Color Image Processing Methods Research Articles

Quaternion Chromaticity Contrast Preserving Decolorization Method Based on Adaptive Singular Value Weighting

Color image decolorization can not only simplify the complexity of image processing and analysis, improving computational efficiency, but also help to preserve the key information of the image, enhance visual effects, and meet various practical application requirements. However, with existing decolorization methods it is difficult to simultaneously maintain the local detail features and global smooth features of the image. To address this shortcoming, this paper utilizes singular value decomposition to obtain the hierarchical local features of the image and utilizes quaternion theory to overcome the limitation of existing color image processing methods that ignore the correlation between the three channels of the color image. Based on this, we propose a singular value adaptive weighted fusion quaternion chromaticity contrast preserving decolorization method. This method utilizes the low-rank matrix approximation principle to design a singular value adaptive weighted fusion strategy for the three channels of the color image and implements image decolorization based on singular value adaptive weighting. To address the deficiency of the decolorization result obtained in this step, which cannot maintain global smoothness characteristics well, a contrast preserving decolorization algorithm based on quaternion chromaticity distance is further proposed, and the global weighting strategy obtained by this algorithm is integrated into the image decolorization based on singular value adaptive weighting. The experimental results show that the decolorization method proposed in this paper achieves excellent results in both subjective visual perception and objective evaluation metrics.

Review of Quaternion-Based Color Image Processing Methods

Images are a convenient way for humans to obtain information and knowledge, but they are often destroyed throughout the collection or distribution process. Therefore, image processing evolves as the need arises, and color image processing is a broad and active field. A color image includes three distinct but closely related channels (red, green, and blue (RGB)). Compared to directly expressing color images as vectors or matrices, the quaternion representation offers an effective alternative. There are several papers and works on this subject, as well as numerous definitions, hypotheses, and methodologies. Our observations indicate that the quaternion representation method is effective, and models and methods based on it have rapidly developed. Hence, the purpose of this paper is to review and categorize past methods, as well as study their efficacy and computational examples. We hope that this research will be helpful to academics interested in quaternion representation.

OFFICE SCANNER USE FOR OBTAINING ANALYSIS OBJECTS DIGITAL IMAGES AND THEIR COLOR CHARACTERISTICS COMPUTER DETERMINATION

Recently, the practice of analytical chemistry has included the method of chemical chromaticity, based on the measurement of the integral characteristics of the color reflected from the surface of the analyte and which makes it possible to distinguish between spectrally close substances. With the spread scanners and computer programs for color image processing, a fast and objective method for assessing the color characteristics of colored samples has appeared, opening up the prospect of using chemical chromaticity in the chemical analysis of various objects in the absence of special spectrophotometric equipment or with minimal use of it. Thus, the process of chemical chromaticity analysis is divided into two parts: obtaining a high-quality digital image of the analyzed object and processing the resulting file in a graphics editor. The article describes in detail the methodological issues of scanning solutions, powders, surfaces of dosage forms using an office scanner and processing the resulting digital images in a graphics editor. On the example of model samples, standard solutions of salts FeCl3⋅6H2O, CoCl2⋅6H2O, CuSO4⋅5H2O, it is shown that in the range of 6–60 g/l their concentrations can be determined with sufficient accuracy as with using its absorption spectra (registered on a Lambda 9 PerkinElmer spectrophotometer) or spectra of diffuse reflection (on the same instrument using a scanning sphere B013–9941), and when using an desktop scanner HP scanjet 2200c with HP PrecisionScan LTX 1.2 software, followed by digital processing of the scan in Photoshop CS3. Acquisition and analysis of digital images of analytes can be considered as an inexpensive replacement for spectrophotometry in a certain interval of concentrations, comparable with it in terms of accuracy and reliability of determinations. The introduction of this technique into the educational process makes it possible to increase the efficiency of teaching students when mastering the skills of converting color characteristics into optical density values and to better understand the principles of the science of color and light absorption. The method can be made mobile for analysis of results in remote areas using a portable scanner powered by battery.

A Preliminary Exploration to Make Stereotactic Surgery Robots Aware of the Semantic 2D/3D Working Scene

Scene perceptual ability is key to developing autonomy and intelligence in surgical robots. This study helps stereotactic surgical robots detect and segment key objects in unstructured surgical scenes. First, we construct a neurosurgery robot working scene dataset. Next, we propose a 2Dimage-scene-aware pipeline that integrates a Mask R-CNN (mask region-based convolutional neural network) with a conditional random field and a superpixel method; the pipeline detects and segments key objects, such as the patient’s head, head frame, and body. Then, we establish a multiview projection voting and supervoxel fusion pipeline that extracts further information from a 3D point cloud scene. The proposed method was tested in different clinical scenarios, and the results show that the method can detect and segment specific surgical objects and achieves comparable accuracy and stability on both 2D images and 3D point cloud data. The average precision (AP) and average 2D and 3D Dice scores for the patient’s head were 97.65, 91.6, and 92.6, respectively. Better segmentation performances can be achieved when the data-based neural network method further integrates the traditional color and contour-based image processing methods. The proposed solution allows stereotactic surgical robots to better understand their surroundings, provides semantic information useful for subsequent tasks, and lays a foundation for autonomous stereotactic surgical robots.

Study on the Relationship between Accelerated Aging, Color Characterization and Properties of Natural Fibers

ABSTRACT Fiber yellowing is an important indicator to distinguish the quality of natural fibers. Natural fibers such as cotton, kenaf and wool are subjected to production conditions such as UV light, heat and acid-base treatment during their growth and processing. These will affect the fiber appearance and performance. In this paper, the laboratory accelerated aging method was used to replace the natural aging of the fabric, and the color characteristics of the aged fiber are quantitatively analyzed through the color measurement system and image processing method. Meanwhile, the property changes of fiber composition destruction and fiber strength decrease were also measured and compared by infrared spectrum analysis and monofilament tensile test. Thermal aging destroyed the surface composition of cotton and kenaf fibers, thereby reducing the mechanical properties of fibers. Among them, the strength of kenaf fiber was reduced to 1/2 of the original after 8 hours of thermal aging. On the contrary UV aging damaged wool fiber even more. The most obvious result was that after 8 hours of UV irradiation, the strength of silk fiber dropped to 1/3 of its original strength. The mechanical properties of kenaf fiber were affected more obviously by acid and alkali than protein fiber.

FPGA‐based favourite skin colour restoration using improved histogram equalization with variable enhancement degree and ensemble extreme learning machine

This paper presents skin color enhancement based on favorite skin color to agree with user-defined favorite skin color using improved histogram equalization with variable enhancement degree (IHEwVED) and machine learning methods. The skin color to be adjusted in the input image is shifted to favorite skin color by using novel control parameters of the proposed IHEwVED method. Three different novel display device-dependent color image processing methods are introduced based on hsv and yiq color space to obtain the desired enhanced output images. A reduced convolutional neural network and the novel ensemble extreme learning machine (EELM) architectures are developed and implemented in a field-programmable gate array to test, synthesize, and validate the recognition capability of the user-defined favorite skin color. The less computational complex proposed IHEwVED-EELM method recognizes 45 to 50 favorite skin color per second of test images by consuming 0.035 second training time with training root mean square error (RMSE) of 0.0048 and testing RMSE of 0.01208. Finally, a stand-alone favorite skin color restoration system is developed using the high-speed video processor NI-PXI-1031 based on the IHEwVED-EELM method in the Python-OpenCV environment. The laboratory experimental performances ascertain the real-time ability of the proposed favorite skin color restoration method.

تشخیص تازگی گوشت گوساله به کمک پردازش تصویر و سطح پاسخ

گوشت و محصولات مرتبط با آن ارزش تجاریی بالایی داشته و یکی از مهمترین اقلام سبد غذایی خانوار را تشکیل می‌دهد. با توجه به عرضه گوشت در قصابی‌ها و نیز استفاده از آن در کارخانه‌های فرآوری نیاز به اطمینان از تازگی آن احساس می‌شود چه بسا گوشت با گذشت از زمان ذبح آن نه تنها از کیفیت آن کاسته می‌شود بلکه ممکن است به سبب فساد موجب بیماری نیز شود لذا هدف از این تحقیق تشخیص تازگی گوشت گوساله با تخمین مدت زمان گذشته از ذبح می‌باشد. برای این منظور از سه قسمت ران، سر دست و گردن گوساله ذبح شده نمونه‌هایی تهیه شد و نمونه‌ها در دو محیط استاندارد و رایج یکی در یخچال با دمای 3 درجه سانتی‌گراد و دیگری در محیط خنک با دمای میانگین 8 درجه سانتی‌گراد نگهداری شد سپس از نمونه‌ها در زمان‌های مشخص (هر 2 ساعت) تصویربرداری شد و از تصاویر تهیه شده توسط پردازش تصویر و به کمک نرم‌افزار MATLAB پارامترهایی استخراج گردید. از بین پارامترهای استخراج شده توسط آنالیز حساسیت سه پارامتر به‌عنوان عامل تأثیرگذار در مدت زمان گذشته از ذبح انتخاب گردید که عبارتند از: خصوصیت بافت، میزان کنتراست و زبری سطح گوشت. در نهایت به کمک روش سطح پاسخ و توسط نرم‌افزارDesign Expert مدل‌های مناسبی تدوین و بهینه گردید.

Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine

In order to improve the prediction ability of welding quality during high-power disk laser welding, a new approach was proposed and applied in the classification of the dynamic features of metal vapor plume. Six features were extracted through the color image processing method. Three features, including the area of plume, number of spatters, and horizontal coordinate of plume centroid, were selected based on the classification accuracy rates and Pearson product-moment correlation coefficients. A support vector machine model was adopted to classify the welding quality status into two categories, good or poor. The results demonstrated that the support vector machine model established according to the selected features had satisfactory prediction and generalization ability. The classification accuracy rate was higher than 90%, and the model could be applied in the prediction of welding quality during high-power disk laser welding.

Prediction of pork color attributes using computer vision system

Color image processing and regression methods were utilized to evaluate color score of pork center cut loin samples. One hundred loin samples of subjective color scores 1 to 5 (NPB, 2011; n=20 for each color score) were selected to determine correlation values between Minolta colorimeter measurements and image processing features. Eighteen image color features were extracted from three different RGB (red, green, blue) model, HSI (hue, saturation, intensity) and L*a*b* color spaces. When comparing Minolta colorimeter values with those obtained from image processing, correlations were significant (P<0.0001) for L* (0.91), a* (0.80), and b* (0.66). Two comparable regression models (linear and stepwise) were used to evaluate prediction results of pork color attributes. The proposed linear regression model had a coefficient of determination (R2) of 0.83 compared to the stepwise regression results (R2=0.70). These results indicate that computer vision methods have potential to be used as a tool in predicting pork color attributes.

Classification of Color Images of Dermatological Ulcers

We present color image processing methods for the analysis of images of dermatological lesions. The focus of the present work is on the application of feature extraction and selection methods for classification and analysis of the tissue composition of skin lesions or ulcers, in terms of granulation (red), fibrin (yellow), necrotic (black), callous (white), and mixed tissue composition. The images were analyzed and classified by an expert dermatologist into the classes mentioned above. Indexing of the images was performed based on statistical texture features derived from cooccurrence matrices of the RGB (Red, Green, and Blue), HSI (Hue, Saturation, and Intensity), L*a*b*, and L*u*v* color components. Feature selection methods were applied using the Wrapper algorithm with different classifiers. The performance of classification was measured in terms of the percentage of correctly classified images and the area under the receiver operating characteristic curve, with values of up to 73.8% and 0.82, respectively.

Fourier-Transform-Based Method for Automated Steel Bridge Coating Defect Recognition

Intelligent computerized system can analyze and process vast amounts of data instantaneously in the civil engineering domain recently. However, most of previously developed image assessment methods for steel bridge rust inspection could not directly deal with acquired images. The acquired images should be firstly classified into two groups, non-defect or defect, by naked eyes or using the method reported in Lee's PhD dissertation (Lee 2005). Lee's method works effectively with blue-coated steel bridge images in Indiana. However, its effect on other colors or in particular environmental conditions, such as non-uniform illumination, has not yet been explored. A defect recognition method for steel bridge surface was proposed by using Fourier Transform and color image processing method in order to adapt to various background colors and overcome the influences from particular environmental conditions, such as non-uniform illumination. Fourier Transform in this system is aimed to determine the existence of defects in an acquired image. The proposed method shows better performance than previous methods such as rust defect recognition method (RUDR) proposed by Lee in 2005.

Wide color-gamut improvement of LCM using multi-phosphor white LED and modified rich color method

This paper presents an improved wide color-gamut technique for liquid crystal modules (LCM) with multiphosphor white (MPW) light emitting diodes (LED) and an modified rich color image processing method. The MPW LED improves 13% of the display color gamut in comparison with standard white LED under the assumption of equal power consumption and brightness. Via many simulation results, the pixel-level rich color method is shown to obtain a superior image data by giving about 45% saturation enhancement without hue distortion. The performance and merit of the proposed technique are exemplified by conducting several experimental results on a 2.8-inch thin-film-transistor (TFT) LCM.

木質内装の印象に及ぼす色彩の影響―アクセントカラーを考慮した画像解析法の適用について―

Various materials are used for the interior space, and therefore various colors of them appear there. To design visually comfortable living space, an examination about the relations between the colors of the interior materials such as wood and the influence of them on our visual impression is very important. In this study colorimetric features of various interior images are evaluated numerically and the relation between those values and our visual impression of “warm” is discussed. Thirty seven of interior images were prepared as specimens, and questionnaires regarding visual warmth of the specimens were performed to 39 subjects in Experiment A, and to 30 subjects in Experiment B, respectively. The specimen set for Experiment A was composed of more colorful interior images than for Experiment B. To calculate the colorimetric features of the interior images, a digital color image processing method according to sRGB system was adopted. The method made possible the transformation from RGB data to L*a*b* values of LAB color system in every one pixel. By the comparison of the result of both experiments, the effective hue index that was based on a modified hue angle weighed by a metric chroma value was devised. The index could express visual warmth of the interior images which accent colors, vivid red, blue, and so on, existed in well.

Regions adjacency graph applied to color image segmentation

The aim of this paper is to present different algorithms, based on a combination of two structures of graph and of two color image processing methods, in order to segment color images. The structures used in this study are the region adjacency graph and the line graph associated.We will see how these structures can enhance segmentation processes such as region growing or watershed transformation. The principal advantage of these structures is that they give more weight to adjacency relationships between regions than usual methods. Let us note nevertheless that this advantage leads in return to adjust more parameters than other methods to best refine the result of the segmentation.We will show that this adjustment is necessarily image dependent and observer dependent.

9732757 Color image processing methods for driving situation understanding Toshio Ito, Kenichi Yamada (Daihatsu Motor Co., Ltd.)

9732757 Color image processing methods for driving situation understanding Toshio Ito, Kenichi Yamada (Daihatsu Motor Co., Ltd.)