Vehicle detection algorithm based on codebook and local binary patterns algorithms

In this paper, face recognition technology is studied and explored, and the classical local binary pattern (LBP) algorithm is studied and improved. An improved LBP algorithm is proposed to solve the deficiency of LBP: a "double circle" LBP algorithm is proposed, which improves the rotation invariability of LBP to a greater extent and has a good recognition effect on small-pose face images. On the basis of good recognition effect of LBP based on partitioning, the idea of "multiple partitioning + intermediate partitioning" is put forward, which effectively makes up for the shortage of incomplete extraction of peripheral information of original segmentation line. Facial features are the key features of face recognition. Different organs contribute differently to face image recognition. The idea of "key block" based on the original block is proposed. Finally, the application of an LBP algorithm for face recognition is taken as an example, and the experimental results verify the reliability of the face recognition method based on LBP for weak light and expression change images.

Studies on two-factor authentication based on RFID and face recognition have been carried out on a large scale. However, these studies didn't discuss the way to overcome the weaknesses of face recognition authentication in the access control systems. In this study, two authentication factors, RFID and face recognition, were implemented using the LBP (Local Binary Pattern) algorithm to overcome weaknesses of face recognition authentication in the access control system. Based on the results of performance testing, the access control system has 100% RFID authentication and 80% face recognition authentication. The average time for the RFID authentication process is 0.03 seconds, the face recognition process is 6.3885 seconds and the verification of the face recognition is 0.1970 seconds. The access control system can still work properly after three days without being switched off. The results of security testing showed that the capabilities spoofing detection has 100% overcome the photo attack.

To detect defects in fabrics more efficiently, easily and accurately, a method based on Local Binary Pattern (LBP) is proposed in this paper. The main purpose of this algorithm is to extract the feature value of fabric images. Firstly the feature of the whole defect-free fabric image is got with LBP algorithm. Then the image is divided into small detection windows, and the feature of each window can be obtained. Compare their similarity calculated by Chi-square function to get the threshold. Then process the defective images according to the same procedure. At last compare the similarity with the threshold to obtain defect regions. The defects are detected at the same time. Experimental results demonstrate that, LBP algorithm is effective in the area of detecting defects of fabrics.

In this paper a new approach is proposed. The new approach is inspired by the compressive sensing theory used for data compression. Feature vectors from the facial data were extracted using compressive sensing technique. The compressive measurements were obtained at different feature vector lengths. Comparison of recognition rates with Local Binary Pattern (LBP) feature extractor were made using different classifiers. The results obtained by using three distance measures and Support Vector Machines (SVM) validate the performance of our proposed approach as it outperforms the best result obtained using LBP algorithms at different combinations of cells and bins numbers. Moreover, performance comparison using Sparse Representation Classifier (SRC) shows that LBP can only matches the proposed method performance with a very large feature vectors size which is computationally expensive for SRC.

Gender identification is a new domain in image recognition. Gender identification of human face is to judge one's gender according to his/her face features. The article adopted local binary pattern (LBP) algorithm to build feature subspaces, and processed data using Support Vector Machine (SVM) learning models. Experiments showed that integration of LBP algorithm with linear SVM and integration of LBP algorithm with low-order polynomial SVM are better than Gauss-kernel function.

Based on neural network and local binary pattern algorithm, this paper builds a lightweight artificial face recognition system on chip Firefly-RK3399, with high speed, strong robustness and high recognition accuracy. Our embedded artificial intelligent face recognition system mainly consists of face detection, feature extraction and recognition. Multi-task convolutional neural network (MTCNN) under the CaffeOnACL framework is utilized for face detection, and the local binary pattern (LBP) is applied as face recognition algorithm. Experiments illustrate that our artificial intelligent embedded face recognition system has high speed and accuracy, which is easy-carrying and of high commercial value as well.

Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle

Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) is used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint.

In this paper several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection system.

Embryo transfer is an extremely important step in the process of in- vitro fertilization and embryo transfer (IVF-ET). The identification of the embryo with the greatest potential for producing a child is a very big challenge faced by embryologists. Most current scoring systems of assessing embryo viability are based on doctors’ subjective visual analysis of the embryos’ morphological features. So it provides only a very rough guide to potential. A classifier as a computer-aided method which is based on Pattern Recognition can help to automatically and accurately select embryos. This paper presents a classifier based on the support vector machine (SVM) algorithm. Key characteristics are formulated by using the local binary pattern (LBP) algorithm, which can eliminate the inter-observer variation, thus adding objectivity to the selection process. The experiment is done with 185 embryo images, including 47 “good” and 138 “bad” embryo images. The result shows our proposed method is robust and accurate, and the accurate rate of classification can reach about 80.42%.

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different resolutions. By considering features from multiple levels, the detection algorithm can better capture both global and local characteristics of the manipulated regions, enhancing the accuracy of forgery detection. To achieve a high accuracy rate, this paper presents a variety of scenarios based on a machine-learning approach. In Copy-Move detection, artifacts and their properties are used as image features and support Vector Machine (SVM) to determine whether an image is tampered with. The dataset is manipulated to train and test each classifier; the target is to learn the discriminative patterns that detect instances of copy-move forgery. Media Integration and Call Center Forgery (MICC-F2000) were utilized in this paper. Experimental evaluations demonstrate the effectiveness of the proposed methodology in detecting copy-move. The implementation phases in the proposed work have produced encouraging outcomes. In the case of the best-implemented scenario involving multiple trials, the detection stage achieved a copy-move accuracy of 97.8 %.

Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%.

Recognition of patterns in a person by using parts of the human body, such as on fingerprints, has been widely applied in life such as to perform absenteeism, tracking a criminal, system security and so on. Local Binary Pattern (LBP) algorithm is known as an algorithm that can describe local texture pattern in an area. LBP uses 8 scattered circular neighborhoods with center pixels centered. In a 3 x 3 pixel image, the binary value in the image center is compared with the surrounding value. The surrounding value will be 1 if the central pixel value is smaller, and is 0 if the central binary value is greater. A total of 78 data were used for this study where 26 data were using blue ink fingerprints, and 26 black ink data. After the fingerprint pattern data obtained then the image is scanned. After that the image in the crop to be 50 pixels x 50 pixels, so all the data becomes uniform. The algorithm used to make an introduction is the Manhattan Distance algorithm. Based on the test results of 26 test data with different color inks, the result obtained accuracy of 61.54%.

For the demand of leaf recognition under rotation condition. This paper propose a method to extract the multi-feature extraction and Locally Preserving Projection (LPP) fusion algorithm. First, the texture feature of the leaf is extracted by Local Binary Pattern (LBP) algorithm based on leaf block. Then, in order to improve the speed of feature classification recognition, this paper adopts LPP algorithm for feature dimension reduction of the high dimensional of LBP. In addition, combining leaf texture feature LBP with shape feature (Hu Moment invariants) after dimension reduction, shaping into the comprehensive characteristics of plant leaves. Finally, Support Vector Machine (SVM) classifier is used to establish classifier for classification and identification of plant leaves, the results of experiment have verified the validity of the algorithm in this paper.

It is a feasible and meaningful way to preserve the precious murals by using digitization technology. However, it may need to stitch multiple images to get a complete mural panorama. It poses great challenges to stitch and blend these multiple images in a seamless and visually pleasant way. In this paper, we propose an image stitching and blending method based on image pyramid, including Gaussian and Laplacian pyramid, for obtaining a natural stitched images of Dunhuang murals. In special, Gaussian pyramid is used to down-sample the images and get multiple images of different scales through using Gaussian filters. Laplacian pyramid is applied to generate the difference image between each levels of the Gaussian pyramid. Finally, the images can be blended in an adjustable way by specifying the weights of different images. In this way, the stitched images can be presented with reasonable image fusion. Through using the real Dunhuang murals images, the efficiency of the proposed method has been validated.