics) course by Ms Sidra Mehtab under the guidance of Prof. Jaydip Sen. The work involves implementation of various object detection and tracking in a video using methods like: (i) frame differencing, (ii) color-space transformation, (iii) background separation, (iv) optical flow, (v) Haar cascade-based classifier. In addition to these detection and tracking methods, a very popular and efficient edge detection method - Canny's Edge Detection - is also implemented. All implementations are done using OpenCV libraries in Python. Extensive results are presented and the results are analyzed in detail.

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm.

IS HAPPENING DUE TO BUBBLE TECH

THERE HAVE BEEN SYSTEM WIDE CHANGES BEFORE IN THE NATURE OF LIGHT!!!  -  ANOTHER ONE IN THE FORM OF A DREAM TIME /  GOLDEN AGE APPROACHES – WHICH ACTUALLY IT’S A EUPHORIC LT PURPLE AGE REALLY AND IT IS UNSTOPPABLE.  A FUNDAMENTAL CHANGE IN THE ENTIRETY OF CREATION I.E. THE ELIMINATION OF DUALITY VIA EUPHORIA IS HAPPENING AND WILL CONTINUE UNTIL EVERYTHING IS ENLIGHTENED. 
https://www.academia.edu/41144909/SYSTEM_WIDE_CHANGE_EUPHORIA 

THE ENTIRETY OF CREATION IS CHANGING – EUPHORIA IS EXPUNGING THE DUALITY OF FEAR & LOVE – EVEN IN THE AETHERS 
https://www.academia.edu/44044980/THE_ARTIST_S_GUIDE_TO_5D 
https://www.academia.edu/44420500/5D_EUPHORIC_TEAMS_THE_ARTISTS_VISION

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image 'features' that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent 'subspaces' of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, materialbased segmentation, and motion estimation.

A computer vision system (CVS) was implemented to quantify standard color of fruit and vegetables in sRGB, HSV and L*a*b* color spaces, and image capture conditions affecting the results were evaluated. These three color spaces are compared in terms of their suitability for color quantification in curved surfaces. The results show that sRGB standard (linear signals) was efficient to define the mapping between R′G′B′ (no-linear signals) from the CCD camera and a device-independent system such as CIE XYZ. The CVS showed to be robust to changes in sample orientation, resolution, and zoom. However, the measured average color was shown to be significantly affected by the properties of the background and by the surface curvature and gloss. Thus all average color results should be interpreted with caution. L*a*b* system is suggested as the best color space for quantification in foods with curved surfaces.

We have analyzed the properties of the HSV (Hue, Saturation and Value) color space with emphasis on the visual perception of the variation in Hue, Saturation and Intensity values of an image pixel. We extract pixel features by either choosing the Hue or the Intensity as the dominant property based on the Saturation value of a pixel. The feature extraction method has been applied for both image segmentation as well as histogram generation applications -two distinct approaches to content based image retrieval (CBIR). Segmentation using this method shows better identification of objects in an image. The histogram retains a uniform color transition that enables us to do a window-based smoothing during retrieval. The results have been compared with those generated using the RGB color space.

A novel color image histogram equalization approach is proposed that exploits the correlation between color components and it is enhanced by a multi-level smoothing technique borrowed from statistical language engineering. Multi-level smoothing aims at dealing efficiently with the problem of unseen color values, either considered independently or in combination with others. It is applied here to the HSI color space for the probability of intensity and the probability of saturation given the intensity, while the hue is left unchanged. Moreover, the proposed approach is extended by an empirical technique, which is based on a hue preserving non-linear transformation, in order to eliminate the gamut problem. This is the second method proposed in the paper. The equalized images by the two methods are compared to those produced by other well-known methods. The better quality of the images equalized by the proposed methods is judged in terms of their visual appeal and objective figures of merit, such as the entropy and the Kullback–Leibler divergence estimates between the resulting color histogram and the multivariate uniform probability density function.

In this paper, we propose a color image quantization algorithm based upon TBM. In this context, we consider that the color quantization problem can be viewed as clustering problem of the color-space into P clusters. Using TBM, we define a top-down evidential clustering algorithm which iteratively decreases the number of clusters of the color space into P clusters. This convergence is ensured using a novel criterion based upon the pignistic probability function. The P clusters provide the new reduced color palette and a quantized color image is computed. This quantization method is completely automatic and preserves the final result from any initial condition. Experiments on various images show the algorithm efficiency for color quantization and highlight the efficiency of TBM for color image processing.

In recent years, many methods have been proposed for the spectral-based characterization of inkjet printers. To our knowledge, the majority of these are based on a physical description of the printing process, employing different strategies to deal with mechanical dot gain and the physical interaction among inks. But our experience tells us that as printing is a physical process involving a large number of effects and unpredictable interactions, it is not unusual to be unable to fit a mathematical model to a given printer. The question becomes, therefore, whether it is feasible, and to what degree, to employ an analytical printer model even if it appears to be incapable of describing the behavior of a given device. A key objective of our work is to obtain a procedure that can spectrally characterize any printer, regardless of the paper and the printer driver used. We consider in fact the printers RGB devices, and incorporate the printer driver operations, even if they are unknown to us, into the analytical model. We report here our experimentation on the use of genetic algorithms to tune a spectral printer model based on the Yule-Nielsen modified Neugebauer equation. In our experiments we have considered three different inkjet printers and used different kinds of paper and printer drivers. For each device the printer model has been tuned, using a genetic algorithm, on a data set of some 150 measured reflectance spectra. The test set used for model evaluation was composed of 777 samples, uniformly distributed in the RGB color space.

Fruit peel coloring is one of the most important measures of commercial values in various citrus fruits. It is well documented that a gaseous plant hormone, ethylene, regulates the peel color changes associated with fruit ripening. To handle the peel color quality, it is necessary to develop a universal measure of ethylene-directed color changes. Here, we proposed a novel colorimetric formula: y* = [ (L* n • max-L* t) 2 + (a* n • max-a* t) 2 + (b* n • max-b* t) 2 ] 1/2 (y* > 0), based the CIE 1976 L*a*b* colour space units, which reflect the wholistic changes in fruit color. Since all known colorimetric values are specialized for estimation of the changes in certain single pigment, our formula may be useful for assessing the total fruit color development in a controlled atmosphere.

Luminance-dependent change in color appearance-the Bezold-Brücke effect-was investigated in protanopes and related to that in normal trichromats. Spectral lights were presented at six luminance levels covering mesopic, low, and high photopic vision-across three log steps from 0.76 to 760 Td. To judge color appearance, a variant of the color-naming method was used with four primary basic color terms and a "White" response. This modification enabled us to examine apparent saturation changes along with the Bezold-Brücke hue shift. Color-naming frequency functions were acquired across ten presentations of each stimulus. Since protanopes name colors idiosyncratically, changes in color appearance cannot be quantified directly from the color-naming functions. To circumvent the difficulty, these functions were transformed into color similarity measures for analysis with multidimensional scaling purported to reconstruct individual color spaces. In these, luminancedependent shifts in color appearance were represented by means of geometric displacements. We found that for normal trichromats, shifts measured in this way agreed with those derived in our study directly, and with the hue shifts reported in earlier studies. For protanopes, contrary to some models of dichromatic vision, changes in color appearance are significant and indicate superimposed shifts in hue and saturation. The results obtained for normal trichromats, especially for protanopes, imply that nonlinearity in the yellow-blue opponent system is insufficient to explain the Bezold-Brücke effect, given the nature of the saturation shift and the demonstrated divergence between unique hues and invariant hues.

We propose a novel approach for content based color image classification using Support Vector Machine (SVM). Traditional classification approaches deal poorly on content based image classification tasks being one of the reasons of high dimensionality of the feature space. In this paper, color image classification is done on features extracted from histograms of color components. The benefit of using color image histograms are better efficiency, and insensitivity to small changes in camera view-point i.e. translation and rotation. As a case study for validation purpose, experimental trials were done on a database of about 500 images divided into four different classes has been reported and compared on histogram features for RGB, CMYK, Lab, YUV, YCBCR, HSV, HVC and YIQ color spaces. Results based on the proposed approach are found encouraging in terms of color image classification accuracy.

Clear Color management with ICC profiles is used to investigate reproduction of Pantone Matching System colors for different digital printers. Three digital printers: XEROX Phaser 8200, FUJI PictroProof II and EPSON Stylus Photo 2200 were tested and their measured color gamuts were compared. The quality of reproduction is evaluated in terms of the usual ΔE in L*a*b* color space for selected PANTONE Matching System colors.

Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking objectionable content. In this paper, we investigate and evaluate (1) the effect of color space transformation on skin detection performance and finding the appropriate color space for skin detection, (2) the role of the illuminance component of a color space, (3) the appropriate pixel based skin color modeling technique and finally, (4) the effect of color constancy algorithms on color based skin classification. The comprehensive color space and skin color modeling evaluation will help in the selection of the best combinations for skin detection. Nine skin modeling approaches (AdaBoost, Bayesian network, J48, Multilayer Perceptron, Naive Bayesian, Random Forest, RBF network, SVM and the histogram approach of Jones and Rehg ) in six color spaces (IHLS, HSI, RGB, normalized RGB, YCbCr and CIELAB) with the presence or absence of the illuminance component are compared and evaluated. Moreover, the impact of five color constancy algorithms on skin detection is reported. Results on a database of 8991 images with manually annotated pixel-level ground truth show that (1) the cylindrical color spaces outperform other color spaces, (2) the absence of the illuminance component decreases performance, (3) the selection of an appropriate skin color modeling approach is important and that the tree based classifiers (Random forest, J48) are well suited to pixel based skin detection. As a best combination, the Random Forest combined with the cylindrical color spaces, while keeping the illumi-Email addresses: [email protected] (Rehanullah Khan), [email protected] (Allan Hanbury), [email protected] (Julian Stöttinger), [email protected] (Abdul Bais) nance component outperforms other combinations, and (4) the usage of color constancy algorithms can improve skin detection performance.