Cb Components Research Articles

Rate-distortion optimized quantization for geometry-based point cloud compression

Limited by the network bandwidth, three-dimensional (3D) point cloud needs to be efficiently compressed before transmission. As one of the three attribute coding methods adopted in the geometry-based point cloud compression (G-PCC) standard developed by MPEG, predicting transform (PT) has received increasing attention. To further improve the coding efficiency of PT, we propose a rate-distortion optimized quantization (RDOQ) in which an additional option for quantization results is added by setting the quantized residuals to zero forcedly. Rate-distortion optimization is then used to determine whether the final quantized residual should be zero or nonzero. Experimental results show that average BD rates of −0.5 % , −3.0 % , and −3.0 % can be achieved for Luma, Chroma Cb, and Chroma Cr components, respectively, with negligible increment of time complexity.

Research on noncontact heart rate detection based on face tracking and BVP signal extraction

The noncontact heart rate detection technology is very suitable for daily health monitoring due to its advantages of no contact with the human body, simple operation, and low cost. However, this technique is susceptible to ambient light variation interference and motion artifacts. In addition, most of the existing HR (real-time heart rate monitoring) detection studies based on remote photoplethysmography only focus on illumination variation interference or motion artifacts, and few studies consider both illumination variation interference and motion artifacts. First, a nonparametric skin color segmentation model and support vector machine (SVM) are used in the Cb and Cr components of the YCbCr color space to construct a joint skin color segmentation model specific to the current video sequence, to remove the skin-like background in the video frame in a coarse-to-fine manner. Subsequently, the CamShift algorithm color histogram is constructed within the Cr component and face tracking is performed. It should be noted that the use of SVM to build a skin color segmentation model is based on the following three considerations: first, the skin color segmentation algorithm based on SVM has a relatively fast running speed. Second, considering skin color and background sample information at the same time will help to improve the accuracy of skin color segmentation. Third, SVM can enhance the generalization ability of the learning machine according to the principle of structural risk minimization, which makes the skin color segmentation model based only on video standard frames can also play a better role in the segmentation of subsequent video frames. The experimental results show that the proposed algorithm has higher accuracy for noncontact heart rate detection than the original algorithm; compared with the tracking algorithm in recent years, it has good tracking accuracy and obvious speed advantage.

Frequency based edge-texture feature using Otsu’s based enhanced local ternary pattern technique for digital image splicing detection

Sharing information through images is a trend nowadays. Advancements in the technology and user-friendly image editing tool make easy to edit the image and spread fake news through different social networking platforms. Forged image has been generated through an advanced image editing tool, so it is very challenging for image forensics to detect the micro discrepancy which distorted the micro pattern. This paper proposes an image forensic detection technique, which implies multi-level discrete wavelet transform to implement digital image filtering. Canny edge detection technique is implemented to detect the edge of the image to implement Otsu’s based enhanced local ternary pattern (OELTP), which can detect forgery-related artifact. DWT is implemented over Cb and Cr components of the image and using edge texture to improve the Otsu global threshold, which is used to extract features using ELTP technique. Support vector machine (SVM) is used for classification to find the image is forged or not. The performance of the work evaluated on three different open available data sets CASIA v1, CASIA v2, and Columbia. Our proposed work gives better results with some of the previous states of the work in terms of detection accuracy.

Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Watermarking Scheme for using YCbCr Based On 2-Level DWT

Multimedia data for both personal and commercial purposes is now accessible to everyone due to the rapid development of the Internet. Consequently, the issue of copyright protection has surfaced and has triggered the development of several techniques for multimedia copyright protection. Such techniques include digital watermarking in which the important information contained in the host media is concealed by embedment in carriers such as images, videos, or audios. In this paper, the adaptive color image watermarking technique is proposed for the satisfaction of both imperceptibility and robustness demands. There are two main stages involved in this technique – coding/embedding and decoding/extraction. Prior to the coding stage, imperceptibility and robustness are preserved by first converting the host image from RGB to YCbCr color space before selecting the Cb component to apply the DWT embedding technique. Once more, the selected quadrant of the hosted image is decomposed using DWT before extracting the watermarked image. The robustness and efficiency of this technique were proved by exposing the watermarked image to six types of attacks, namely Median filter, Gaussian noise, Sharpening filter, Salt & Pepper Noise, JPEG Compression, and Rotation. The results of the study were benchmarked against other methods that deploy DST on the same images. From the benchmarking process, the proposed algorithm was found to withstand the six types of attacks earlier mentioned and achieved a better performance compared to the DST approach. The quality of the watermarked image was also preserved in the proposed method.

Digital Image Tampering Detection Using Multilevel Local Binary Pattern Texture Descriptor

Digital images can be manipulated using the latest tools and techniques without leaving any visible traces. Image tampering detection is required to authenticate image validation. It is concluded from previous research that image tampering modifies the texture micropattern in a digital image. Therefore, texture descriptors can be applied to highlight these changes. A texture descriptor–based technique is proposed for detecting both copy-move and splicing forgery. In the proposed method, an RGB image is converted into a YCbCr image and Cb and Cr image components are extracted, as these components are more sensitive to tampering artifacts. Further, a standard deviation (STD) filter and higher-order texture descriptors are applied on Cb and Cr components. The STD filter is used to highlight important details of objects in the image. A support vector machine classifier is used to classify forged and tampered images. Support vector machine (SVM) classifier gives good results on both large- and small-image data sets. The performance is appraised in three online-available, widely used data sets: CASIA v1.0, CASIA v2.0, and Columbia. The proposed method outperforms most of the state-of-the-art methods.

Research on the Correlation between Breeding Environment and Activity of Yellow Feather Broilers Based on the Multichromatic Aberration Model.

Broiler behavior is closely related to the breeding environment. Therefore, studying broiler behavior helps breeding farm workers to better carry out welfare breeding. In the breeding environment of yellow feather broilers, temperature, humidity, and ammonia concentration are the main factors that affect the behavior of the broilers. This study used a multichromatic aberration model to process the color images of yellow feather broilers to extract the activity feature of the broilers at different periods, utilized the Cb component of YCbCr color model and the b component of Lab color model to remove background litter in images, and employed the Q component of YIQ color model to remove the feeders and the drinkers from the image. The segmented images were constructed into an accumulator to generate a heat map of yellow feather broilers' activity. Then, the correlation between the activity and the temperature and humidity index (THI) and the correlation between the activity and ammonia concentration were explored. The experiment found that the activity of the broilers was significantly positively correlated with ammonia concentration (P < 0.05), indicating that the activity of yellow feather broilers increased with ammonia concentration ascending. Besides, the THI in the broiler chamber had a significant positive correlation with the ammonia data (P < 0.01), indicating that when the THI in the broiler chamber increases, the ammonia concentration rises. The research provides a direction for exploring the impact of THI and ammonia concentration on the performance of yellow feather broilers. At the same time, it provides a theoretical basis for the early warning and judgment of broiler breeding by farm workers in the future.

Superpixel based robust reversible data hiding scheme exploiting Arnold transform with DCT and CA

The emergence of data hiding schemes was to provide multimedia security besides the sharing of information in a confidential manner. Since then, various techniques have cropped up to meet the objective of secured data communication using multimedia documents. In this paper, a novel data hiding approach has been designed to exploit the superpixel of the image to identify the nature of the blocks for embedding data using spatial and transform domain techniques such as Cellular Automata (CA) and Discrete Cosine Transform (DCT) respectively. In addition, an Arnold transform is used to provide a blanket of security through a random selection of blocks via sharable keys. The proposed technique obtains YCbCr color model of an RGB image to use superpixel on only the Y color component. Thereafter, the acquired labeled image of superpixel is partitioned into (8×8) blocks which are to be categorised as homogeneous and heterogeneous blocks. Based on the categorization, DCT and CA are applied for the embedding of the secret data in the Cb and Cr color components. The experimental results and its comparison with state-of-the-art schemes uphold the effectiveness of the demonstrated scheme. Various analysis have been conducted to showcase the degree of robustness, imperceptibility, and visual quality attained by the proposed scheme.

Image Steganography Based on Wavelet Transform and Color Space Approach

This paper presents a color image steganography scheme based on discrete wavelet transform (DWT) and YCbCr color space. The cover image is initially transformed from RGB color space into YCbCr and the Cb component is utilized for hiding a secret grayscale image. Two-level DWT is applied to the selected component to obtain the horizontal sub-band HL2 which is used for the embedding process based on the singular value decomposition (SVD) of the secret image and the obtained sub-band HL2 of the cover image. Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) are measured for evaluating the performance of the proposed scheme. Simulation results show that the proposed steganography method is reliable against several attacks without degrading the visual quality of the cover image. Further, comparative results for the proposed method in YCbCr and RGB color spaces show that the YCbCr space is more efficient in secret image retrieval

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images.

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

Curvelet transform based robust copyright protection scheme for color images using extended visual cryptography

A copyright protection scheme based on curvelet transform, k-means clustering and extended visual cryptography is proposed for color images. Unlike the existing schemes, the proposed scheme creates meaningful shares to provide better security and handles false positive cases efficiently. Curvelet Transform is applied on Y component of host image to generate six-scale layers. Out of these layers, non fine scale layers are selected to generate the master share, which is further processed by Henon map to enhance its security. Two watermarks are used in the proposed scheme. One of them is provided by the user and other one is constructed by using Cb and Cr components of the host image. Second watermark is used to to handle false positives. Meaningful ownership share is constructed from the master share, both watermarks and cover image by using extended visual secret sharing. Master share is overlapped with its corresponding ownership share to reveal the watermark which is further used to prove the ownership. Experimental results show that the proposed scheme clearly verifies the copyright of the digital images, is robust to withstand several image processing attacks and handles false positive cases efficiently. Comparison with the existing copyright protection schemes shows that the proposed scheme gives better performance.

An automatic visible-range video weed detection, segmentation and classification prototype in potato field

Weeds might be defined as destructive plants that grow and compete with agricultural crops in order to achieve water and nutrients. Uniform spray of herbicides is nowadays a common cause in crops poisoning, environment pollution and high cost of herbicide consumption. Site-specific spraying is a possible solution for the problems that occur with uniform spray in fields. For this reason, a machine vision prototype is proposed in this study based on video processing and meta-heuristic classifiers for online identification and classification of Marfona potato plant (Solanum tuberosum) and 4299 samples from five weed plant varieties: Malva neglecta (mallow), Portulaca oleracea (purslane), Chenopodium album L (lamb's quarters), Secale cereale L (rye) and Xanthium strumarium (coklebur). In order to properly train the machine vision system, various videos taken from two Marfona potato fields within a surface of six hectares are used. After extraction of texture features based on the gray level co-occurrence matrix (GLCM), color features, spectral descriptors of texture, moment invariants and shape features, six effective discriminant features were selected: the standard deviation of saturation (S) component in HSV color space, difference of first and seventh moment invariants, mean value of hue component (H) in HSI color space, area to length ratio, average blue-difference chrominance (Cb) component in YCbCr color space and standard deviation of in-phase (I) component in YIQ color space. Classification results show a high accuracy of 98% correct classification rate (CCR) over the test set, being able to properly identify potato plant from previously mentioned five different weed varieties. Finally, the machine vision prototype was tested in field under real conditions and was able to properly detect, segment and classify weed from potato plant at a speed of up to 0.15 m/s.

Efficient Color Artifact Removal Algorithm Based on High-Efficiency Video Coding (HEVC) for High-Dynamic Range Video Sequences

High efficiency video coding (HEVC) has been developed rapidly to support new generation display devices and their ultra high definition (UHD) with high dynamic range (HDR) and wide color gamut (WCG). To support HDR/WCG sequences on the HEVC standard, pre-/post-processing technique has been designed. After an HDR video is compressed, a reconstructed frame exhibits chromatic distortions that resemble color smearing. To remove this color artifact, we herein propose a block-level quantization parameter (QP) offset-control-based efficient compression algorithm for the HDR sequence. First, we extract the candidate coding units (CUs) with the annoying area to the human eye based on the just noticeable distortion (JND) model. Subsequently, the chromatic distorted blocks are verified by the activity function as the chromatic artifact is observed at the nearby strong edge. For the verified artifact blocks, we reassign the $QP\text{s}$ for the Cb and Cr chroma components. Our experimental results show that the proposed method yields an average gain in BD-rate of 3.3% for U, and 3.4% for V with a negligible bitrate increase of 0.3% on average.

Preparation and optical properties of Bi2FeVMoO10 nanoparticles structurally derived from cation-substitutions in Scheelite-type BiVO4

Bi2FeVMoO10 [(Bi0.8Fe0.4)(Mo0.4V0.4)O4] was prepared via facile co-precipitation route. It has a tetragonal Scheelite structure derived by cation substitutions in monoclinic BiVO4. Rietveld refinements of Bi2FeVMoO10 were performed in body-centered tetragonal Scheelite. The morphological property was tested via SEM, EDX, and TEM measurements. The optical property was measured by UV-vis absorption spectrum. Bi2FeVMoO10 shows a narrower band gap of 2.07 eV than the monoclinic BiVO4 and α-Bi2Mo3O12. The photo-degradation experiments of Rhodamine-B (RhB) dye solutions were conducted. Bi2FeVMoO10 nanoparticles have more efficient photocatalytic ability on RhB degradation than that of BiVO4 and α-Bi2Mo3O12. The results were discussed on the Scheelite structure characteristics such as complicated optical absorption centers, loose space in the lattice because of the vacancies, large dispersion in the CB and VB electronic components and the Fe3+/Fe2+ redox couples. This multicomponent Scheelite oxide could have potential applications in environment protection.

Computer vision-based apple grading for golden delicious apples based on surface features

In this paper, a computer vision-based algorithm for golden delicious apple grading is proposed which works in six steps. Non-apple pixels as background are firstly removed from input images. Then, stem end is detected by combination of morphological methods and Mahalanobis distant classifier. Calyx region is also detected by applying K-means clustering on the Cb component in YCbCr color space. After that, defects segmentation is achieved using Multi-Layer Perceptron (MLP) neural network. In the next step, stem end and calyx regions are removed from defected regions to refine and improve apple grading process. Then, statistical, textural and geometric features from refined defected regions are extracted. Finally, for apple grading, a comparison between performance of Support Vector Machine (SVM), MLP and K-Nearest Neighbor (KNN) classifiers is done. Classification is done in two manners which in the first one, an input apple is classified into two categories of healthy and defected. In the second manner, the input apple is classified into three categories of first rank, second rank and rejected ones. In both grading steps, SVM classifier works as the best one with recognition rate of 92.5% and 89.2% for two categories (healthy and defected) and three quality categories (first rank, second rank and rejected ones), among 120 different golden delicious apple images, respectively, considering K-folding with K=5. Moreover, the accuracy of the proposed segmentation algorithms including stem end detection and calyx detection are evaluated for two different apple image databases.

Human Skin Region Segmentation Based on Chrominance Component Using Modified Watershed Algorithm

A novel watershed segmentation algorithm has been proposed to segment the human skin region of RGB color images based on Cb component of YCbCr color space in this paper. The conventional watershed segmentation algorithm is not suitable for segmentation of human skin region from color images directly, since it is difficult to decide suitable color information of human skin. At first stage, the RGB image is converted into YCbCr color space which contains three components luminance, blue difference component (Cb) and red-difference component (Cr). The Cb component has been extracted and used for further processing steps. After extraction of Cb component, the modified marker based watershed algorithm has been applied for segmentation of human skin region only from RGB color image. The proposed method has been tested on FRI CVL Face Database. In experimental stage, results show that the proposed technique avoids over segmentation problem and produce good segmentation result which is comparable with the same obtained by other algorithms.

Static Hand Gestures: Fingertips Detection Based on Segmented Images

Fingertips detection is important for recognition of static gesture in sign language. This paper presents a new method that is based on YCbCr colour space and skeletonization for fingertips detection towards gesture segmentation and recognition. The method begins with the conversion of an image in RGB colour space into YCbCr colour space. Then, the chrominance Cb and Cr are extracted from the YCbCr colour space. A thresholding technique, which is based on a pre-defined range for Cb and Cr components representing the skin colour value is used to extract the hand from the background and to achieve the binary image. A morphological processing is performed on the obtained binary image to remove noise and unwanted image pixels. The candidate fingertips position is then calculated based on skeletonization algorithm and tracing process. The centroid is subsequently found, after which the Euclidean distances between the pixels’ coordinates that belong to the candidate fingertips and the centroid are calculated towards validating their representation of the actual fingertips. Based on the proposed method, the fingertips of twenty-six American Sign Language (ASL) alphabets hand sign samples were detected successfully and the gesture was correctly recognized with an accuracy of 96.3% during the conducted experiments.

Identifying Sport Types and Postures with Complex Background by Fusion of Local Descriptors

Sport type classification and posture identification based on visual meaning of posture semantic in still images are challenging tasks. The difficulty of these tasks comes from the complex image content consisting of a player's posture, the color and texture of a player's clothes as well as complexity of the background. Player detection is one of the most important tasks in posture identification. For sport type classification without object segmentation, the new set of features, based on 64-bins color histogram, DCT coefficients, and Cb and Cr components, is introduced. To achieve high accuracy, an appropriate feature extraction technique should be also realized. For posture identification, three algorithms, concerning player region detection and suitable features for posture identification, are proposed namely blurred background elimination, irrelevant region elimination, and trimming players region. The DFT coefficients, based on image resizing and slicing techniques, are used as significant features in posture identification. Our proposed features were compared with Edge Histogram and Region-based Shape (EH and RS), two of MPEG-7 descriptors. The experimental results showed that our proposed features yielded better performance with 85.76% of accuracy in sport classification and 86.66% of accuracy in posture identification.

An Improved QIM Watermarking Algorithm

Put forward an improved QIM watermark embedding algorithm. The carrier image is transformed into YCbCr space, lifting wavelet transform the three components, then using the QIM quantization method embeds watermark into high frequency coefficients of Cb and Cr component, and Y component of low frequency coefficients. The experimental results show that embedded watermark which using improved algorithm has good invisibility. Anti attack test results show that after using common attack method attack the image embedded watermark, it can still extract the watermark completely. The anti-attack performance of improved QIM algorithm is much better than texture watermarking algorithm based on DCT.

Fuzzy Watermarking for Colour Images based on Fusion of Discrete Wavelet Transform (DWT) Technique and Histogram Stretching

This study presents a method of scheming colour image by using fuzzy watermarking through Discrete Wavelet Transform (DWT) and histogram stretching methods. This study aims further at achieving and developing robustness, imperceptibility and available capacity of cover image for the watermarked image, which can withstand against various attacks. Digital watermarking techniques are used to protect the copyrights of multimedia data by embedding secret information in the host media, for example, embedding in images, audios, or videos. Many watermarking techniques have been proposed in the literature to solve the copyright violation problems, but most of these techniques failed to satisfy both imperceptibility and robustness requirements. In this research, adaptive RGB colour image fuzzy watermarking technique is proposed. The host image is converted from RGB to YCbCr colour space to preserve imperceptibility and robustness, then, Cb component is extracted and partitioned into four quadrants. While the Histogram Stretching will be used to convert the Watermark image to 4-bits plan to preserve the capacity for watermarking.