Local Histogram Research Articles

Reversible data hiding based on automatic contrast enhancement using histogram expansion

A lot of Reversible Data Hiding (RDH) methods aim to generate a stego image infinitely approaches the original image while the quality of the original image is leaved out of consideration. Juxtaposed with a plain image, a contrast enhanced version always improves the user experience significantly. Reversible Data Hiding with Contrast Enhancement (RDHCE) enhances the stego image contrast combined with its payloads and enables the cover image to be regained accurately after the payloads have been extracted. This study presents a novel RDHCE method using histogram expansion. First, a new local histogram selecting strategy is proposed to improve the contrast of the whole image. Meanwhile, the global average brightness is used as a reference to determine the shifting direction of the local histogram to prevent the image from being over-enhanced. Moreover, the contrast can be improved adaptively when a reasonable number of data is embedded at the selected embedding points. Experimental results show that, with a given payload, the proposed method achieves better contrast and maintains good visual quality compared with state-of-the-arts.

Local Binary Pattern Histogram Based Web Facial Authentication System

The objective of the project is to Detect and Recognize Human Facial Features via a Camera. The basic concept of this project is:- To convert the input image to HSV format to extract the binary image and additionally remove the noise from binary image using morphological operations and finally then contours are used to segment out the region of interest and further the region is analyzed to get the final result. While biometric data is generally considered one of the most reliable authentication methods, it also carries significant risk. That’s because if someone’s credit card details are hacked, that person has the option to freeze their credit and take steps to change the personal information that was breached. What do you do if you lose your digital ‘face’? Around the world, biometric information is being captured, stored, and analyzed in increasing quantities, often by organizations and governments, with a mixed record on cybersecurity. A question increasingly being asked is, how safe is the infrastructure that holds and processes all this data? As facial recognition software is still in its relative infancy, the laws governing this area are evolving (and sometimes non-existent).

HAND GESTURE AND DETEKSI WAJAHDETECTION USING RASPBERRY PI

Face detection is currently used for various purposes, one of which is to record employees attendance. This strategy is ineffective since the employees still can hack the attendance by making their own photos and put them in their desks. If they are unable to come to the office,they can always ask their colleagues to submit their already available photos.Therefore, an alternative that can complement the current face detection method is highly needed.One of the methods that can be used is hand gesture detection.This study aims to detect hand gestures made by the employees to ensure whether they really come to work or not,so the chance for manipulation is quite small.For the purpose of hand gesture recognition, this study utilized Local Binary Pattern Histogram algorithm. LBPH is an algorithm used for the image matching process between images that have been given training and images taken in real time.The hand gesture image was first taken using a raspberry pi camera and then processed to examine whether it matches the registered ID or not.The results showed that ID recognition by using hand gestures is detectable and is in accordance with the registered ID.The number recognition in hand gestures includes numbers 1 to 10. The test results showed that, the average time required for reading hand gestures using a laptop was 9.2 seconds, while that of using raspberry was 14,2 seconds.Motion reading using a raspberry takes longer than that of using a laptop because the laptop's performance is higher than that of a raspberry.

ANALYSIS OF APPLICATION HAAR CASCADE CLASSIFIER AND LOCAL BINARY PATTERN HISTOGRAM ALGORITHM IN RECOGNIZING FACES WITH REAL-TIME GRAYSCALE IMAGES USING OPENCV

Face detection and recognition systems have been developed with the various application of algorithms. Based on the literature study that has been carried out, the researcher will analyze the performance between the HCC (Haar Cascade Classifier) ​​and LBPH (Local Binary Pattern Histogram) Algorithms with real-time grayscale images using the OpenCV library. The test is carried out based on a sample of facial images with external conditions in the form of lighting conditions which are divided into morning, afternoon, and evening, as well as varying face rotation angles. Parameters observed were accuracy values, FAR (False Acceptance Rate), and FRR (False Rejection Rate). Based on the results of the tests that have been carried out, the average value of accuracy is 56%, while the average value of FAR is 22% and FRR is 23%. Judging from the average accuracy value obtained is 56%, then to be able to be detected and recognized properly the face position must be in frontal condition and with normal lighting. Thus, the final results of this study can be considered for other researchers who want to use a similar algorithm to develop a detection and recognition system.

Optimized Deep Learning-Based E-Waste Management in IoT Application via Energy-Aware Routing

Recycling, reusing, and reducing electronic garbage (E-waste) may be the sole methods for managing E-waste in use today. In essence, there is no ideal method of managing E-waste. This process required more labor and resources. E-waste classification is fulfilled using fractional Henry gas optimization-based deep convolutional neural network (FHGO-based deep CNN) is shown in this manuscript. To predict the optimal path the E-waste images are conversed through the energy-aware FHGO routing algorithm. The feature extraction procedure is performed to cut out the features for example the gray level co-occurrence matrix (GLCM) feature, local Gabor binary pattern (LGBP) and histogram of oriented gradient (HOG) and the pre-processing phase is concluded with a median filter. To supplement the extracted feature size the data augmentation is fulfilled. Moreover, the E-waste classification is done based on deep CNN, which is trained using a FHGO algorithm. FHGO is exhibited by the merging of Henry gas solubility optimization (HGSO) algorithm, fractional calculus (FC). Comparing to the existing approaches like deep learning, tensor flow deep learning, Cuckoo search-based neural network and machine learning the accuracy of the proposed method is 19.49%, 18.05%, 12.77%, and 7.89% privileged.

CAD System Based on Face Mask Recognition for Respiratory Infections Diseases Hospital

The infection of respiratory diseases can be eliminating and controlling by wearing face mask in contaminated areas. However, to control people about wearing face mask it has been challenging unless the automatic recognitions are applied. Therefore, in this paper, a Face Mask Recognition System by Computer Aided Design (CAD) is introduced. The proposed design system is based on face, mouth and nose detections in captured image. The CAD system considers to be implemented for specialized respiratory diseases hospital with different departments, each department controlled by separated door. The main goal of this paper is to design system based on software programs that helps reduce the spread of respiratory diseases and controlling wearing face mask inside respiratory infection diseases hospital based on mask detection and mask color detection. The proposed system designed for hospital with three respiratory diseases departments and three mask color applied each mask color for each department. The mask recognition system has been used cascaded object detector that is Local Binary Pattern Histogram LBPH algorithm, then color detection as artificial intelligence-based method, Red, Green, and Blue (RGB) color of the face mask images. Finally, by using of Convolutional Neural Network (CNN), the classification accuracy of color recognition achieved 100%, and also the whole system functionality tested successfully obtained all results by testing accuracy 95%. The hardware designing circuit simulation in Proteus software were obtained to control the systems of hospital department doors based on the results obtained from MATLAB software.

APLIKASI PRESENSI KEHADIRAN GURU DI SMKN PAKISJAYA BERBASIS FACE RECOGNITION MENGGUNAKAN OPENCV

The research objective was to design a teacher attendance application that has a camera feature to take pictures during teacher absences so that it can simplify and speed up attendance requests for absences from work, leave, permission and illness. The prototype method is used as a tool to develop a model into a system with stages which include; gathering requirements, building prototypes, evaluating, coding attendance systems, conducting testing, evaluating, and finally using presence applications to see the functions of the applications being built. The results of this study have produced a prototype of teacher presence at SMKN Pakisjaya based on face recognition using OpenCV and implementing the Local Binary Pattern Histogram (LBPH) as an algorithm for detecting faces from images.

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials.

Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time-frequency representations in the form of mel spectrograms. Then, two image descriptors-the local binary pattern (LBP) and histogram of oriented gradient (HOG)-are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect, accounting for about 30% of congenital defects, is the most common one. Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns. In Fetal and Neonatal Cardiology, medical imaging technology (2D ultrasonic, MRI) has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis. It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane (FHUSP) manually. Compared with manual identification, automatic identification through artificial intelligence can save a lot of time, ensure the efficiency of diagnosis, and improve the accuracy of diagnosis. In this study, a feature extraction method based on texture features (Local Binary Pattern LBP and Histogram of Oriented Gradient HOG) and combined with Bag of Words (BOW) model is carried out, and then feature fusion is performed. Finally, it adopts Support Vector Machine (SVM) to realize automatic recognition and classification of FHUSP. The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets. Compared with some other methods and the single method model, the classification accuracy of our model has been obviously improved, with the highest accuracy reaching 87.35%. Similarly, we also verify the performance of the model in normal and abnormal planes, and the average accuracy in classifying abnormal and normal planes is 84.92%. The experimental results show that this method can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

An Enhanced LBPH Approach to Ambient-Light-Affected Face Recognition Data in Sensor Network

Although combining a high-resolution camera with a wireless sensing network is effective for interpreting different signals for image presentation on the identification of face recognition, its accuracy is still severely restricted. Removing the unfavorable impact of ambient light remains one of the most difficult challenges in facial recognition. Therefore, it is important to find an algorithm that can capture the major features of the object when there are ambient light changes. In this study, face recognition is used as an example of image recognition to analyze the differences between Local Binary Patterns Histograms (LBPH) and OpenFace deep learning neural network algorithms and compare the accuracy and error rates of face recognition in different environmental lighting. According to the prediction results of 13 images based on grouping statistics, the accuracy rate of face recognition of LBPH is higher than that of OpenFace in scenes with changes in ambient lighting. When the azimuth angle of the light source is more than +/−25° and the elevation angle is +000°, the accuracy rate of face recognition is low. When the azimuth angle is between +25° and −25° and the elevation angle is +000°, the accuracy rate of face recognition is higher. Through the experimental design, the results show that, concerning the uncertainty of illumination angles of lighting source, the LBPH algorithm has a higher accuracy in face recognition.

Neural Image Style Transfer Using Modified Histogram Matching

Neural Image Style Transfer is an algorithm which was first introduced in 2016 for transferring the style of an image using a CNN. Over the years, a large portion of the research has been devoted in balancing the trade-off between time taken by the algorithm to process the images and the quality of the results generated. In addition, the application of traditional image processing algorithms has been limited. In this paper, we aim to introduce a modification to the traditional method by utilizing a localized histogram matching algorithm combined with Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show that the quality of style transferred and the colour of images obtained by this method is much better than the traditional method for the same number of iterations.

Raspberry Based Hand Gesture Recognition Using Haar Cascade and Local Binary Pattern Histogram

Many companies and even public institutions for civil servants currently use photo-taking for the attendance. However, this strategy is still considered ineffective since the employees still can hack the attendance by making their own photos and put them in their desks. Therefore, an alternative that can complement the current face detection method is highly needed so that the employee’s attendance can be directly monitored. One of the methods that can be used to detect the attendance is hand gesture detection. This research aims to detect hand gestures made by the employees to ensure whether they really come to work or not. This research make the chance for manipulation using photo or fake GPS is quite small. For the purpose of hand gesture recognition, this study utilized Local Binary Pattern Histogram algorithm. The hand gesture image was first taken using a raspberry pi camera and then processed by the device to examine whether it matches the registered ID or not. The results showed that ID recognition by using hand gestures is detectable. The number recognition in hand gestures includes numbers 1 to 10. The test results showed that for 5 trials, the average time required for reading hand gestures using a laptop was 9.2 seconds, while that of using raspberry was 14.2 seconds. The results of this research show that the system has not been able to distinguish which hand is read first, so numbers that have the same number are considered the same, such as 81 and 18. So, the motion reading using a raspberry takes longer than that of using a laptop because the laptop's performance is higher than that of a raspberry and system cannot distinguish between numbers consisting of the same number.

An efficient facial expression recognition system with appearance-based fused descriptors

Facial expressions have always been defined as the most efficient way to characterize emotions. Therefore, they have been used in various fields of applications such as human-computer interaction. With the continuous progress in terms of image processing and machine learning techniques, several new methods are proposed each year. However, their performance still needs to be improved and this task remains challenging. In this paper, we propose an automatic emotion recognition system through facial analysis. We compare the use of two distinct image descriptors namely, Local Binary Pattern and Histogram of Oriented Gradients with two different dimensionality reduction techniques namely, Principal Component Analysis and Locally Linear Embedding. Moreover, we also combine both descriptors in order to improve the system efficiency. For the classification part of the system, we choose to use the multi-class Support Vector Machine classifier for its generalization capabilities to distinguish between the six basic emotions. Finally, we assess the performance of the proposed system using three different benchmark facial expression datasets namely, JApanese Female Facial Expression (JAFFE), Karolinska Directed Emotional Faces (KDEF) and Radboud Faces Database (RaFD) which yield promising recognition rates with 97.16%, 90.12% and 95.54%, respectively.

A Method for Local Contrast Enhancement of Endoscopic Images Based on Color Tensor Transformation into a Matrix of Color Vectors’ Modules Using a Sliding Window

A new method aimed at endoscopic color images’ local contrast enhancement is proposed, based on local sliding histogram equalization with adaptive threshold limitation, color distortions correction, and image brightness preservation. For this, the original RGB image, represented as a tensor of size M × N × 3, is transformed into a matrix of size M × N, composed by the color vectors’ modules. As a result of local contrast enhancement, the obtained color vectors are symmetrical in respect of the input ones, because they satisfy the requirement for invariance after rotation. To enhance the local contrast, recursive local histogram equalization with adaptive thresholding is applied to each matrix element. This threshold divides the histogram into two regions of equal areas. A new metric for local contrast enhancement evaluation based on the mean square difference entropy is proposed. The presented new method is characterized by low computational complexity, due to the lack of direct and inverse color conversion and the possibility for adaptive local contrast enhancement, which is essential for accurate medical diagnosis based on endoscopic images analysis. In addition, the presented method performs both the correction of color distortions and the brightness preservation of each pixel.

A comparative Study of Image Enhancement Techniques for Natural Images

Enhancement is most interesting parts in image processing field. It uses to enhance the structural appearance for picture without the degradation in the original input image. The enhancement techniques become the important key and simply extraction features by removing noise and another items inside an image. Several enhancement techniques have achieved with different and inaccurate results. The aim of this paper, the nature images quality was improved by the many enhancement techniques like of Histogram Equalization (HE), Local Histogram Equalization (LHE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Recursive sub-image histogram equalization (RSIHE). In evaluation step, the performance of all these techniques are examined by values measurements of SSIM (Structural Similarity Index Matrix), Entropy, Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR). The comparisons of the better existing results are given because to explain the best possible technique that can be used as suitable image enhancement. The results of the enhanced of 15 nature images have showed DSIHE technique has a batter a values of SSIM and Entropy with 0.9885 and 59975 respectively. Overall, based on the PSNR and SNR values, the CLAHE technique are recorded values higher than of other six techniques in 21.2952 and 192932 values.

Development of a Facial Recognition-based Attendance System using Binary Patterns Histograms Method and Telegram Bot Notification

In carrying out attendance activities, most schools still use the manual attendance method, which utilizes the attendance book as a medium. However, the presence of the manual still causes some problems that arise when using this method. With the aim of this study, a "Facial Recognition-Based Attendance System" was created. This study used the Local Binary Pattern Histogram (LBPH) method to detect and recognize faces. The system is made to recognize the student's face and name, which the attendance system will then carry out along with the student's identity in the form of Name and Absence Number in real time to find out that the student is present in the class. The names that have been diabase can be saved through the XLS format. The result of this study is a facial recognition-based attendance system using the LBPH (Local Binary Pattern Histogram) method. The system is web-based, so users can easily access it through the internet. This research method uses research and development methods. This research stage consists of research and information collection, planning, and product draft development. The results of this study showed the feasibility level of facial recognition-based attendance systems. The eligibility rate obtained showed a score of 78.98%. From these results, this system is worthy of being used as an attendance system.

Development of surveillance robot based on face recognition using Raspberry-PI and IOT

The advancement in recent technologies such as artificial intelligence (AI), computer vision (CV) and internet of things (IoT) extends to various areas, especially surveillance systems that require real-time facial recognition processing to ensure safety. In fact, mobile robots are widely used in surveillance systems to perform dangerous tasks that humans cannot.In this context, we propose a prototype of a low-cost mobile surveillance robot based on Raspberry PI 4 which can be integrated into any industrial area. This intelligent robot checks about the presence of intruder using IoT and face recognition technology.The system is equipped with passive infrared (PIR) sensor and camera to record live streaming video and photos, which are transmitted to the room control via IoT.Thanks to facial recognition algorithms, the system can distinguish between company personnel and intruders. Haar Cascade Classifier and LBPH (Local Binary Pattern Histogram) algorithm are used in image processing to detect and identify individuals. When a stranger is detected, alert notifications and email with captured image are sent to the room control via IoT. A web interface was developed to remotely control the designed robot via a WiFi connection. This application allows monitoring of a wide area and can improve the robot's perception system. Performance evaluations will be conducted to demonstrate the effectiveness and robustness of the face recognition algorithms used in this framework.

An algebra for local histograms

In this article, we consider local overlapping histograms of functions between discrete domains and codomains. We develop a simple algebra for local histograms. Based on a separation of overlapping domains into non-overlapping domains, we (1) show how these can be used to enumerate the size of the set of possible histograms given the local histogram domains, and (2) enumerate the number of functions, which share a specific choice of a set of local histograms. Finally, we present a decoding algorithm, which given a set of overlapping histograms, and calculate the set of functions, which share these histograms.

Blur and Motion Blur Influence on Recognition Performance of Color Face

Face recognition is an existing and one of the most prominent biometrics techniques, including the processing of images. It is widely used in many applications. The performance of such systems is directly due to face image quality. Since blur and motion blur are common imagery problems, this paper explores the influence of such disturbances on color face recognition performance. The research described in this paper compares the performance of the face recognition algorithm based on the Haar features and Local Binary Patterns Histograms when it uses color face images of good quality, images with added Gaussian blur and motion blur, as well as enhanced images.

Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method

In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme.