Detection Of Reference Points Research Articles

Estimation of errors due to errors in the detection of reference points of the arterial blood pulsation signal

The issues of detection of reference points of the arterial blood pulsation signal under the influence of physical and physiological artifacts are considered. Comparison of schemes for detecting the reference point of the arterial blood pulsation signal is carried out. As a criterion for the effectiveness of the scheme for detecting reference points of the arterial blood pulsation signal, the methodological error in determining the temporary position of the reference point from the true value was chosen. It has been shown that among the parametric means for detecting the reference point of the arterial blood pulsation signal, the most effective is the amplitude-time detector based on synchronous processing of ECG signals and arterial blood pulsation.

Computer Vision-Based Inspection System for Worker Training in Build and Construction Industry

Recently computer vision has been applied in various fields of engineering successfully ranging from manufacturing to autonomous cars. A key player in this development is the achievements of the latest object detection and classification architectures. In this study, we utilized computer vision and the latest object detection techniques for an automated assessment system. It is developed to reduce the person-hours involved in worker training assessment. In our local building and construction industry, workers are required to be certificated for their technical skills in order to qualify working in this industry. For the qualification, they are required to go through a training and assessment process. During the assessment, trainees implement an assembly such as electrical wiring and wall-trunking by referring to technical drawings provided. Trainees’ work quality and correctness are then examined by a team of experts manually and visually, which is a time-consuming process. The system described in this paper aims to automate the assessment process to reduce the significant person-hours required during the assessment. We employed computer vision techniques to measure the dimensions, orientation, and position of the wall assembly produced hence speeding up the assessment process. A number of key parts and components are analyzed and their discrepancies from the technical drawing are reported as the assessment result. The performance of the developed system depends on the accurate detection of the wall assembly objects and their corner points. Corner points are used as reference points for the measurements, considering the shape of objects, in this particular application. However, conventional corner detection algorithms are founded upon pixel-based operations and they return many redundant or false corner points. In this study, we employed a hybrid approach using deep learning and conventional corner detection algorithms. Deep learning is employed to detect the whereabouts of objects as well as their reference corner points in the image. We then perform a search within these locations for potential corner points returned from the conventional corner detector algorithm. This approach resulted in highly accurate detection of reference points for measurements and evaluation of the assembly.

Artificial intelligence in orthodontics: Where are we now? A scoping review.

This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11-year time restriction: January 2010 till March 2021. No additional manual searches were performed. The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.

Video-Based System for Automatic Measurement of Barbell Velocity in Back Squat.

Velocity-based training is a contemporary method used by sports coaches to prescribe the optimal loading based on the velocity of movement of a load lifted. The most employed and accurate instruments to monitor velocity are linear position transducers. Alternatively, smartphone apps compute mean velocity after each execution by manual on-screen digitizing, introducing human error. In this paper, a video-based instrument delivering unattended, real-time measures of barbell velocity with a smartphone high-speed camera has been developed. A custom image-processing algorithm allows for the detection of reference points of a multipower machine to autocalibrate and automatically track barbell markers to give real-time kinematic-derived parameters. Validity and reliability were studied by comparing the simultaneous measurement of 160 repetitions of back squat lifts executed by 20 athletes with the proposed instrument and a validated linear position transducer, used as a criterion. The video system produced practically identical range, velocity, force, and power outcomes to the criterion with low and proportional systematic bias and random errors. Our results suggest that the developed video system is a valid, reliable, and trustworthy instrument for measuring velocity and derived variables accurately with practical implications for use by coaches and practitioners.

Automatic detection of anatomical landmarks of the aorta in CTA images.

Computed tomography angiography (CTA) is one of the most common vascular imaging modalities. However, for clinical use, it still requires laborious manual analysis. This study demonstrates the feasibility of a fully automated technology for the accurate detection and identification of several anatomical reference points (landmarks), commonly used in intravascular imaging. This technology uses two different approaches, specially designed for the detection of aortic root and supra-aortic and visceral branches. In order to adjust the parameters of the developed algorithms, a total of 33 computed tomography scans with different types of pathologies were selected. Furthermore, a total of 30 independently selected computed tomography scans were used to assess their performance. Accuracy was evaluated by comparing the locations of reference points manually marked by human experts with those that were automatically detected. For supra-aortic and visceral branches detection, average values of 91.8 % for recall and 98.8 % for precision were obtained. For aortic root detection, the average difference between the positions marked by the experts and those detected by the computer was 5.7 ± 7.3 mm. Finally, diameters and lengths of the aorta were measured at different locations related to the extracted landmarks. Those measurements agreed with the values reported by the literature. Graphical abstract Schematic description of the proposed algorithm. The input includes an already segmented aorta (left), there are two main sub-processes related to the detection of branches and roots (center), and the output includes the segmented original aorta with the branches and the detected landmarks superimposed (right).

A new methodology for automatic detection of reference points in 3D cephalometry: A pilot study

The aim of this study was to develop a new method for an automatic detection of reference points in 3D cephalometry to overcome the limits of 2D cephalometric analyses. A specific application was designed using the C++ language for automatic and manual identification of 21 (reference) points on the craniofacial structures. Our algorithm is based on the implementation of an anatomical and geometrical network adapted to the craniofacial structure. This network was constructed based on the anatomical knowledge of the 3D cephalometric (reference) points. The proposed algorithm was tested on five CBCT images. The proposed approach for the automatic 3D cephalometric identification was able to detect 21 points with a mean error of 2.32mm. In this pilot study, we propose an automated methodology for the identification of the 3D cephalometric (reference) points. A larger sample will be implemented in the future to assess the method validity and reliability.

Human Friendly Interface Design for Virtual Fitting Room Applications on Android Based Mobile Devices

This paper presents an image processing design flow for virtual fitting room (VFR) applications, targeting both personal computers and mobile devices. The proposed human friendly interface is implemented by a three-stage algorithm: Detection and sizing of the user's body, detection of reference points based on face detection and augmented reality markers, and superimposition of the clothing over the user's image. Compared to other existing VFR systems, key difference is the lack of any proprietary hardware components or peripherals. Proposed VFR is software based and designed to be universally compatible as long as the device has a camera. Furthermore, JAVA implementation on Android based mobile systems is computationally efficient and it can run in real-time on existing mobile devices.

Experiences with computer navigated total knee arthroplasty

The successful outcome of total knee arthroplasty (TKA) is very much dependent on precise positioning of the components. Inaccuracy may result in complaints as well as in early mechanical failure. Between March 2003 and September 2005, 69 TKA procedures were performed by the computer navigated technique. The postoperative outcome of this cohort was compared with the same number of TKAs done by the traditional technique. The lower limb anatomical axis was determined in all cases pre- and postoperatively by weight-bearing anteroposterior (AP) and lateral full length X-rays. The positions of femoral and tibial components were recorded. Comparing the data in the navigation group on the AP view, 96.6% of femoral and 96.9% of tibial components and on the lateral view in 95.4% of femoral and in 95.4% of tibial components, the overall postoperative axis in 95.4% fell in the range considered in the literature as optimal. In the traditional group on the AP view, 75.7% of femoral and 68.1% of tibial components and on the lateral view 81.8% of femoral and 63.6% of tibial components, the overall postoperative axis in 60.6% fell between the values considered optimal in the literature. It seems to be proven that the computer navigated total knee arthroplasty technique ensures positioning of components significantly more precisely compared with the traditional surgical method. Accuracy of navigation depends on the software used, on the correct detection of anatomical reference points, and on a potentially uneven thickness of the cement layer during final insertion of the components. The computer navigated technique does not substitute professional skill and experience, since it merely transmits information for the surgeon. The decision is in the hands of the doctor during the entire procedure. The real benefits of the computer navigated technique require further research and can be determined only after long-term analyses.

Fast Fingerprint Verification Using Subregions of Fingerprint Images

We propose a fast fingerprint matching methodology based on localizing the matching regions in captured fingerprint images. The determination of the locations of such regions relies on the accurate detection of reference points in the images together with a priori knowledge of the complete fingerprint obtained during the fingerprint enrollment procedure. The relationship between authentication reliability and region size is studied experimentally. Results show that sufficiently accurate fingerprint matching can be achieved using very small bitmaps, making it possible to implement very fast fingerprint authentication systems using relatively slow embedding processors.

Speaker‐independent consonant recognition by integrating discriminant analysis and hmm

AbstractA new consonant recognition method is proposed which integrates two stochastic methods: discriminant analysis and HMM. Discriminant analysis is effective in analyzing local patterns but it assumes precise detection of reference points. HMM is able to extract the overall dynamic features and needs no explicit segmentation of speech. However, it lacks the ability to discriminate between similar consonants. The method herein constructs HMM with discriminant analysis front end and recognizes consonants by combining the score obtained by discriminant analysis and that by HMM. For all the Japanese consonants, this integrated method achieved the recognition rate of 92.1 percent, which is higher by 5 to 15 percent than the case using either of the two methods alone.

Beat-to-beat interval measurement in the electrocardiogram.

A method of measuring online interval durations between different waves in the electrocardiogram is presented. After preprocessing two signals, data segments containing the wave segments of interest are sampled. The detection of time reference points for these waveforms is accomplished in a separate recognition phase (of prime importance for the reliability) and an estimation phase (mainly important for the method's accuracy). The recognition of a given wave segment is triggered by a pulse derived from the QRS complex by a hardware R-wave detector, to which the P-wave and T-wave are time related under normal circumstances. A recognition window is defined interactively by fixing a starting point and an end point in the sampled data segment. The estimation of wave positions is performed with the aid of four estimation criteria, which are described in the text. The capabilities of the method are discussed with reference to the P-P, P-R and R-R interval measurements under different experimental conditions.