Pixel-Based Image Processing for CIE Standard Sky Classification through ANN

Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification problem.

Color space influence on ANN skin lesion classification using statistics texture feature

This study aims to investigate the ability of an artificial neural network to differentiate between malign and benign skin lesions based on two statistics terms and for RGB (R red, G green, B blue) and YIQ (Y luminance, and I and Q chromatic differences) color spaces. The targeted statistics texture features are skewness (S) and kurtosis (K) which are extracted from the histograms of each color channel corresponding to the color spaces and for the two classes of lesions: nevi and melanomas. The extracted data is used to train the Feed-Forward Back Propagation Networks (FFBPNs). The number of neurons in the hidden layer varies: it can be 8, 16, 24, or 32. The results indicate skewness features computed for the red channel in the RGB color space as the best choice to reach the goal of our study. The reported result shows the advantages of monochrome channels representation for skin lesions diagnosis.

Hue-Preserving Saturation Improvement in RGB Color Cube

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement.

RESEARCH OF IMAGES FILTRATION METHODS IN COMPUTER SYSTEMS

It is known that human eyes are less sensitive to color, than to their brightness. In the RGB color space, all three components are considered equally important, and they are usually stored with the same resolution. However, you can display a color image more efficiently, separating the brightness from color information and presenting it with a higher resolution than color. RGB space is well suited for computer graphics, because it uses these three components for color formation. However, RGB space is not very effective when it comes to real images. The fact is that to save the color of an image, you need to know and store all three components of the RGB, and if one of them is missing, it will greatly distort the visual image representation. Also, when processing images in RGB space, it is not always convenient to perform any pixel conversion, because, in this case, it will be necessary to list all three values of the RGB component and write back. This greatly reduces the performance of various image processing algorithms. For these and other reasons, many video standards use brightness and two signals that carry information about the red and blue components of the signal, as a color model other than RGB. The most famous among such spaces is YCbCr.

Differentiation of peach cultivars by image analysis based on the skin, flesh, stone and seed textures

The peaches belonging to different cultivars can be characterized by differentiation in properties. The aim of this study was to evaluate the usefulness of individual parts of fruit (skin, flesh, stone and seed) for cultivar discrimination of peaches based on textures determined using image analysis. Discriminant analysis was performed using the classifiers of Bayes net, logistic, SMO, multi-class classifier and random forest based on a set of combined textures selected from all color channels R, G, B, L, a, b, X, Y, Z and for textures selected separately for RGB, Lab and XYZ color spaces. In the case of sets of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z), the accuracy of 100% was observed for flesh, stones and seeds for selected classifiers. The sets of textures selected from RGB color space produced the correctness equal to 100% in the case of flesh and seeds of peaches. In the case of Lab and XYZ color spaces, slightly lower accuracies than for RGB color space were obtained and the accuracy reaching 100% was noted only for the discrimination of seeds of peaches. The research proved the usefulness of selected texture parameters of fruit flesh, stones and seeds for successful discrimination of peach cultivars with an accuracy of 100%. The distinguishing between cultivars may be important for breeders, consumers and the peach industry for ensuring adequate processing conditions and equipment parameters. The cultivar identification of fruit by human may be characterized by large errors. The molecular or chemical methods may require special equipment or be time-consuming. The image analysis may ensure objective, rapid and relatively inexpensive procedure and high accuracy for peach cultivar discrimination.

Chroma Enhancement in CIELAB Color Space Using a Lookup Table

In this study, we present a method of chroma enhancement in the CIELAB color space and compare it with that in the RGB color space. Color image enhancement using the CIELAB color space has the disadvantage that the color gamut problem occurs because the conversion to the RGB color space is necessary to display the image. However, since the CIELAB color space is based on human visual perception, the quality of the resulting images is expected to be higher than that of the RGB color space. In the method using the CIELAB color space, we introduce a lookup table to reduce the calculation costs. Experiments comparing image enhancement results obtained from two color spaces are performed using several digital images.

Visibility Restoration Using Generalized Haze-Lines

Haze reduces the perceived scene radiance and limits the visibility in outdoor images. The visibility is different for each scene point and is proportional to haze thickness, and distance from the camera. Transmission map represents percentage of scene radiance captured by the camera and is unknown for every pixel. This work generalizes the concept of haze-lines, and presents an algorithm to estimate transmission map and restore scene radiance accurately. The proposed technique depends on the perception that the colors of haze-free natural images can be well approximated by a set of distinct colors and their shades (natural color-palette) that can be learned beforehand. In presence of haze, the pixels forming a cluster in haze-free image, make a line (haze-line) in RGB color space. The two endpoints of this haze-line are the haze-free color and the airlight. We propose that these haze-lines can be generalized, with one end as learned color-palette of natural images and the other as airlight. Hence the scene radiance end can be made independent of underlying image. The algorithm recovers the transmission map, by determining membership of each pixel to a given haze-line and finding how far-off it is from its learned color-palette. The algorithm is linear to the size of image, and requires just a collection of haze-free natural images for training. The results obtained on a diverse range of images demonstrate the efficiency of proposed algorithm.

A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition

In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red–green–blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.