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Biomedical image is generally noisy and little blur due to the physical mechanisms of the acquisition process, so one of the common degradations in biomedical image is their noise and poor contrast. The idea of biomedical image enhancement is to improve the quality of the image for early diagnosis. In this paper we are using Wavelet Transformation to remove the Gaussian noise from biomedical images: Positron Emission Tomography (PET) image and Radiography (Radio) image, in different color spaces (RGB, HSV, YCbCr), and we perform the fusion of the denoised images resulting from the above denoising techniques using add image method. Then some quantive performance metrics such as signal -to -noise ratio (SNR), peak signal-to-noise ratio (PSNR), and Mean Square Error (MSE), etc. are computed. Since this statistical measurement helps in the assessment of fidelity and image quality. The results showed that our approach can be applied of Image types of color spaces for biomedical images.

This research reports two methods which used in controlling hexacopter, namely, avoiding objects with three colors of pattern and determining the shortest path. The obstacle object with three colors of pattern that has a dimension of 40 cm × 60 cm and placed on a pillar with a position of 1.5 meter above the floor. The obstacle that had been captured was saved in jpg format then registered on RGB-based image processing program. The image will be classified into 8 binary based on the intensity of color and projected against the floor. If the object was detected by a hexacopter camera when flown and matched with the objects that has registered on the program, the hexacopter will avoid the object. The position of the object that has projected was used for the program to determine the shortest path. The shortest path determination is represented on a 20 × 20 square-shaped floor box format, which the box has 32 × 24 pixel dimension. This process is using A (x, y) = B (x, y) + C (x, y) equation. B(x, y) stated the distance where the hexacopter moves to the starting point and the value of C stated the distance from the hexacopter position to the final point. The value of A (x, y) stated A sum of the two and look for the smallest value of the order to surrounding squares. The result of this research stated that the hexacopter were able to fly through the shortest way in order to avoid the objects with a success rate of about 70 % in a guided fly mode (waiting for a command from the computer).

This research was conducted to analyze the spray combustion ratio of Kapuk Seed Oil (KSO) and Jatropha Oil (JO), with combustion length, combustion width, RGB color and combustion stage as the parameters using video camera with 420 fps speed in a spray and nozzle tester with a pressure of 2100 psi. The results of the comparative analysis showed that the spray combustion of KSO was better than that of JO. Specifically, it found that: 1) there was a 23.58% difference in Maximum Flame Spray Length (MFSL) between KSO and JO, where the MFSL of KSO and Jo was 1780.8 mm and 1360.8 mm, respectively; 2) there was 30.94% difference in the Maximum Flame Spray Width (MFSW) between KSO and JO, where the MFSW of KSO and Jo was 616 mm and 425 mm, respectively; and 3) the analysis of combustion stage showed that there is one stage in kapok seed oil combustion and four stages in jatropha oil combustion. Furthermore, the RGB values of KSO and JO as demonstrated in four analyzed graphs showed KSO has lower value than JO, which means the color of KSO Spray Combustion is thicker or more visible than the color of JO Spray Combustion.

Ripeness is one of important factors for quality sorting of harvested oil palm fresh fruit bunches (FFB). Traditional ripeness classifications using FFB color and number of fruit loose for harvesting have some disadvantages especially for high oil palm trees. A laser based imaging system is proposed to substitute the traditional method. In this study, ripeness detection simulation of oil palm FFBs was performed. The system composed of two diode lasers with 532 nm and 680 nm in wavelengths and a CMOS camera which was set on a rotating plate for easy adjustment of laser beam hitting FFB. The FFB samples were placed on an aluminum platform with 4 height variations, 1.5 m, 2 m, 2.5 m, and 3 m. The relations of reflectance intensities represented by Red Green Blue (RGB) values of the FFB images to the height variations and ripeness levels of FFBs with and without laser beam were analyzed. The samples were from Tenera variety with 4 ripeness levels called F0, F1, F3, and F4. The results showed that the red comp...

Object localization is the first task in pattern detection and recognition. This task is very important due to it reduces the searching time to the interest object. In this paper we introduce our novel method of object localization based on color feature. Our novel method is a histogram of s-RGB. This histogram is used in the training phase to determine the color dominant in the initial Region of Interest (ROI). Then this information is used to label the interest object. To reduce noise and localize the interest object, we apply the row and column density function of pixels. The comparison result with some processes, our system gives a best result and takes a short computation time of 26.56 ms, in the video rate of 15 frames per second (fps).

In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

In the following article we present some of our results from observations of two total solar eclipses (TSE). By combining appropriate photographic equipment and post-processing techniques we show that numerous solar phenomena can be captured in details during TSE. We use color slide RGB photometry technique to visualize invisible regions of the solar corona and to highlight some of the solar phenomena that are very difficult for observation by Earth-based observatories. In fact it reveals more details of the far solar corona than any original image taken from ground-based observations. RGB photometry visualizes different components of the solar corona in one image, which is impossible using conventional observations. This makes it valuable tool for studies of the solar corona. Here we first observe peculiar near infrared emission regions around the upper part of the solar limb during the 1999 TSE. So far its origin is unknown and they need further studies including observations during other solar eclipses...

This work presents a color image pattern recognition system invariant to rotation, scale and translation. The system works with three 1D signatures, one for each RGB color channel. The signatures are constructed based on Fourier transform, analytic Fourier-Mellin transform and Hilbert binary rings mask. According with the statistical theory of box-plots, the pattern recognition system has a confidence level at least of 95.4%.

Malaria is a disease in human caused by Plasmodium parasite. Misdiagnosis and improperness in medical treatment towards this disease can lead to the death of patients. A number of digital image analysis-based research works to minimize the misdiagnosis has been developed. This research aims to segment the form of parasitic cells of Plasmodium using the combination of the red (R) channel in the RGB color space and saturation (S) channel in the HSV color space. The images used were parasitic cells of Plasmodium vivax, consisting of the phases of trophozoite, schizonts, and gametocytes. Each of which comprises ten samples. The process was begun by improving the image quality continued by separating the red channel in the RGB color space and by converting the image of RGB into the HSV image to obtain the saturation (S) channel. These two color channels were then combined in the form of a grayscale image. The further process was conducted by changing those grayscale image into the binary image using the Otsu method and the morphological operation to form it as the masking image. Of 30 tested images, the segmentation of the red (R) channel and saturation (S) channel combination achieved the accuracy of 93.33%, while the segmentation using saturation (S) channel produces result in the accuracy of 66.67%.

In the color image segmentation, clustering based methods are one of ideal method to use. In this paper, we design a segmentation system of multi-view and multi-class fruit images by incorporating depth data obtained from Kinect, along with the RGB value, using k-means clustering. It was well-known that k-means clustering is one of clustering techniques which is widely used and studied. In addition, many researchers have shown many advantages using depth images from the RGBD sensor. For the evaluation, silhouette coefficient is used to evaluate the quality of clustering result and mean-squared-error is used to evaluate the segmented image. In conclusion, from our experiment, we show that our approach to incorporate depth data into fruit segmentation can reduce the Mean Squared Error (MSE).

Sala mango (Mangifera indicia) is one of the Malaysia’s most popular tropical fruits that are widely marketed within the country. The degrees of ripeness of mangoes have conventionally been evaluated manually on the basis of color parameters, but a simple non-destructive technique using the Samsung Galaxy Note 1 mobile phone camera is introduced to replace the destructive technique. In this research, color parameters in terms of RGB values acquired using the ENVI software system were linked to detect Sala mango quality parameters. The features of mango were extracted from the acquired images and then used to classify of fruit skin color, which relates to the stages of ripening. A multivariate analysis method, multiple linear regression, was employed with the purpose of using RGB color parameters to estimate the pH, soluble solids content (SSC), and firmness. The relationship between these qualities parameters of Sala mango and its mean pixel values in the RGB system is analyzed. Findings show that pH yields the highest accuracy with a correlation coefficient R = 0.913 and root mean square of error RMSE = 0.166 pH. Meanwhile, firmness has R = 0.875 and RMSE = 1.392 kgf, whereas soluble solid content has the lowest accuracy with R = 0.814 and RMSE = 1.218°Brix with the correlation between color parameters. Therefore, this non-invasive method can be used to determine the quality attributes of mangoes.

Colour image segmentation has becoming more popular for computer vision due to its important process in most medical analysis tasks. This paper proposes comparison between different colour components of RGB(red, green, blue) and HSI (hue, saturation, intensity) colour models that will be used in order to segment the acute leukemia images. First, partial contrast stretching is applied on leukemia images to increase the visual aspect of the blast cells. Then, an unsupervised moving k-means clustering algorithm is applied on the various colour components of RGB and HSI colour models for the purpose of segmentation of blast cells from the red blood cells and background regions in leukemia image. Different colour components of RGB and HSI colour models have been analyzed in order to identify the colour component that can give the good segmentation performance. The segmented images are then processed using median filter and region growing technique to reduce noise and smooth the images. The results show that segmentation using saturation component of HSI colour model has proven to be the best in segmenting nucleus of the blast cells in acute leukemia image as compared to the other colour components of RGB and HSI colour models.

The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

This paper explores the adaptation of the two-colour principle to develop a high-speed colour temperature correlation system, which is able to cover a range of temperature that is challenging to achieve before. A colour digital camera has built in RGB filters. It is possible to measure the temperature from the ratio of intensity of the green and red pixels using the two-colour principle based on the expansion of the Plank’s radiation law. In this study, experiments were carried out using a temperature calibrated tungsten ribbon lamp which can be tuned to vary from 1300 to 2200°C. Using very high shutter speed and small aperture, the high-speed camera successfully captured the tungsten ribbon without image saturation at the full temperature scale. Tests have been carried out at different temperature and camera settings. The sensitivity and errors have been analysed, and experiment results demonstrate the potential of using very high shutter speed is available for measuring the temperature even beyond 2200°C.

This work presents a pattern recognition digital system based on nonlinear correlations. The correlation peak values given by the system were analyzed by the peak-to-correlation energy (PCE) metric to determine the optimal value of the non-linear coefficient kin the k-law. The system was tested with 18 different color images of butterflies; each image was rotated from 0° to 180° with increments of 1° and scaled ±25% with increments of 1% and to take advantage of the color property of the images the RGB model was employed. The boxplot statistical analysis of the mean with ±2*EE (standard errors) for the PCE values set that the system invariant to rotation and scale has a confidence level at least of 95.4%.

Indirect Immunofluorescence (IIF) is a good standard used for antinuclear autoantibody (ANA) test using Hep-2 cells to determine specific diseases. Different classifier algorithm methods have been proposed in previous works however, there still no valid set as a standard to classify the fluorescence intensity. This paper presents the use of fuzzy logic to classify the fluorescence intensity and to determine the positivity of the Hep-2 cell serum samples. The fuzzy algorithm involves the image pre-processing by filtering the noises and smoothen the image, converting the red, green and blue (RGB) color space of images to luminosity layer, chromaticity layer "a" and "b" (LAB) color space where the mean value of the lightness and chromaticity layer "a" was extracted and classified by using fuzzy logic algorithm based on the standard score ranges of antinuclear autoantibody (ANA) fluorescence intensity. Using 100 data sets of positive and intermediate fluorescence intensity for testing the performance measurements, the fuzzy logic obtained an accuracy of intermediate and positive class as 85% and 87% respectively.

In term of value and volume of production, mango is the third most important fruit product next to pineapple and banana. Accurate size assessment of mango fruits during harvesting is vital to ensure that they are classified to the grade accordingly. However, the current practice in mango industry is grading the mango fruit manually using human graders. This method is inconsistent, inefficient and labor intensive. In this project, a new method of automated mango size and grade assessment is developed using RGB fiber optic sensor and fuzzy logic approach. The calculation of maximum, minimum and mean values based on RGB fiber optic sensor and the decision making development using minimum entropy formulation to analyse the data and make the classification for the mango fruit. This proposed method is capable to differentiate three different grades of mango fruit automatically with 77.78% of overall accuracy compared to human graders sorting. This method was found to be helpful for the application in the current agricultural industry.

In this papera methodology for classifying skin cancerin images of dermatologie spots based on spectral analysis using the K-law Fourier non-lineartechnique is presented. The image is segmented and binarized to build the function that contains the interest area. The image is divided into their respective RGB channels to obtain the spectral properties of each channel. The green channel contains more information and therefore this channel is always chosen. This information is point to point multiplied by a binary mask and to this result a Fourier transform is applied written in nonlinear form. If the real part of this spectrum is positive, the spectral density takeunit values, otherwise are zero. Finally the ratio of the sum of the unit values of the spectral density with the sum of values of the binary mask are calculated. This ratio is called spectral index. When the value calculated is in the spectral index range three types of cancer can be detected. Values found out of this range are benign injure.

We propose a new lossless digital image encryption scheme based on the permutation and substitution architecture. Initially, original image is divided into squared sub‐images and then three layers of pixels corresponding to additive primary colours (RGB) of each sub‐image are separated. Each layer of pixels of squared sub‐images are scrambled by three different ways in the permutation process whereas a simple arithmetic, mainly sorting and differencing, is performed on each layer of pixels to achieve the substitution. The results of several experiments show that the proposed image cipher provides an efficient way for image encryption with high decryption rate.

Polymer dispersed liquid crystal (PDLC) are a novel class of optical composite materials. They offer varied range of applications in optical devices as device materials. The polymer stabilized liquid crystals (PSLC), have improved upon some of the device characteristics. The presence of a polymer network formed at low polymer concentrations provides similar advantages in enhancing the stability of the structure, aiding the re‐orientation of liquid crystal director to the desired stable configuration, reducing the switching time, and improving the optical contrast in the devices. Electro‐optic switching behavior with variation in voltage and temperature is studied for application in switchable display devices. The color response (RGB) of textures is calibrated against temperature by suitable polynomial fitting. The variation in R, G and B intensities of PSLC film with temperature is used to determine hue, saturation and intensity corresponds to HSI model. We shall present interesting results on these materials and discuss how performance of devices can be improved by tailoring some functional groups in polymer networks to control some physical parameters.

In this paper, a novel composite RGB fringe pattern projection and processing method is presented to obtain the absolute phase information of objects having surface discontinuities and/or spatially isolated surfaces. Multiple sinusoidal fringe patterns and binary fringe patterns are coded into the red, green and blue channels of composite RGB fringe pattern images. The generated composite images are projected onto object surfaces by a DLP(Digital Light Processing) projector and the deformed fringe patterns captured by a 3‐chip color CCD camera from a different viewpoint. The sinusoidal and binary fringe patterns are extracted from the acquired composite fringe pattern images. A multiple‐step phase shift algorithm calculates the wrapped phase information from the obtained sinusoidal fringe patterns, so the phase has a high resolution and dynamic range. While the binary fringe patterns determine the absolute fringe order of the wrapped phase map. Therefore, the composite RGB fringe pattern projection techni...

The objective of the paper is to determine the coordinates of the dominant colors in complex digital images. The key issue is the selection of an appropriate color space and optimal quantization of the selected color space. Five color spaces, RGB, LCh, Lab, I1I2I3 and HSI, and several quantization levels are involved in evaluating the performance of the propose method. The method is based on the local detection of the maxima of the color histogram. It is analyzed how the quantization and the size used in the localization of the maxima affect to the number of dominant colors obtained and their color coordinates. First, a group of twenty eight images has designed itself test. The objective of using images test is to be able to compare the coordinates of the dominant colors that the method provides with the expected values. This allows evaluating what color space provides smaller mean square error in the calculation of the color coordinates of the dominant colors, as well as their area. They have been design...

We propose a color image encryption by using digital joint transform correlator (JTC) architecture and wavelength multiplexing. In this arrangement, the color image to be encrypted is separated into three channels: red, green and blue. One JTC aperture has the information corresponding to a determined color channel embedded in a phase mask (object aperture), and the other JTC aperture contains the encrypting phase mask. The joint power spectrum reflects a behavior according to the double‐aperture content and the illumination wavelength. The wavelength variation will produce a corresponding power spectrum change. The basic concept within this approach is that the speckle size is wavelength‐dependent. Then, the wavelength change can be used to store the encrypted information associated to each color channel in the same media. We achieve the color storing by changing the illumination wavelength, while varying the color image content in the object aperture of the JTC architecture. The multiplexed power spectr...

In the present paper a digital algorithm was developed to make phase encryption of digital indexed images to color using the fractional Fourier transform (the images in RGB are converted to indexed before to encrypt). The indexed images are represented by a matrix of M×N pixels (where M defines the height and N is the Width of the image) and a color map (it's a matrix of C×3 elements, where C indicates the colors number of the image and the number 3 indicates the three columns associated with the color components: Red, Green and Blue of each pixel of the matrix of M×N) associated to the matrix of pixels to suitably represent the color information of the image. The indexed image (matrix of M×N pixels) to encrypt is placed as the phase of a complex exponential, then is transformed three times and multiplied in intermediate steps by two random phase masks statistically independent thus to obtain the encrypted image, for decrypt the coding image the encryption procedure is applied in the inverse sense to the ...