Hue Component Research Articles

Three-dimensional shape measurement for thin objects based on hue-height mapping using color-encoded fringe projection

Three-dimensional (3D) shape measurement based on hue-height mapping using color-encoded fringe projection is proposed for thin objects. The projected color-encoded fringe pattern is encoded by three sinusoidal fringe patterns with 2π/3 shift phase in between into red (R), green (G) and blue (B) channels, separately. It is found that the hue component is of periodicity with the same period as the sinusoidal fringe pattern in the R channel of the color-encoded fringe and the hue distribution in a period is of monotonicity. While this color-encoded fringe pattern is projected onto an object, as long as the deformation of the captured deformed color fringe pattern is not out of a period, the 3D shape of the measured object can be reconstructed directly by hue-height mapping. This method is concise and fast with no color calibration to remove color crosstalk and no time-consuming phase extraction or phase unwrapping. The experimental results show the feasibility and the practicability of the proposed method.

Fusion of PET and MR Brain Images Based on IHS and Log-Gabor Transforms

In medical imaging, positron emission tomography (PET) shows metabolic changes of an organism in pseudo color and magnetic resonance (MR) imaging presents anatomical structures in gray level. The PET and MR brain medical image fusion produces one composite image rendering the anatomical structures with metabolic changes to help doctors effectively diagnose a possible disease. For this purpose, a new fusion method based on Intensity-Hue-Saturation model and log-Gabor wavelet transform is proposed. First, MR image and the intensity component of PET image are decomposed by log-Gabor wavelet transform with suitable decomposition scale. Then, maximum selection’ fusion rule for the high-frequency sub-band and two-stage fusion rule based on weighted-averaging scheme and visibility measure for the low-frequency sub-band are employed. Finally, a new intensity component, obtained by applying reverse log-Gabor wavelet transform to the fused high-and low-frequency sub-bands, along with original hue and saturation components of PET image are converted to obtain our fused color image. In our fused images, both anatomical structures and color changes are rendered with effectively-reduced color distortion. Experimental results on twelve sets, including normal axial, normal coronal, and Alzheimer’s disease brain images, demonstrate that our fusion method outpaces framelet-transform-based method both visually and quantitatively.

Determining the parameters of emotion by analyzing environmental images captured by a mobile device

This paper proposes a method to quantitatively extract human emotions by analyzing images of the surrounding environment captured by a smartphone camera in real time. In the field of psychology, it is known that visual elements such as colors and complexity affect human emotions. Based on this knowledge, we developed an application for the extraction of emotions in real time using the colors and spatial complexity of images obtained with Android smartphones. Among the color components of images, the hue component, which indicates the color, is extracted as the color element and the spatial complexity is extracted by quantitatively determining the high- and low-frequency components that are visible in the images. The corresponding two-dimensional emotion is identified by using two support vector regression modules. The results show that the root-mean-square error between the estimated emotion and the subjectively evaluated emotion is approximately 0.36 on the −1 to +1 axes plane.

Color texture description with novel local binary patterns for effective image retrieval

We propose a novel local color texture descriptor called local binary pattern for color images (LBPC). The proposed descriptor uses a plane to threshold color pixels in the neighborhood of a local window into two categories. To boost the discriminative power of the proposed LBPC operator, local binary patterns of the hue component in the HSI color space, called the local binary pattern of the hue (LBPH) is derived. Further, LBPC, LBPH are fused to derive LBPC+LBPH which when combined with the color histogram (CH) of the hue component results in an effective image retrieval method LBPC+LBPH+CH. The uniform patterns of the two proposed descriptors ULBPC and ULBPH are combined to yield another low dimension local color descriptor ULBPC+ULBPH+CH which provides a good tradeoff between retrieval accuracy and speed. Detailed experiments conducted on Wang, Holidays, Corel-5K and Corel-10K datasets demonstrate that the proposed low dimension descriptors LBPC+LBPH+CH, and ULBPC+ULBPH+CH outperform the state-of-the-art color texture descriptors in terms of retrieval accuracy and speed.

Single Channel Quantum Color Image Encryption Algorithm Based on HSI Model and Quantum Fourier Transform

In order to obtain high-quality color images, it is important to keep the hue component unchanged while emphasize the intensity or saturation component. As a public color model, Hue-Saturation Intensity (HSI) model is commonly used in image processing. A new single channel quantum color image encryption algorithm based on HSI model and quantum Fourier transform (QFT) is investigated, where the color components of the original color image are converted to HSI and the logistic map is employed to diffuse the relationship of pixels in color components. Subsequently, quantum Fourier transform is exploited to fulfill the encryption. The cipher-text is a combination of a gray image and a phase matrix. Simulations and theoretical analyses demonstrate that the proposed single channel quantum color image encryption scheme based on the HSI model and quantum Fourier transform is secure and effective.

High IR reflecting BiVO4-CaMoO4 based yellow pigments for cool roof applications

New high IR reflecting yellow pigments are developed in BiVO4-CaMoO4 powellite type system. The resulting colorants were characterized for their structural as well as optical properties. These solid solutions allow fine tuning of the band gap resulting various shades of yellow hues with high IR reflectance. The yellow hue component (b*) values have been significantly improved to 75.7 in the solid solutions of (BiV)x(CaMo)1-xO4 than that of BiVO4. Application study of selected pigments on concrete cement substrate demonstrate their coloring as well as IR reflective property. These pigments exhibit strong chemical stability and light resistance. Thus the developed pigments are potential candidates as environmentally friendly cool pigments for roofing applications.

Hue preservation based color space transformation for brightness-robust tracking

Brightness-robust tracking is always a tough task in the applications of optical signal processing. Conventional red-green-blue (RGB) space based visual tracking always fails due to the sensitiveness to changes in brightness. Hue is a robust color feature existing in the aforementioned unstable condition, but seldom considered by researchers. In this paper, a hue preservation based color space is designed to replace RGB space and to stabilize the tracking accuracy for tracking algorithms in case of severe brightness variation. First, RGB space is mapped to hue-saturation-intensity (HSI) space and the hue component remains constant through a hue preservation principle. Then, the saturation and intensity components which are easily affected by brightness are regularized through shifting and scaling operations. When finally converted back to RGB space, connection lines that connect the three corners of the RGB cube as well as the geometric center of the lines are selected as the new color space. Experimental results demonstrate that when applied to visual tracking algorithms, our color space transformation method can improve the accuracy and stability under the condition of varying brightness, or even in the low-light-level environment.

High-speed 3D profilometry employing HSI color model for color surface with discontinuities

Structured-light projection methods are facing two remaining challenges. One is a high-speed optical metrology on objects with chromatic surfaces and the other one is avoiding phase errors caused by height steps or spatially isolated surfaces. To overcome them, this paper provides an effective profilometry with a single-shot image by employing the HSI color model to compose the colorful pattern. Three sinusoidal fringes with different phase steps are encoded in RGB channels respectively. The hue component of a deformed pattern is applied to reconstruct the 3D shape of an object. The saturation and the intensity are utilized to correct the hue demodulation. Besides, an effective color calibration procedure is developed to compensate the hue error. Experimental results verify the feasibility of the developed method.

Real time visual tracking using a spatially weighted von Mises mixture model

A real time, kernel based, model-free tracking algorithm is proposed, which employs a weighted von Mises mixture as the target’s appearance model. The mixture weights, which are provided by a spatial kernel, along with the hue values are used in order to estimate the parameters of the weighted von Mises mixture model. The von Mises distribution is suitable for circular data and it is employed in order to eliminate drawbacks in kernel-based tracking caused by eventual shifts of the target’s histogram bins. The weights allow a mean shift-like gradient based optimization by maximizing the weighted likelihood, which would not be feasible in the context of a standard von Mises mixture. Moreover, as only the hue component of the target is involved, many quantities of the algorithm may be pre-calculated for given parameters and therefore the algorithm can perform in real time, which is experimentally confirmed. Finally, it is shown that the proposed method has comparative performance in terms of accuracy and robustness with other state-of-the-art tracking algorithms.

Classification of Serranidae Species Using Color Based Statistical Features

In this study 6 species of Serranidae family (Epinephelus aeneus, Epinephelus caninus, Epinephelus costae, Epinephelus marginatus, Hyporthodus haifensis, Mycteroperca rubra) were classified by using a color based feature extraction method. A database which consists of 112 fish images was used in this study. In each image, a fish was located on a white background floor with the same position and the images were taken from different distances. A combination of manual processes and automatic algorithms were applied on images until obtaining colored fish sample images with a black background. Since the presented color based feature extraction method avoids including background, these images were processed by using an automatic algorithm in order to obtain a solid texture image from the fish and extract features. The obtained solid texture image was in HSV color space and used due to extract species-specific information from the fish samples. Each of the hue, saturation and value components of the HSV color space was used separately in order to extract 7 statistical features. Hence, totally 21 features were extracted for each fish sample. The extracted features were used within Nearest Neighbor algorithm and 112 fish samples from the 6 species were classified with an overall accuracy achievement of 86%.

JCLMM: A finite mixture model for clustering of circular-linear data and its application to psoriatic plaque segmentation

The hue and chroma components of an image pixel carry crucial information that can be exploited to perform segmentation. However, due to its directional property, a circular distribution is required to characterize the hue component. In this article, we propose a mixture of bi-variate circular–linear distributions, for modelling hue and chroma information. The proposed model incorporates a joint distribution of a circular and a linear variable by means of circular copula and offers a flexible architecture that deals with heterogeneous margins for different mixture components. We apply this model for psoriatic plaque segmentation in skin images, using the hue and the chroma information. We observe that the chroma exhibits a heterogeneous distribution in a skin image. Moreover, the joint distribution of hue and chroma possesses multi-modal characteristics. Our model is suitable to perform segmentation under such circumstances. After segmentation, we perform automatic plaque localization by means of a statistical model that exploits hue information of the segmented regions. We conduct the experiments on a set of 75 psoriasis skin images. Both segmentation and localization performances are evaluated with respect to a number of commonly used criteria. The experimental results show that the proposed segmentation model outperforms several competing supervised and unsupervised methods in detecting psoriatic plaque regions in skin images.

Image Fusion for Mars Data Using Mix of Robust PCA

Multi-sensor image fusion is the process of combining relevant information from high spatial resolution image and high spectral resolution image. This paper proposes a pansharpening method for the fusion of Mars images obtained by the Mars Reconnaissance Orbiter satellite (PAN) and Mars Odyssey satellite (THEMIS MS). The method is based on some mix of Intensity, Hue, Saturation (IHS) and robust principal component analysis (RPCA) combined with the discrete wavelet transformation (DWT). Meanwhile, the results obtained with that of the other fusion techniques are compared, and a relatively objective comprehensive evaluation for fused image is used. Experiments show that the proposed algorithm has a significant suppression of artificial textures and less spectral distortion, and its running time is acceptable.

Fuzzy Logic Technique Based Image Contrast Enhancement

Image enhancement plays a vital role in image processing technique. This paper presents a Novel method for image enhancement by using Fuzzy. In this proposed algorithm, the RGB image converted into HSI (Hue, Saturation and Intensity) model, where as Intensity and Hue color considered for Image enhancement after conversion. The Hue component decomposed into directional co-efficient by Fuzzy. The higher directional coefficients are eliminated as it causes artifact and unnatural efforts in a image and the intensity components of image is contrast enhanced by using Fuzzy. The performance of the proposed image enhancement method is compared with existing histogram equalization and contrast limited adaptive histogram equalization based image enhancement. The result of the proposed method achieves satisfactory performance in visualization.

Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity

Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive.

Flow feature analysis and extraction for classifying axisymmetric vector field patterns

A relatively fixed structure vector field which includes clockwise vortex, anticlockwise vortex, convergence, divergence, and saddle is defined as axisymmetric vector fields (AVF) in this study. A method for characterizing and classifying the type of flow patterns in 2-dimension actual vector field for the meteorology application is proposed. First of all, the collected AVF samples are transformed to a directional pseudo-color image by means of mapping them to the hue component space in the HSL color model. Secondly, the directional hue difference and similarity degree to the normal AVF are respectively extracted as two features by the technique of image processing. Thirdly, two improved physical properties (vorticity and divergence) of AVF are introduced and improved for this study. Finally, in the experiment, the probability density distribution for every type of AVF samples is employed to analyze the four features advantages and disadvantages on the five AVF patterns classification. The correlation of the features is discussed by the PCA method. The statistics results show that the features are effective to describe the AVF patterns. By training a classifier based on constructing a decision tree, the classification ability of the features is proved on processing different scale and resolution AVF samples by comparing with traditional methods.

Bleeding and Tumor Detection for Capsule Endoscopy Images Using Improved Geometric Feature

This paper presents a method for detecting bleeding and tumors within capsule endoscopy (CE) images. Because CE can be used for visual, non-invasive examinations of the small bowel, it has recently become widely used. However, as a capsule progresses along the gastrointestinal tract, it collects vast quantities of images that make diagnosis a very time-consuming task. To address this problem, many computational approaches for anomaly detection have been proposed. Common to most approaches is the belief that color, texture, and shape are the most promising features for detecting anomalies within CE images. However, given that the requirements for each type of feature vary according to the anomaly, generally, it is essential to apply a complicated combination of techniques for multiple-feature extraction. In this study, in order to realize a scheme that covers the features of color, texture, and shape and can be applied to lesion areas of various sizes, a geometric image feature called local-contrast-enhanced higher-order local auto-correlation (LCE-HLAC) is proposed. Moreover, although the HSV color space is generally regarded as being appropriate for the analysis of CE images, imbalances in the distributions of utilized hue components limit discriminatory performance for normal and anomalous images. Accordingly, an image pre-processing method that uses a non-linear conversion model for the HSV color space is also proposed. Anomaly detection is implemented using a support vector machine classifier. The results of experiments, conducted with normal, bleeding, and tumor images obtained from 28 patients, demonstrate both the feasibility and superiority of the proposed method for both bleeding and tumor detection tasks.

A sötét egzóta faanyagok helyettesítése gőzölt akác faanyaggal

<p class="p1">Akác faanyagot gőzöltünk, hogy a sötét egzóta faanyagokhoz hasonló színeket kapjunk. A barna színárnyalatok széles skálája hozható létre az akác faanyag gőzölésekor. Megállapítottuk, hogy ezek a színárnyalatok ugyanazt a benyomást keltik, mint a sötét egzóta faanyagok, függetlenül attól, hogy közel vannak-e valamelyik egzóta fafaj színéhez. Néhány egzóta faanyag esetében bemutattuk azokat a gőzölési paramétereket, melyekkel az akác faanyagot ezekhez közel azonos, vagy hasonló színűvé lehet gőzölni. Azt tapasztaltuk, hogy a teljes színeltérés (<em>∆E*</em>) nem követi jól a szemünkkel érzékelt színeltérést, ha a különbséget a vörös színezet eltérése okozza.</p>

Automatic tracking of laparoscopic instruments for autonomous control of a cameraman robot

Background An automated instrument tracking procedure was designed and developed for autonomous control of a cameraman robot during laparoscopic surgery. Material and methods The procedure was based on an innovative marker-free segmentation algorithm for detecting the tip of the surgical instruments in laparoscopic images. A compound measure of Saturation and Value components of HSV color space was incorporated that was enhanced further using the Hue component and some essential characteristics of the instrument segment, e.g., crossing the image boundaries. The procedure was then integrated into the controlling system of the RoboLens cameraman robot, within a triple-thread parallel processing scheme, such that the tip is always kept at the center of the image. Results Assessment of the performance of the system on prerecorded real surgery movies revealed an accuracy rate of 97% for high quality images and about 80% for those suffering from poor lighting and/or blood, water and smoke noises. A reasonably satisfying performance was also observed when employing the system for autonomous control of the robot in a laparoscopic surgery phantom, with a mean time delay of 200ms. Conclusion It was concluded that with further developments, the proposed procedure can provide a practical solution for autonomous control of cameraman robots during laparoscopic surgery operations.

Robust color image segmentation using fuzzy c-means with weighted hue and intensity

In this paper we propose a novel method for color image segmentation. This method uses only hue and intensity components (which are chosen rationally) of image and combines those by adaptive tuned weights in a specially defined fuzzy c-means cost function. The tuned weights indicate how informative every color component (hue and intensity) is. Obtaining tuned weights begins with finding peaks of hue and intensity's histograms and continues by obtaining the table of the frequencies of hue and intensity values and computing entropy and contrast of every color component. Also this method specifies proper initial values for cluster centers with the aim of reducing the overall number of iterations and avoiding converging of FCM to wrong centroids. Experimental results demonstrate that our algorithm achieves better segmentation performance and also runs faster than similar methods.

Lossless Color Image Compression using Double Level RCT in BBWCA

A new lossless image compression scheme for natural color images which is based on double level Reversible Color Transform(RCT) and Bi-level Burrows Wheeler Compression Algorithm(BBWCA) is proposed. The first level RCT from RGB to HSV provide higher reduction in number of unique hue components, thus gives higher compression in case of natural images. The first level RCT followed by the second level RCT from HSV to YUV helps to yield small number of unique Y component values. DC level shifting and twos complement operation will be applied as preprocessing steps to second level RCT. The result of double level RCT is used as the input to BBWCA, in which row-wise BWT followed by column-wise BWT is applied. The compressed image data is formed by using move-to-front(MTF), Run-length-encoding and Entropy coding. The proposed method using double level RCT with BBWCA results in high compression by taking advantage of reduction in hue components of natural images. Among the different color space compared, the proposed method achieves better compression and is well suited for small and large size natural images. Proposed method make use of a double level RCT on the existing BBWCA algorithm and resulted in improving the compression ratio by 46 percentage.