Color Image analysis by vector subspace method

A recent trend in color image processing combines the quaternion algebra with dictionary learning methods. This paper aims to present a generalization of the quaternion dictionary learning method by using the octonion algebra. The octonion algebra combined with dictionary learning methods is well suited for representation of multispectral images with up to 7 color channels. Opposed to the classical dictionary learning techniques that treat multispectral images by concatenating spectral bands into a large monochrome image, we treat all the spectral bands simultaneously. Our approach leads to better preservation of color fidelity in true and false color images of the reconstructed multispectral image. To show the potential of the octonion based model, experiments are conducted for image reconstruction and denoising of color images as well as of extensively used Landsat 7 images.

High-throughput screening in histology and analysis need a necessary automatic cell or nucleus counting. Current methods and systems based on grayscale or color images give results with counting errors. We suggest to use multispectral imaging (with more than three bands) rather than color one for nucleus counting. A traditional acquisition chains uses a source of white light and a CCD camera in addition to the optical microscope. To pass to a multispectral acquisition, we use a Programmable Light Source (PLS) in the place of the white light source. This PLS is capable of generating different wavelengths in the visible spectrum. So, one color image and four multispectral images have been acquired from histological slices. The four multispectral images contain respectively 3 bands, 5 bands, 7 bands and 10 bands. To make a proper comparison of data, several considerations have been taken, like camera linearity, intensity difference between the wavebands from the PLS and non uniformity of the light intensity range in the images. So, a set of measures were done for calibrating the system. An automatic detection method based on segmentation by expectation-maximization and ellipse fitting is used. An extension of this method is proposed in order to be applied to multispectral images. The original and the extended method are then applied to the data previously acquired to have first results regarding the effect of using multispectral images rather than color ones.

The practical limitations on the performance of a conventional MacNeille prism polarizer can be overcome if a liquid is used as the prism material. In particular it is possible to construct large aperture polarizers suitable for use with high power lasers in the UV, visible and near IR spectral regions. The width of the effective spectral region and the polarizing efficiency of the polarizer can be controlled almost at will. The losses in the transmitted beam can be low. The values of the optical thicknesses of the layers are not very critical and the cost of the device is low compared to that of any other polarizer having the same aperture and performance. Results will be given of numerical calculations and of experimental measurements.

segmentation divides an image into several constituent components such as color, structure, shape, and texture. It forms a major research topic for many image processing researchers as the applications are endless. Its applications include image enhancement, object detection, image retrieval, image compression, and medical image processing to name a few. The segmentation of color images is necessary for efficient pattern recognition and feature extraction involving various color spaces such as RGB, HSV and CIE L*A*B*, etc. This paper describes the different cluster based segmentation techniques used for segmenting the different color images and the resultant is analyzed with subjective and objective measures. Initially, registered color images are considered as input. Then the cluster based segmentation techniques namely K-Means clustering, Pillar-Kmeans clustering and Fuzzy C-means (FCM) clustering techniques are applied. Further, the segmented image is analyzed with measures such as compactness and execution time. From the experimental results, it has been observed that K-means and Pillar-Kmeans are the most suitable techniques for RGB, HSV and LAB color spaces than the FCM technique.

Converting near-infrared (NIR) images into color images is a challenging task due to the different characteristics of visible and NIR images. Most methods of generating color images directly from a single NIR image are limited by the scene and object categories. In this paper, we propose a novel approach to recovering object colors from multi-spectral NIR images using gray information. The multi-spectral NIR images are obtained by a 2-CCD NIR/RGB camera with narrow NIR bandpass filters of different wavelengths. The proposed approach is based on multi-spectral NIR images to estimate a conversion matrix for NIR to RGB conversion. In addition to the multi-spectral NIR images, a corresponding gray image is used as a complementary channel to estimate the conversion matrix for NIR to RGB color conversion. The conversion matrix is obtained from the ColorChecker's 24 color blocks using polynomial regression and applied to real-world scene NIR images for color recovery. The proposed approach has been evaluated by a large number of real-world scene images, and the results show that the proposed approach is simple yet effective for recovering color of objects.

Rapid and reliable inspection of food is essential to ensure food safety, particularly in mass production and processing environments. Many studies have focused on spectral imaging for poultry inspection; however, no research has explored the use of multispectral fluorescence imaging (MFI) for on-line poultry inspection. In this study, the feasibility of MFI for on-line detection of fecal matter from the ceca, colon, duodenum, and small intestine of poultry carcasses was investigated for the first time. A multispectral line-scan fluorescence imaging system was integrated with a commercial poultry conveying system, and the images of chicken carcasses with fecal contaminants were scanned at processing line speeds of one, three, and five birds per second. To develop an optimal detection and classification algorithm to distinguish upper and lower feces-contaminated parts from skin, the principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were first performed using the spectral data of the selected regions, and then applied in spatial domain to visualize the feces-contaminated area based on binary images. Our results demonstrated that for the spectral data analysis, both the PCA and PLS-DA can distinguish the high and low feces-contaminated area from normal skin; however, the PCA analysis based on selected band ratio images (F630 nm/F600 nm) exhibited better visualization and discrimination of feces-contaminated area, compared with the PLS-DA-based developed chemical images. A color image analysis using histogram equalization, sharpening, median filter, and threshold value (1) demonstrated 78% accuracy. Thus, the MFI system can be developed utilizing the two band ratios for on-line implementation for the effective detection of fecal contamination on chicken carcasses.

Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support

Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test (“Ishihara Color Blindness Test”) and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF < SM < SM-JHM < LUT < JHM < HM. It is anticipated that this work will provide a benchmark for NV colorization and for quantitative evaluation using an objective metric such as objective evaluation index (OEI).

This paper presents a two-dimensional discrete Fourier transform (DFT), applicable to colour images (and multispectral images generally). It is based on quaternion numbers, which may be thought of as a generalisation of the complex numbers with one real part and three imaginary parts. The transform is referred to the discrete quaternion Fourier transform (DQFT). The DQFT is a useful generalisation of the two-dimensional complex DFT to the case of images with more than two (but not more than four) image components. Natural colour images are a significant example of such images and their three components, for example, red, green and blue, may now be transformed as a whole. There are some important further theoretical steps needed, however, to make generalisation of monochrome frequency domain techniques in image processing widely applicable to colour and other multispectral images.

Approximation of the Karhunen–Loève transformation and its application to colour images

ABSTRACTThe quaternion Fourier transform has been widely employed in the colour image processing. The use of quaternions allow the analysis of colour images as vector fields. In this paper, the right-sided quaternion Fourier transform and its properties are reviewed. Using the polar form of quaternions, two novel uncertainty principles associated with covariance are established. They prescribe the lower bounds with covariances on the products of the effective widths of quaternionic signals in the space and frequency domains. The results generalize the Heisenberg's uncertainty principle to the 2D quaternionic space.

Based on experimental data, obtained in vitro from reflectance measurements and in vivo from digital analysis of color images of human irises, melanin content in human and bovine eye irises has been estimated. Reflectance measurements have been performed using commercially available optical multichannel spectrometer LESA-5 (BioSpec, Russia). For registration of color images digital camera Olympus C-5060 has been used. Analysis of the reflectance spectra has been performed by the method used for determination of melanin content in skin. For digital analysis of iris color images, decomposition of the images in RGB-color-coordinate system has been performed. The images have been obtained both from irises of health volunteers as from irises of patients with glaucoma. Original computer program based on Mathcad software has been developed for the analysis. The results obtained from spectral and color measurements have a good agreement each to other. In eye irises of patients with glaucoma, smaller melanin content has been obtained, and the result has been useful for development of novel and optimization of already existing methods of glaucoma diagnostics.

Fracta1 techniques are widely explored to quantity and recognize texture patterns in digital images. Among the different types of fractal techniques, one that stands out is the Higuchi fractal dimension. The Higuchi fractal dimension allows determining how much space is filled in a two-dimensional image through the projection of the image in 1D signals. This property provides the possibility to calculate the Higuchi fractal dimension of specific regions of an image. The main drawback of this technique is that it does not allow the analysis of color images. In this work, a new Higuchi fractal dimension model is presented with the inclusion of multidimensional and multiscale strategies to expand the traditional Higuchi dimension for texture analysis in color images. The multidimensional approach was applied considering each pixel of the color image as an n-dimensional vector. The multiscale strategy was defined using different scales of observation. The proposed model was applied to a set of 151 colorectal histological images to test its ability to quantity and separate the benign and malignant groups from colorectal cancer. The performance of the proposed model was compared with that provided by consolidated fractal dimension techniques. The results obtained are promising and indicate that the proposal contributes significantly to the literature focused on the quantification and recognition of texture patterns with fractal techniques.

Visual assessment, black & white image analysis and colour image analysis of the severity of powdery mildew (Sphaerotheca fuliginea) infection on leaves of cucumber (Cucumis sativus) were compared. The extent of infection with powdery mildew was expressed as the number of spores per cm2. Statistical treatment of the data showed colour image analysis to be superior to the other two methods of evaluation. Major veins, light reflections and variations in the colour of leaves can degrade the accuracy of colour image analysis somewhat. These problems are, however, surmountable. Colour image analysis qualifies as a valuable new tool for accurate, objective, reproducible and detailed quantitative assessment of disease on leaves of plants.

Focus on lasers and imaging