ASM Algorithm Research Articles

Optimization of Fatigue Detection Method under Altitude Changes in Plateau Region Based on MTCNN

Fatigue driving is the main cause of traffic accidents, and research on fatigue driving detection algorithms is of great significance to improve road safety. This paper proposes an image processing method based on MTCNN model detection optimization, Perform median filter denoising before P-Net training to improve the detection rate of night faces, then, the ASM algorithm is used to detect the facial feature points, and finally the PERCLOS principle is used to analyze the driving fatigue state. The experimental results show that the method has a high detection rate, can be applied to fatigue detection at different altitudes, and has strong practicability.

An auricular division method based on ASM algorithm.

BACKGROUND:Auricular acupuncture point (AAP) therapy is an important part of traditional Chinese medicine and is featured with a sophisticated location method based on the division of auricular subzones.OBJECTIVE:This study aimed to realize the automatic computerized division on the relatively small area of the research object which has long been considered difficult.METHOD:We propose a novel method based on the active shape model algorithm and the “Name and location of AAPs” issued by the World Federation of Acupuncture-Moxibustion Societies (WFAS STANDARD-002:2013).RESULTS:The experimental results showed that the subzones of the auricle could be divided for the location of AAPs using the proposed method automatically and efficiently. The average Hausdorff distance and Euclid distance of landmarks between the machine and the manual positioning were 6.28 0.50 and 6.67 0.59, respectively.CONCLUSIONS: The proposed method might provide benefits for further development of therapeutic and educational applications of AAPs.

3D segmentation of lungs with juxta-pleural tumor using the improved active shape model approach.

BACKGROUND AND OBJECTIVE: At present, there are many methods for pathological lung segmentation. However, there are still two unresolved problems. (1) The search steps in traditional ASM is a least square optimization method, which is sensitive to outlier marker points, and it makes the profile update to the transition area in the middle of normal lung tissue and tumor rather than a true lung contour. (2) If the noise images exist in the training dataset, the corrected shape model cannot be constructed.METHODS: To solve the first problem, we proposed a new ASM algorithm. Firstly, we detected these outlier marker points by a distance method, and then the different searching functions to the abnormal and normal marker points are applied. To solve the second problem, robust principal component analysis (RPCA) of low rank theory can remove noise, so the proposed method combines RPCA instead of PCA with ASM to solve this problem. Low rank decompose for marker points matrix of training dataset and covariance matrix of PCA will be done before segmentation using ASM.RESULTS: Using the proposed method to segment 122 lung images with juxta-pleural tumors of EMPIRE10 database, got the overlap rate with the gold standard as 94.5%. While the accuracy of ASM based on PCA is only 69.5%.CONCLUSIONS: The results showed that when the noise sample is contained in the training sample set, a good segmentation result for the lungs with juxta-pleural tumors can be obtained by the ASM based on RPCA.

Study on intelligent traffic search method based on driver facial feature analysis

With the rapid development of Internet technology and biometrics technology in China, artificial intelligence has gradually entered into every aspect of people's life. Big data is used to upgrade intelligent traffic search for people, which improves the efficiency and accuracy of people search for people. Intelligent traffic search is a hotspot in the field of biometric identification and plays an important role in social stability. As an important feature, driver's face image can not only provide great help to the detection of illegal vehicles, but also help to carry out the tracing of missing people, so as to maintain social harmony and stability. Therefore, the intelligent traffic search method based on the analysis of driver's facial features has a broad application prospect and research value. This paper investigates the current international top facial recognition algorithm technology level, and proposes a face image illumination invariant feature extraction algorithm and face feature detection ASM algorithm. The experimental results show that the intelligent traffic search method in this paper has a good recognition rate, and the study in this paper also has a certain guiding significance for the application of image processing in the field of intelligent traffic.

Study on intelligent traffic search method based on driver facial feature analysis

With the rapid development of Internet technology and biometrics technology in China, artificial intelligence has gradually entered into every aspect of people's life. Big data is used to upgrade intelligent traffic search for people, which improves the efficiency and accuracy of people search for people. Intelligent traffic search is a hotspot in the field of biometric identification and plays an important role in social stability. As an important feature, driver's face image can not only provide great help to the detection of illegal vehicles, but also help to carry out the tracing of missing people, so as to maintain social harmony and stability. Therefore, the intelligent traffic search method based on the analysis of driver's facial features has a broad application prospect and research value. This paper investigates the current international top facial recognition algorithm technology level, and proposes a face image illumination invariant feature extraction algorithm and face feature detection ASM algorithm. The experimental results show that the intelligent traffic search method in this paper has a good recognition rate, and the study in this paper also has a certain guiding significance for the application of image processing in the field of intelligent traffic.

Face Classification in Adults and Minors, an Approach based on Facial Anthropometry

The process of age estimation is a task of computer vision that represents a challenge to the diverse factors that influence the process of aging in humans such as environment, food, and toxic habits, among others. Such a task has been studied from different approaches. In this work, a geometric approach is developed by using distances and proportions existing in every face. The functionality is increased by the use of fiducial points to locate any characteristic features of the face; its location is based on craniofacial growth studies used in the area of forensic anthropometry and plastic surgery. The location of 15 fiducial points is considered through the ASM algorithm and 8 distances between fiducial points, which statistically verify the difference in facial proportions between adults and minors. The distances obtained are analyzed with the Discriminant Analysis procedure to study the correlations between distances and the corresponding age group. From this, a model that allows classifying a new image in an age group is constructed: adults; those over 18 years of age, and minors; those under 17 years of age, obtaining 90% accuracy.

Attendance automatic recognition and learning behavior of web-based course attendance based on machine learning algorithms

At present, the automatic attendance mode of distance education is not conducive to the confirmation and analysis of information after class. In order to study the effective automatic recognition algorithm of remote education classroom, this study takes the educational classroom of intelligent innovation and entrepreneurship of Internet + as an example for analysis. Moreover, this paper adopts facial features as the basis of recognition, establishes corresponding positioning points, and constructs precise positioning methods for real-time feature capture. At the same time, the ASM algorithm is used to extract facial features, and the algorithm is improved to improve the extraction effect. In addition, this paper proposes Gabor-wavelet packet set and Gabor beamlet set for auxiliary recognition, which improves the recognition rate. Finally, this paper designs experiments to analyze the performance of the algorithm of this study. The results show that the proposed algorithm has certain practical effects and can provide theoretical reference for subsequent related research.

A hybrid eye movement feature recognition of classroom students based on machine learning

Online classroom teaching is difficult to identify students’ learning status in real time. Therefore, we need to combine intelligent image recognition technology to analyze student status through eye movement features. This study solves the problem of inaccurate positioning of the initial position of the shape model in the process of eyelid matching through machine learning. Moreover, this study improves the algorithm and uses the AK-EYE model based on the combination of ASM algorithm and Kalman filtering to establish a local feature model for each feature point. According to the gray information in the normal direction of the feature point, the local gray information is modeled. After training through the sample set to obtain the state model, the target eye can be searched, and the pose parameters can be determined. Finally, this study designs a control experiment to analyze the performance of the model proposed in this study. The research shows that the algorithm proposed in this paper has a high recognition accuracy and has a practical basis, which can be used as one of the subsequent classroom teaching system algorithms.

Research of Face Detection Based on AdaBoost and ASM

Face Detection is an essential first step of the face recognition process, and has significant effects on face fea- ture extraction and the effects of face recognition. Face detection has extensive research value and significance. This pa- per deeply researches and analysis the principle, merits and demerits of the classic AdaBoost face detection algorithm and ASM algorithm based on point distribution model, using ASM to solve the problems of face detection based on AdaBoost. Firstly, the method uses the AdaBoost algorithm to detect original face from images or video stream. Secondly it uses ASM algorithm converges, which fit face region detected by Adaboost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experiment results indicate that this method can detect face rapidly and precisely, with a strong robustness.

Margin Refine of Candide-3 Model for Facial Deformation

Because of the structure of the CANDIDE model is simple and the quantity of its marginal gridlines is small, an obvious marginal trace would appear while the face changed. In view of this, this paper proposed a method which refined the facial outline gridlines to improve the effect of facial transformation. Firstly, the feature point of the pupil is set to enhance the location veracity of the model, then 44 key feature points are chosen to represent the information of the basic facial features. The face matching is realized by ASM algorithm, then fit and refine the extracted contour line personalized to enhance the expressive force of the model of CANDIDE-3. Experimental results show that the result of facial transformation has been optimized. The effect of the transformation is optimized on the premise of the computation speed is guaranteed, and the result looks more naturally.

Enhance ASM Based on DCT-SVM for Facial Feature Points Localization

Focused on the facial feature points localization, the enhance ASM algorithm based on modeling texture by the DCT-SVM is proposed. First, the statistical shape model is built. Then, some key feature points are selected and their texture models are built by the DCT-SVM. In the subsequent searching, the feature points are divided into two classes based on their reliability gained by DCT-SVM detector, by combining the reliable feature points to the shape constraint, the original shape can finally match to the target face. Experiments show the algorithm is robust to the expressions change and can better locate the features than the traditional ASM.

A Novel Smart Household Control System by Computer Vision

For the disable people or the aged people, they need extra help in their daily household life. Various modern devices have been developed to improve their living quality. These devices always have automatic functions which are considered crucial for their independent life. Different to the traditional automatic household appliance such as manually infrared remote-controller and automatic voice-operated switch, this paper presents a novel approach based on computer vision to realize intelligent household control. This proposed system uses Adaboost and ASM algorithm for face detection and feature point calibration. Based on the facial key points, the head postures and mouth states are calculated and then coded into a special data packet. Finally, the packet is sent by an infrared carrier to the intelligent appliance with infrared receiver to control their operation manner. By using computer vision technology with infrared communication, the system is especially effective for the disable people such as the upper-body paraplegic and the people with hand dysfunction.

An extension of Ukkonen's enhanced dynamic programming ASM algorithm

We describe an improvement on Ukkonen's Enhanced Dynamic Programming (EHD) approximate string-matching algorithm for unit-penalty four-edit comparisons. The new algorithm has an asymptotic complexity similar to that of Ukkonen's but is significantly faster due to a decrease in the number of array cell calculations. A 42% speedup was achieved in an application involving name comparisons. Even greater improvements are possible when comparing longer and more dissimilar strings. Although the speed of the algorithm under consideration is comparable to other fast ASM algorithms, it has greater effectiveness in text-processing applications because it supports all four basic Damerau-type editing operations.