Color Features Of Image Research Articles

Detection method of sports scene conversion for MPEG compressed video based on fuzzy logic

At present, the majority of sports games video adopts MPEG image technology, and MPEG video compression is the current more mainstream approach. After compression, the quality of the video will decline, and other practical problems. However, the existing detection methods of sports video scene conversion, when dealing with MPEG compressed video, are not ideal, often appear the phenomenon of missing detection and wrong detection. In order to solve this problem, this paper proposes a detection method of sports scene conversion on MPEG compressed video based on fuzzy logic. Introducing fuzzy logic into the detection method of video scene conversion is the highlight of this method. Firstly, this paper preprocessed the video image according to the Convention. In this paper, the recognition of image features and specific extraction methods are introduced in detail, and the extraction algorithm of image color features is further optimized. For the design of the detection method, the main innovation is to fully combine the fuzzy logic and macroblock information. In the existing detection methods, different detection schemes are given for the abrupt change of video scene and the gradual change of scene. Finally, in order to verify the actual effect of the detection method in this paper, an experimental analysis based on the keyframe complexity detection method is established. After a number of experiments including the experimental results of scene transition, analysis, and processing time, through the analysis of data, a step-by-step proof of this method has good accuracy and recall.

Simple Yet Effective Way for Improving the Performance of Depth Map Super-Resolution

In depth map super-resolution (SR), a high-resolution color image plays an important role as guidance for preventing blurry depth boundaries. However, excessive/deficient use of the color image features often causes performance degradation such as texture-copying/edge-smoothing in flat/boundary areas. To alleviate these problems, this letter presents a simple yet effective method for enhancing the performance of the SR without requiring significant modifications to the original SR network. To this end, we present a self-selective concatenation (SSC), which is a substitute for the conventional feature concatenation. In the upsampling layers of the SR network, the SSC extracts spatial and channel attention from both color and depth features such that color features can be selectively used for depth SR. Specifically, the SSC learns to use sufficient color features for rendering sharp depth boundaries, whereas their effects are reduced in smooth regions to prevent texture-copying. The proposed SSC can be included in any existing SR networks that have the encoder-decoder structure. The experimental results show that the proposed method can further improve the performances of existing SR networks in terms of the root mean squared error and peak signal-to-noise ratio.

Suggested Method to Create Color Image Features Victor

Suggested Method to Create Color Image Features Victor

Digital Color Image Classification Based on Modified Local Binary Pattern Using Neural Network

Classification of digital color images is one of the preeminent crucial processes in the image processing field since it increases the efficiency of the digital image recognition system. This paper presents a highly accurate and effective technique to extract color image features, which can be passed as an input data set of artificial neural network for classification purposes. The proposed method will be tested utilizing color images database. Then, the efficiency parameters will be determined in order to demonstrate the exactness and proficiency of the proposed technique. A modified local binary pattern method of extracting color image features, which can be used later as a signature to recognize color image is proposed and discussed. The proposed method is employed to create a feature array for each color image. The suggested classification technique using artificial neural network chops down the memory address space needed to store the necessary data required for the recognition system. Hence, the recognition time will be reduced.

Potential of a double lighting imaging system for characterization of 'Hayward' kiwifruit harvest indices

Abstract The timing of harvest date for ‘Hayward’ kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson. cv. ‘Hayward’) affects its storage life and eating quality. The common method for determination of maturity (Harvest Index) is the soluble solid content (SSC), with a minimum standard of 6.2 %. Measurement of SSC, using a hand-held refractometer, is simple and cheap, but it is destructive and laborious. In this study, we explore the potential for color and fluorescence image features to provide the basis for fast, non-destructive, and portable means of determining the Harvest Index. The most prominent fruit fluorescence excitations and emissions during fruit maturation were identified using excitation-emission matrices (Ex: 280–720 nm, Em: 300–750 nm) measured with Front Face Fluorescence Spectroscopy. The most sensitive excitation-emission wavelengths (Ex: 370 nm, Em: 400–600 nm) that correlated with SSCs were selected using partial least squares regression and were used to develop a double lighting imaging system which captures color and UV-fluorescence images. A SSC prediction model was established based on color and UV-fluorescence images. The results from this system had a root mean square error of validation and correlation coefficient relationship with standard SSCs of 0.39 and 0.94, respectively. This study demonstrated that the developed double lighting imaging system has the potential to non-destructively monitor kiwifruit maturation and determine optimum harvest by using the Harvest Index.

Image color style migration method for mobile applications based color feature extraction

With the development of the computer image processing and smartphones, image processing on smartphones will be a popular technology. This paper mainly provides an image color style migration method for mobile applications based color feature extraction. Firstly, we use dichotomy to extract color fe ature of the template image. Then we use Quartz2D engine of iOS to draw the target image according to the template image color feature. Finally we use Metal interface of iOS to adjust the target image’s color saturation and sharpen its edge. Through the whole process, the source image’s color style has been migrated to a new image. We have implemented the image color style migration system orient to iOS application. The experiment results show that the system runs stable and reliable.

Color-based template selection for detection of gastric abnormalities in video endoscopy

Computer-aided diagnosis of gastric diseases from endoscopy frames is an important task. It facilitates both the patient and gastroenterologist in terms of time, money and most important health. Colors are the basic visual features of endoscopic images and also provide clues about abnormal regions in endoscopy frames. A variety of color spaces available for representation of color frames. However, we are not certain about which color space is more suitable for representing color features of gastric images. This paper presents a comparison of color features in different color spaces for detection of abnormal areas in chromoendoscopy (CH) frames. In addition, the CH images are segmented by using an existing color-difference based segmentation method Delta E (ΔE). A framework for automatic segmentation is presented for endoscopy images by selecting a template image in ΔE by using trained models. For classification, colors features are also merged with texture descriptors. The support vector machine (SVM) classifier is trained on color features and also the hybrid color combined texture characteristics. Then the trained classifier is used to group CH frames into abnormal and normal classes. ΔE with manual template selection has achieved 57.44% accuracy and 56.88% accuracy with the automated process. Moreover, the suggested method achieves 86.6% accuracy and 0.91 area under the curve for the classification of gastric lesions.

Early Prediction of Soybean Traits through Color and Texture Features of Canopy RGB Imagery

Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cycles. If end-season crop traits such as yield can be predicted through early-season phenotypic measurements, crop selection can potentially be made before a full crop generation cycle finishes. This study explored the possibility of predicting soybean end-season traits through the color and texture features of early-season canopy images. Six thousand three hundred and eighty-three images were captured at V4/V5 growth stage over 6039 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix-based texture features were derived from each image. Another two variables were also introduced to account for location and timing differences between the images. Five regression and five classification techniques were explored. Best results were obtained using all 457 predictor variables, with Cubist as the regression technique and Random Forests as the classification technique. Yield (RMSE = 9.82, R2 = 0.68), Maturity (RMSE = 3.70, R2 = 0.76) and Seed Size (RMSE = 1.63, R2 = 0.53) were identified as potential soybean traits that might be early predictable.

Hybrid Tile Based Feature Extraction and Support Vector Machine Base Content-Based Image Retrieval System for Medical Application

Through the landing of therapeutic endoscopes, earth perception satellites and individual telephones, content-based picture recovery (CBIR) has concerned critical consideration, activated by its broad applications, e.g., medicinal picture investigation, removed detecting, and individual re-distinguishing proof. Be that as it may, developing successful component extraction is as yet reported as an invigorating issue.In this paper, to overcome the feature extraction problems a hybrid Tile Based Feature Extraction (TBFE) is introduced. The TBFE algorithm is hybrid with the local binary pattern (LBP) and Local derivative pattern (LDP). These hybrid TBFE feature extraction method helps to extract the color image features in automatic manner. Support vector machine (SVM) is used as a classifier in this image retrieval approach to retrieve the images from the database. The hybrid TBFE along with the SVM classifier image retrieval is named as IR-TBFE-SVM. Experiments show that IR-TBFE-SVMdelivers a higher correctness and recall rate than single feature employed retrieval systems, and ownsdecentweight balancing and query efficiency performance.

Low Orbiting Satellite and Small UAS-Based High-Resolution Imagery Data to Quantify Crop Lodging: A Case Study in Irrigated Spearmint

Conventional methods for crop lodging assessments need accurate ground observations and tend to be laborious. Lodging assessment methods and accuracy can thus be improved using remote sensing data from small unmanned aerial systems (UASs) and low orbiting satellites (LOSs). With such aim, imagery to assess spearmint crop lodging was acquired using a small UAS at two ground sample distances (GSDs) of 0.01 and 0.03 m. Crop surface model (CSM) and six image color features were extracted from small UAS-based data. These features were then classified into not lodged (NL), partially lodged (PL), and lodged (L) groups. Mean and majority feature classes were obtained for 50 regions of interest (ROI) of size 1 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> each. Features were compared with visual crop lodging ratings using Pearson correlation (r) and Cohen's kappa (CK) coefficients. CSM showed higher assessment accuracy with r ≈ 0.85 and CK > 0.60. Mean percentage red (%R) was observed to have the strongest correlation with visual ratings (r = 0.75 and CK = 0.40-0.59) followed by mean percentage blue (%B), both at 0.01-m GSD. The percentage of lodging calculated from %R and %B maps was also contrasted with similar estimates from LOS-based imagery at 3.00-m GSD with no statistical differences found at 5% level.

SVM-based image partitioning for vision recognition of AGV guide paths under complex illumination conditions

Abstract Applying computer vision to mobile robot navigation has been studied more than two decades. For the commercial off-the-shelf (COTS) automated guided vehicles (AGV) products, the cameras are still not widely used for the acquisition of guidance information from the environment. One of the most challenging problems for a vision guidance system of AGVs lies in the complex illumination conditions. Compared to the applications of computer vision where on-machine cameras are fixed in place, it is difficult to structure the illumination circumstance for an AGV that needs to travel through a large work space. In order to distinguish the original color features of path images from their illumination artifacts, an illumination-adaptive image partitioning approach is proposed based on the support vector machine (SVM) classifier with the slack constraint and the kernel function, which is utilized to divide a path image to low-, normal-, and high-illumination regions automatically. Moreover, an intelligent path recognition method is developed to carry out guide color enhancement and adaptive threshold segmentation in different regions. Experimental results show that the SVM-based classifier has the satisfactory generalization ability, and the illumination-adaptive path recognition approach has the high adaptability to the complex illumination conditions, when recognizing the path pixels in the field of view with both high-reflective and dark-shadow regions. The 98% average rate of path recognition will significantly facilitate the subsequent operation of path fitting for vision guidance of AGVs.

Target positioning method in binocular vision manipulator control based on improved canny operator

In order to improve the accuracy of binocular vision manipulator control, a target positioning method based on improved Canny operator is proposed. First, the image acquired by the left and right cameras is converted into an HSI color model, and the range of the target in the image is located by the image color feature as a region of interest (ROI). Then, the improved Canny edge detection algorithm is used to detect the target in the ROI image, extract the target contour, and determine the center position of the target. These processes are performed on the left and right images, respectively, to obtain the center of the target on the left and right images, and then the three-dimensional coordinates of the target with respect to the camera are determined by triangulation. The experimental results show that the method can accurately detect and locate the target position, and the positioning error is 2.4%. Therefore, it can provide accurate coordinates for the manipulator to perform related tasks.

Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine

In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment, a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine (LS-SVM) was proposed. Firstly, bilateral filter was used to filter the RGB channels image respectively to eliminate noise. Then the pixel-level color feature and texture feature of the image, which was used as input of LS-SVM model (classifier) and SVM model (classifier), were extracted via RGB value of image and gray level co-occurrence matrix. Finally, the color image was segmented with the trained LS-SVM model (classifier) and SVM model (classifier) separately. The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting, back-lighting and overshadow, with the accuracy of above 90%. When segmenting an image, the SVM algorithm required 1.3 s, while the LS-SVM algorithm proposed in this paper just needed 0.7 s, which was better than the SVM algorithm obviously. The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81% picking success rate. Keywords: bilateral filter, least squares support vector machine (LS-SVM), image segmentation, Hangzhou white chrysanthemum, illumination intensity DOI: 10.25165/j.ijabe.20191204.4584 Citation: Yang Q H, Luo S L, Chang C, Xun Y, Bao G J. Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine. Int J Agric & Biol Eng, 2019; 12(4): 127–134.

Improving the processing of machine vision images of robotic systems in the Arctic

The transformation of the development of the Arctic is due to modern robotic systems. The use of unmanned vehicles in many industries in the Arctic provides an array of photo and video information. Accuracy of image analysis and pattern recognition is enhanced by image preprocessing. However, the existing binarization algorithms are not universal for images with different distortions and loss of information. The accuracy of binarization algorithms depends on many factors, such as shadows, uneven lighting, low contrast, noise, etc. Images with different characteristics of light and noise are simulated in order to model various lighting conditions on information from digital cameras of robotic systems. The paper investigates global and adaptive image binarization algorithms. The binarized images were obtained using these algorithms and the results of binarized images recognition are compared by an optical character recognition system. An analysis of the comparison results showed that for images made in poor lighting conditions or images with low contrast, or images with high noise levels, adaptive binarization algorithms are better suited. However, in most cases it is not possible to obtain fully correctly recognized images.The paper proposes a new binarization method based on artificial neural networks. The process of creating an artificial neural network is shown, include the parameters for determining the class of a pixel, the adjustment of weights, the architecture of an artificial neural network. A comparison of the proposed artificial neural network with existing image binarization algorithms demonstrate that in most cases the artificial neural network has the result of image processing at the level of adaptive algorithms or higher. The proposed method of images binarization based on the image color characteristics analysis allows to solve image recognition tasks by robotized systems.

Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features

The existing image search system often faces difficulty to find a appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by that the users’ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper we present a new approach for content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something - supposed as an object. We applies segmentation of image as content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract color feature of image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)’s approach with 15 kernels. For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.

Analysis of color image features extraction using texture methods

A digital color images are the most important types of data currently being traded; they are used in many vital and important applications. Hence, the need for a small data representation of the image is an important issue. This paper will focus on analyzing different methods used to extract texture features for a color image. These features can be used as a primary key to identify and recognize the image. The proposed discrete wave equation DWE method of generating color image key will be presented, implemented and tested. This method showed that the percentage of reduction in the key size is 85% compared with other methods.

Research on cooperative classification of multimedia visual images based on deep machine learning model

Aiming at the low accuracy of multimedia visual image cooperative classification, a new method of multimedia visual image cooperative classification based on depth machine learning model is proposed. Firstly, HSV color space model is selected to extract color features of multimedia visual images, Gabor function is used to extract texture features, and shape invariant moments are used to extract shape features. Then the features of multimedia visual images are recognized and classified, and the model parameters are optimized and adjusted according to the deviation of training output. The experimental results show that the average accuracy of this method is 95.892%, and the classification efficiency is high. The classification accuracy of this method is basically above 95%, and the classification accuracy is high. The training time of image type samples is 19 s, the testing time of image type is 12 s, and the time consumption is low.

Marginal distribution covariance model in the multiple wavelet domain for texture representation

Filters are the commonly used techniques for texture feature extraction. In this paper, we present a texture feature extraction method based on multiple wavelet filters. For modeling the multiple wavelet coefficients, we develop Marginal Distribution Covariance Model (MDCM) in which the data points are projected into Cumulative Distribution Function (CDF) space and then the covariance model is constructed in the CDF space. MDCM can capture the dependence of variables, so it can be applied to model the texture features, in which the dependence exists, such as image intensities, color features and wavelet filter features. According to the characteristics of different wavelet filter features, we construct the different MDCMs in the three wavelet transform domains: Orthogonal Wavelet Transform (OWT), Dual Tree Complex Wavelet Transform (DTCWT) and Gabor Wavelet Transform (GWT). Experiments show the proposed method which uses multiple wavelet features can provide a promising performance compared with the state-of-the-art methods.

A feature extraction method for lung nodules based on a multichannel principal component analysis network (PCANet)

Feature extraction of lung nodules is very important in the diagnosis of lung cancer and is the premise of feature description, target matching, recognition and benign and malignant diagnosis. The main contribution of this work is the development of a new end-to-end feature extraction method that learns effective feature representation from images to effectively establish the direct relationship between multiple features and tissue features (that is, benign or malignant). The architecture consists of three seamlessly connected functional layers. RGB multichannel can automatically extract ROI sequence images involving lung parenchyma from the lung imaging sequence. The feature extraction layers, using Principal Component Analysis Network - random binary hash (PCANet-RBH), a) extract high-level semantic features of the R/G/B channel by cascading PCA and fuse the extracted normal color patterns, and b) generate multiple binary patterns via RBH to produce richer features with color information. The connected spatial pyramid pooling (SPP) layer can extract the location features of the lung nodules and map the feature matrix to the low-dimensional space and then establish a correspondence between the image features and the organizational identity. We validate the performance of the proposed method using the public dataset LIDC. The experimental results show that the fusion features extracted by our method have high and stable classification accuracy (accuracy:93.25 ± 0.53, sensitivity:93.12 ± 0.62 specificity:91.37 ± 0.62), which is significantly better than the traditional algorithm for lung nodule feature extraction. Moreover, RGB-PCANet has a short training time, which can meet the requirements of real-time diagnosis of lung cancer. In general, the advantage of our framework is that it provides a better and more comprehensive method to establish a direct relationship between image high-level semantic features, color features, location features and tissue features, making it an attractive clinical diagnostic tool for lung cancer.

A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.