YCbCr Color Model Research Articles

PENERAPAN METODE NAÏVE BAYES DALAM KLASIFIKASI KESEGARAN IKAN BERDASARKAN WARNA PADA CITRA AREA MATA

As a maritime nation, fish is a staple in the Indonesian diet, rich in nutrition and a crucial protein source. It is imperative to maintain the freshness of fish to ensure the quality of fish production. However, the practice of mixing fresh and non-fresh fish poses a serious threat to consumer health and diminishes the overall quality of fish production. Therefore, the development of an automated and efficient method is necessary to distinguish between fresh and non-fresh fish. This research proposes the application of the Naïve Bayes method in classifying fish freshness based on color analysis in the eye area image. This approach involves the extraction of entropy features after segmenting fish images using the RGB and YCbCr color models. A total of 40 datasets of fish eye images were used for training and testing the model. The research results indicate that the proposed classification method achieved an accuracy rate of 97.5%. This success signifies the potential of the color analysis method and entropy features in distinguishing levels of fish freshness. These findings contribute to the development of automated techniques for monitoring and processing fish quality in the fisheries industry.

RBWCI: Robust and Blind Watermarking Framework for Cultural Images

Heritage multimedia, which comprises images, audio, and videos, are precious artifacts of a region. In addition to enabling a better understanding of earlier generations, heritage media provides information about their creative approach, style of living, and diversity of historical and archaeological ideologies. Heritage is an important resource that boosts the local economy, generates sustainable communities, and improves tourism and business sectors. Once heritage images are dissipated to consumers, they may be transmitted through wire/wireless systems, where the data may be accessed by both authorized and unauthorized consumers. With the rapid advancement of technology and 5G networks, establishing an approach that protects cultural heritage media from unauthorized consumers is necessary. This study presents a robust and blind watermarking-framework for cultural images (RBWCI) utilizing the discrete cosine transform domain for ownership verification and copyright protection. The embedding scenario of our technique relies on the difference between coefficients obtained from the two channels of the YCbCr color model. The watermark bit is embedded by taking the difference between two preselected mid-frequency coefficients (Cb-Cr). Moreover, chaotic and deoxyribonucleic acid encryption are employed to ensure double-layer watermark security. The proposed algorithm demonstrates a peak signal-to-noise ratio between 42-43 dB and structural similarity index metric value of approximately 1 for different test images when not under attack. The robustness of the RBWCI was revealed by comparing it with various state-of-the-art schemes, making it suitable for different consumer applications.

Digital Image Processing to Detect Cracks in Buildings Using Naïve Bayes Algorithm (Case Study: Faculty of Engineering, Halu Oleo University)

Purpose: To detect cracks in the walls of buildings using digital image processing and the Naïve Bayes Algorithm.Design/methodology/approach: Using the YCbCr color model for the segmentation process and the HSV color model for the feature extraction process. This study also uses the Naïve Bayes Algorithm to calculate the probability of feature similarity between testing data and training data.Findings/result: Detecting cracks is an important task to check the condition of the structure. Manual testing is a recognized method of crack detection. In manual testing, crack sketches are prepared by hand and deviation states are recorded. Because the manual approach relies heavily on the knowledge and experience of experts, it lacks objectivity in quantitative analysis. In addition, the manual method takes quite a lot of time. Instead of the manual method, this research proposes digital-based crack detection by utilizing image processing. This study uses an intelligent model based on image processing techniques that have been processed in the HSV color space. In addition, this study also uses the YcbCr color space for feature extraction and classification using the Naïve Bayes Algorithm for crack detection analysis on building walls. The accuracy of the research test data reached 88.888888888888890%, while the training data achieved an accuracy of 93.333333333333330%.Originality/value/state of the art: This study has the same focus as previous research, namely detecting cracks in building walls, but has different methods and is implemented in case studies.

Fire Detection using Image Processing

To reduce loss of life and property from fire, an early warning is an imperative. A fire detection system based on light detection and analysis is proposed in the paper. This system uses HSV and YCbCr color models with given conditions to separate orange, yellow, and high brightness light from background and ambient light. Fire growth is analysed and calculated based on frame differences. The overall accuracy from the experiments has been greater than 90%.

A Method of Detecting Candidate Regions and Flames Based on Deep Learning Using Color-Based Pre-Processing

Recently, object detection methods using deep learning have made significant progress in terms of accuracy and speed. However, the requirements of a system to provide real-time detection are somewhat high, and current methods are still insufficient to accurately detect important factors directly related to life and safety, such as fires. Therefore, this study attempted to improve the detection rate by supplementing the existing research to reduce the false detection rate of flame detection in fire and to reduce the number of candidate regions extracted in advance. To this end, pre-processing based on the HSV and YCbCr color models was performed to filter the flame area simply and strongly, and a selective search was used to detect a valid candidate region for the filtered image. In addition, for the detected candidate region, a deep learning-based convolutional neural network (CNN) was used to infer whether the object was a flame. As a result, it was found that the flame-detection accuracy of the model proposed in this study was 7% higher than that of the other models presented for comparison, and the recall rate was increased by 6%.

Using Computer Vision to Track Facial Color Changes and Predict Heart Rate.

The current technological advances have pushed the quantification of exercise intensity to new era of physical exercise sciences. Monitoring physical exercise is essential in the process of planning, applying, and controlling loads for performance optimization and health. A lot of research studies applied various statistical approaches to estimate various physiological indices, to our knowledge, no studies found to investigate the relationship of facial color changes and increased exercise intensity. The aim of this study was to develop a non-contact method based on computer vision to determine the heart rate and, ultimately, the exercise intensity. The method was based on analyzing facial color changes during exercise by using RGB, HSV, YCbCr, Lab, and YUV color models. Nine university students participated in the study (mean age = 26.88 ± 6.01 years, mean weight = 72.56 ± 14.27 kg, mean height = 172.88 ± 12.04 cm, six males and three females, and all white Caucasian). The data analyses were carried out separately for each participant (personalized model) as well as all the participants at a time (universal model). The multiple auto regressions, and a multiple polynomial regression model were designed to predict maximum heart rate percentage (maxHR%) from each color models. The results were analyzed and evaluated using Root Mean Square Error (RMSE), F-values, and R-square. The multiple polynomial regression using all participants exhibits the best accuracy with RMSE of 6.75 (R-square = 0.78). Exercise prescription and monitoring can benefit from the use of these methods, for example, to optimize the process of online monitoring, without having the need to use any other instrumentation.

A New Block Based Non-Blind Hybrid Color Image Watermarking Approach Using Lifting Scheme and Chaotic Encryption Based on Arnold Cat Map

Online platforms became preferred mode of communication due to advancement in communication technology. Sharing of digital documents over online communication medium grown exponentially and thus demanded a secure, robust and transparent watermarking technique for authenticity of digital media and copyright protection. This research study proposes a robust and secure non-blind SVD-LWT watermarking technique. Color images are employed instead of gray scale images and Y channel of YCbCr color model is utilized to embed secret digital information. The selected color model is in accordance with the human visual system and Y channel is ideal for data hiding. Two level LWT, SVD is used and diagonal matrix of Y channel of host (cover) and watermark image along with scaling factor (α) is used to embed digital data. Block based and chaotic image encryption transform are used for image scrambling. The performances of presented watermarking scheme evaluated with the aid of fidelity parameters namely MSE, PSNR, SSIM and NCC.

АЛГОРИТМ И МЕТОДИКА РАНЖИРОВАНИЯ ГРУППЫ РАСТРОВЫХ ИЗОБРАЖЕНИЙ

The article presents an algorithm and a methodology of ranking a group of raster images by using the criterion of their expected quality. Ranking refers to the evaluation of a sample of bitmap images in a descending order of their quality, the image quality assessment being performed on the basis of a number of statistical parameters, such as coefficients of variation, determination, rank correlation index, as well as errors (absolute maximum error, average error, average quadratic error). The differences between the images are based on converting a full-color RGB image into HSV, Lab, NTSC, XYZ, YCbCr color models, which are represented as one-dimensional pixel ma-trices. The colour model RGB is taken as a reference. In relation to it, the proposed statistical char-acteristics of other color models are compared, any object of each color model being compared with the base model - an RGB image. Based on this comparison, all images of a given group are analyzed independently of each other. Image quality assessment is performed in a module that can be used to cycle through multiple images and is represented in numerical form as a real number. One of the module blocks calculates the statistical parameters between each color model and the base RGB model. After receiving the values of the quality scores they are ranked according to their values. As a result, an image with a higher or lower scene quality can be determined. Images with blocking artifacts, noisy images of the salt & pepper type, and images with strobe effects artifacts were considered as test images.

Face Spoof Detection Using VGG-Face Architecture

Face recognition systems have been obtaining substantial importance in modern world. Security systems are major application of face recognition system. However, the potential of the face recognition system to withstand the attack of an unauthorized person is an important concern. Face recognition systems are vulnerable to photographs and video spoof attacks. In these scenarios, anti-spoofing systems comes in handy to evade these attacks. Robust solutions are required for face recognition system to be immune against spoofing attacks. In this paper, the detected face is denoised and then converted to YCbCr and CIELUV colour model and then passed through VGG-Face architecture for extraction of face embeddings of each colour space. Then the extracted face embeddings are concatenated and then passed through SVC (Support Vector Classifier) which then classifies real and spoof faces. The proposed method has obtained a test accuracy of 99.6% with specificity of 99.5% for spoof detection.

Image-processing based facial imperfection region detection and segmentation

As interest in skin-beauty has recently increased, the consumption of skin-related products has steadily increased. Visiting a professional medical institution or clinic to determine one’s skin condition(s) for self-management is not only costly but also cumbersome. It is also difficult to recognize small changes in the skin when an individual is managing the skin. To solve these problems, we suggest a new image-processing based system that detects facial imperfection regions such as wrinkles, moles, etc. of someone’s facial photographs taken with normal cameras rather than with equipment such as expensive skin microscopes. When the facial photograph is input to our system, the LAB color model is used to correct the illumination effect, and then only the skin region of the face is extracted by using the range of the skin region of the HSV and YCbCr color models as well as by the K-Means algorithm. After the facial region is extracted, the Gabor filter is processed to detect the reactivity of discontinuity. Finally, the density-based spatial clustering of applications with noise algorithm is processed to classify areas of wrinkles, spots, and other skin diseases. Experiments on various images showed that the proposed algorithm showed a robust response to the distortion caused by the illumination and obtained remarkable results. Our method allows the user to check the skin condition of images taken in real life.

Image Compression Based on Arithmetic Coding Algorithm

The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.

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.

Analysis of the relationship between the choice of the color model for the representation of images and the efficiency of solving the problem of improving the image resolution

The article discusses the influence of the choice of a color model for representing a raster image on the efficiency of increasing its resolution using an artificial neural network. The article begins with the essence of the research. After that, the process of obtaining for training networks with an identical topology of a dataset using different color models of image representation, namely RGB, HSV, L * a * b *, NTSC and YCbCr, is described. After that, an assessment of the effectiveness of trained neural networks on the previously described datasets is given. To assess the performance of an artificial neural network, two algorithms are used: SSIM and PSNR. As a result of the assessment, the networks using RGB and YCbCr color models showed the highest results. At the end of the article, there are reflections on the reasons for this result.

Image Processing Based Forest Fire Detection using Infrared Camera

When time goes by, human beings are advancing in technology, artificial and natural disasters are drastically increasing. The forest fire is one of the hazards. Forest fire incinerates trees that provide us with oxygen and if it is not detected early, it is very elusive to stop a forest fire from continue burns. The project’s objective is to capture infrared image of forest fire detection using the appropriate camera, detect fire with RGB and YCbCr colour model to isolate fire pixels from the background and separate luminance and chrominance from the original image, and filter image using MATLAB Analyzer to process images. The method is tested on a selected image, which captured by the camera that contains fire. Next method is used for calculating and analysing the fire image, which to differentiate between fire detection or false detection. Other method is used to process the fire image, which the image will compute and shown in terminal nodes and graphs by using Wavelet Analyzer 5.0. The results of this system are achieved fire detection and obtain data for the fire images.

Facial image recognition for biometric authentication systems using a combination of geometrical feature points and low-level visual features

This paper proposes a method for biometric-driven facial image recognition, based on multivariate correlation analysis, which extracts geometrical feature points and low-level visual features. The low-level visual features, such as colour and texture, are extracted at local from the selected prominent regions of the facial image. The geometrical features are captured using the Active Shape Model (ASM). The colour features are extracted from the YCbCr colour model, and the autocorrelation method is deployed to extract the texture features. The extracted features are formed as a feature tensor matrix. The feature matrix of the key face image is compared to the feature matrices of the target face image that are stored in the feature vector database using the Canonical Correlation method. The correlation between the key and target feature matrices is tested, whether it is highly significant or not. If it is significantly correlated, then it is inferred that the key and target face images are the same; otherwise, it is concluded that they are different. The benchmark facial image datasets, GT, LFW, and Pointing ’04 datasets, considered for the experiments; in addition to the datasets, a facial image database has been constructed with celebrities on our interest which has also been subjected to experiments. The proposed method resulted in a mean precision score (mP@α) of 95.27%, 94.20%, 96.19%, and 96.05 forGT, LFW, Pointing ‘04, and our datasets, respectively. Also, the F-score was calculated that are, 96.78%, 95.15%, 97.08%, and 96.96%forGT, LFW, Pointing ‘04, and our datasets, respectively. The results obtained by the proposed method are comparable to the existing methods.

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.

Design of Image Extraction Hardware for Hand Gesture Vision Recognition

In this paper, we propose a system that can detect the shape of a hand at high speed using an FPGA. The hand-shape detection system is designed using Verilog HDL, a hardware language that can process in parallel instead of sequentially running C++ because real-time processing is important. There are several methods for hand gesture recognition, but the image processing method is used. Since the human eye is sensitive to brightness, the YCbCr color model was selected among various color expression methods to obtain a result that is less affected by lighting. For the CbCr elements, only the components corresponding to the skin color are filtered out from the input image by utilizing the restriction conditions. In order to increase the speed of object recognition, a median filter that removes noise present in the input image is used, and this filter is designed to allow comparison of values and extraction of intermediate values at the same time to reduce the amount of computation. For parallel processing, it is designed to locate the centerline of the hand during scanning and sorting the stored data. The line with the highest count is selected as the center line of the hand, and the size of the hand is determined based on the count, and the hand and arm parts are separated. The designed hardware circuit satisfied the target operating frequency and the number of gates.

Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

The Arithmetic Coding and Hybrid Discrete Wavelet and Cosine Transform Approaches in Image Compression

Image compression is one of the data compression types applied to digital images in order to reduce their high cost for storage and/or transmission. Image compression algorithms may take the benefit of visual sensitivity and statistical properties of image data to deliver superior results in comparison with generic data compression schemes, which are used for other digital data. In the first approach, the input image is divided into blocks, each of which is 16 x 16, 32 x 32, or 64 x 64 pixels. The blocks are converted first into a string; then, encoded by using a lossless and dictionary-based algorithm known as arithmetic coding. The more occurrence of the pixels values is codded in few bits compare with pixel values of less occurrence through the sub intervals between the range 0 and 1. Finally, the stream of compressed tables is reassembled for decompressing (image restoration). The results showed a compression gain of 10-12% and less time consumption when applying this type of coding to each block rather than the entire image. To improve the compression ratio, the second approach was used based on the YCbCr colour model. In this regard, images were decomposed into four sub-bands (low-low, high-low, low-high, and high-high) by using the discrete wavelet transform compression algorithm. Then, the low-low sub-band was transmuted to frequency components (low and high) via discrete wavelet transform. Next, these components were quantized by using scalar quantization and then scanning in a zigzag way. The compression ratio result is 15.1 to 27.5 for magnetic resonance imaging with a different peak signal to noise ratio and mean square error; 25 to 43 for X-ray images; 32 to 46 for computed tomography scan images; and 19 to 36 for magnetic resonance imaging brain images. The second approach showed an improved compression scheme compared to the first approach considering compression ratio, peak signal to noise ratio, and mean square error.

Stress conditions in soybean areas based on measurements of soil-plant-atmosphere system and UAV images

ABSTRACT The identification of stress conditions in soybean crops is, in most cases, inaccurate, since they may not be noticeable to their full extent if only walking observations are carried out in the crop fields. This study aimed to identify the stress conditions in soybean crops, in three growing environments, in the Minas Gerais state, Brazil, using image processing techniques obtained by UAV, leaf and soil sensors, and climate data. The surveys encompassed two growth stages [beginning of blooming (R1) and beginning of seed enlargement (R5)] and consisted on UAV flights; mapping of chlorophyll content, soil moisture and soil pH; in addition to climate data. The HSV and yCbCr color models applied to RGB images showed the best Kappa accuracy index for the identification of crop features. The soil pH and moisture (water availability), solar radiation and temperature affected the crop growth and development in the study regions, in the R1 and R5 reproductive stages. However, the soil pH had less influence than the climatic variables. The R5 stage showed a greater vulnerability to stress caused by soil moisture and temperature.