Automatic Recognition System for Relative Position of Required Shape in Workpiece Space

In recent years, we have developed several automatic marking systems and used them for marking students' programs, Word files, Excel files, and e-mails in our courses. The automatic marking software provides an effective way to know students' learning states, and enables us to conduct many tests in a large class or an internet online class. Teachers can give students more appropriate instructions and advice referring to the information collected by the systems. If we can expand the use of automatic marking software for other subjects, more teachers and students would benefit. However, developing reliable and efficient marking systems using conventional methods is a difficult and time-consuming job. To help programmers develop automatic marking systems in a clearer, more systematic way, we need a software development model for automatic marking systems. Through development of our systems, we found common structures among several different systems. Generalizing the structures is a first step towards making the model. In this paper, we share our experience of developing automatic marking software and using them in our classes. We then formalize generalizations of the automatic marking systems.

Human perception of sign language can serve as inspiration for the improvement of automatic recognition systems. Experiments with human signers show that sign language signs contain redundancy over time. In this article, experiments are conducted to investigate whether comparable redundancies also exist for an automatic sign language recognition system. Such redundancies could be exploited, for example, by reserving more processing resources for the more informative phases of a sign, or by discarding uninformative phases. In the experiments, an automatic system is trained and tested on isolated fragments of sign language signs. The stimuli used were similar to those of the human signer experiments, allowing us to compare the results. The experiments show that redundancy over time exists for the automatic recognizer. The central phase of a sign is the most informative phase, and the first half of a sign is sufficient to achieve a recognition performance similar to that of the entire sign. These findings concur with the results of the human signer studies. However, there are differences as well, most notably the fact that human signers score better on the early phases of a sign than the automatic system. The results can be used to improve the automatic recognizer, by using only the most informative phases of a sign as input.

To meet the testing requirements of optical automatic target recognition system for loitering munition, based on digital twin, a parallel test system is proposed. Using this parallel test system, single application tests in the design stage of optical automatic target recognition system are carried out first, and then the operational course of action of loitering munition is verified. Through test examples, the analytical method of the technical and tactical capacity of optical automatic target recognition system is introduced. Besides, by carrying out operational course of action verification tests, the effectiveness of designed trajectory planning is verified. With the help of parallel test, more trajectory planning options will be derived to provide reference for actual flight tests.

In recent years, with the popularity of the Internet, digital media has become an indispensable part of people's life. Image acquisition equipment is widely used in daily life and work. However, due to technical problems, it can not meet the performance requirements of users. At the same time, a large number of data and information are difficult to be effectively processed, which also reduces the image quality, reduces the storage space and increases the image distortion. Therefore, it is necessary to develop new algorithms to improve these defects. Therefore, this paper designs and develops an automatic digital media image recognition system based on mean clustering algorithm. Firstly, this paper describes the concept and characteristics of digital media. Then, this paper also studies the application of mean clustering algorithm. Based on this, an automatic digital media image recognition system is designed, and the performance of the system is tested by simulation. Finally, the test results show that the algorithm can accurately identify media images. In general, the performance of the digital media image automatic recognition system based on mean clustering algorithm can meet the needs of target users.

With the increasing improvement of living standards, healthy long-distance running has become one of the current ways of aerobic physical exercise. Long-distance running is beneficial to many bodily functions such as neck, spine, and heart, but inappropriate long-distance running can easily damage the joints of the human body. In order to make more fitness athletes run with a healthy and reasonable posture, this paper studies the automatic recognition and correction system of running movements based on computer vision technology. Based on the analysis of human motion detection methods and human motion recognition methods, an automatic recognition and correction system for running actions is designed. In order to understand the accuracy of the system in the recognition of wrong actions, this paper compares the designed system with the traditional system. The results show that the system designed in this paper has good accuracy and can control the deviation within a reasonable range.

Optical Character Recognition (OCR) research includes computer vision, artificial intelligence, and pattern recognition. Character recognition has garnered a lot of attention in the last decade due to its broad variety of uses and applications, including multiple-choice test data, business documents (e.g., ID cards, bank notes, passports, etc.), and automatic number plate recognition. This paper introduces an automatic recognition system for printed numerals. The automatic reading system is based on extracting local statistical and geometrical features from the text image. Those features are represented by eight vectors extracted from each digit. Two of these features are local statistical (A, A th), and six are local geometrical (P1, P2, P3, P4, P5, and P6). Thus, the database created consists of 1120 statistical and geometrical features. For the purpose of recognition, the features of the test image are compared with the features of all the images saved in the database depending on the value of the Minimum Distance (MD). All digits (0–9) were identified with 100% accuracy. The average computational time required to recognize a numeral at any font size is 0.06879 seconds.

The increasing number of cars inside cities creates problems in traffic control. This issue can be solved by implementing a computer-based automatic system known as the Automatic Car Plate Recognition System (ACPRS). The main purpose of the current paper is to propose an automatic system to detect, extract, segment, and recognize the car plate numbers in the Kurdistan Region of Iraq (KRI). To do so, a frontal image of cars is captured and used as an input of the system. After applying the required pre-processing steps, the SURF descriptor is utilized to detect and extract the car plate from the whole input image. After segmentation of the extracted plate, an efficient projection-based technique is being exploited to describe the available digits and the city name of the registered car plate. The system is evaluated over 200 sample images, which are taken under various testing conditions. The best accuracy of the proposed system, under the controlled condition, shows the high performance and accuracy of the system which is 94%.

Automatic human face recognition is required in numerous applications. In this paper, a new approach for automatic face recognition system is developed based on 3D geometric face features. The objectives of this system are generating the real time automatic personal identification system, improvement of fast and accurate face recognition. The system developed the algorithm for computing the accurate 3D measurement of face features. It is based on the perspective projection of a triangle constructed from three nodal points extracted from two eyes and lips corners. The set of non-linear equation is established the nodal points of a triangle built by any three points in a 2D scene and their corresponding projected image points. Some mathematical concepts are used to calculate the distances and angles between feature points. The proposed system is not only recognizing the person but also extracting the personal information of a person from database.

Automatic facial expression recognition systems can provide information about our emotions and how they change over time. However, based on different statistical methods the results of automatic systems have not yet been compared. In the current paper we evaluate the emotion detection between three different commercial systems (i.e. Affectiva, Kairos and Microsoft) when detecting dynamic and spontaneous facial expressions. Even if the study was performed on a limited sample of videos, the results show significant differences between the systems for the same video and per system across comparable facial expressions. Finally, we reflect on the implications according the generalization of the results provided by automatic emotion detection.

A non-probabilistic recognizer of stochastic signals based on KLT

This paper considers automatic accent recognition system performance in relation to the specific nature of the accent data. This is of relevance to the forensic application, where an accent recogniser may have a place in casework involving various accent classification tasks with different challenges attached. The study presented here is composed of two main parts. Firstly, it examines the performance of five different automatic accent recognition systems when distinguishing between geographically-proximate accents. Using geographically-proximate accents is expected to challenge the systems by increasing the degree of similarity between the varieties we are trying to distinguish between. The second part of the study is concerned with identifying the specific phonemes which are important in a given accent recognition task, and eliminating those which are not. Depending on the varieties we are classifying, the phonemes which are most useful to the task will vary. This study therefore integrates feature selection methods into the accent recognition system shown to be the highest performer, the Y-ACCDIST-SVM system, to help to identify the most valuable speech segments and to increase accent recognition rates.

Characterizing and detecting spontaneous speech: Application to speaker role recognition

In this study, an automatic birdsong recognition system based on syllable features was developed. In this system, after syllable segmentation, three syllable features, namely mean, QI and QE, were computed from the MFCCs of each syllable aims at capturing variations in time as well as amplitude transitions of the MFCC sequences. With the advantages of the fuzzy c-mean (FCM) clustering algorithm and the linear discriminant analysis (LDA), the presented feature vector was used to construct an automatic birdsong recognition system applied to a birdsong database with 420 bird species.KeywordsMFCCsyllablelinear discriminant analysistransition matrix

In this paper, an automatic facial expression recognition system is presented. When a face image is input, two inner canthi are detected as the reference points for searching the expression features extracted from the contour and displacement of eyebrows, eyes, and mouth. Our feature extraction method can reduce the partial influence of shadows and noises. Finally, the expression features are used as the input to an Elman neural network of classifiers. The results on the JAFFE facial database show an average recognition accuracy of 84.7% in seven expressions by automatic canthi detection and 92.2% by manual canthi detection.

<span lang="EN-US">Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of a contextual bias or pre-centric understanding of a reference model with the validity of an unknown audio sample and any suspicious individual. Many researchers are continuing to develop automatic algorithms in signal processing and machine learning so that improving performance can effectively introduce the speaker’s identity, where the automatic system performs equally with the human audience. In this paper, I examine the literature about the identification of speakers by machines and humans, emphasizing the key technical speaker pattern emerging for the automatic technology in the last decade. I focus on many aspects of automatic speaker recognition (ASR) systems, including speaker-specific features, speaker models, standard assessment data sets, and performance metrics</span>