3차원 영상 객체 휴먼팩터 알고리즘 측정에 관한 연구

최병관,Choi, Byungkwan 디지털산업정보학회 2018 디지털산업정보학회논문지 Vol.14 No.2

The 4th industrial revolution, digital image technology has developed beyond the limit of multimedia industry to advanced IT fusion and composite industry. Particularly, application technology related to HCI element algorithm in 3D image object recognition field is actively developed. 3D image object recognition technology evolved into intelligent image sensing and recognition technology through 3D modeling. In particular, image recognition technology has been actively studied in image processing using object recognition recognition processing, face recognition, object recognition, and 3D object recognition. In this paper, we propose a research method of human factor 3D image recognition technology applying human factor algorithm for 3D object recognition. 1. Methods of 3D object recognition using 3D modeling, image system analysis, design and human cognitive technology analysis 2. We propose a 3D object recognition parameter estimation method using FACS algorithm and optimal object recognition measurement method. In this paper, we propose a method to effectively evaluate psychological research techniques using 3D image objects. We studied the 3D 3D recognition and applied the result to the object recognition element to extract and study the characteristic points of the recognition technology.

다면기법 SPFACS 영상객체를 이용한 AAM 알고리즘 적용 미소검출 설계 분석

최병관,Choi, Byungkwan 디지털산업정보학회 2015 디지털산업정보학회논문지 Vol.11 No.3

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.

영상객체 spFACS ASM 알고리즘을 적용한 얼굴인식에 관한 연구

최병관,Choi, Byungkwan 디지털산업정보학회 2016 디지털산업정보학회논문지 Vol.12 No.4

Digital imaging technology has developed into a state-of-the-art IT convergence, composite industry beyond the limits of the multimedia industry, especially in the field of smart object recognition, face - Application developed various techniques have been actively studied in conjunction with the phone. Recently, face recognition technology through the object recognition technology and evolved into intelligent video detection recognition technology, image recognition technology object detection recognition process applies to skills through is applied to the IP camera, the image object recognition technology with face recognition and active research have. In this paper, we first propose the necessary technical elements of the human factor technology trends and look at the human object recognition based spFACS (Smile Progress Facial Action Coding System) for detecting smiles study plan of the image recognition technology recognizes objects. Study scheme 1). ASM algorithm. By suggesting ways to effectively evaluate psychological research skills through the image object 2). By applying the result via the face recognition object to the tooth area it is detected in accordance with the recognized facial expression recognition of a person demonstrated the effect of extracting the feature points.

Object Recognition Algorithm with Partial Information

Suk Won Yoo 국제문화기술진흥원 2019 International Journal of Advanced Culture Technolo Vol.7 No.4

Due to the development of video and optical technology today, video equipments are being used in a variety of fields such as identification, security maintenance, and factory automation systems that generate products. In this paper, we investigate an algorithm that effectively recognizes an experimental object in an input image with a partial problem due to the mechanical problem of the input imaging device. The object recognition algorithm proposed in this paper moves and rotates the vertices constituting the outline of the experimental object to the positions of the respective vertices constituting the outline of the DB model. Then, the discordance values between the moved and rotated experimental object and the corresponding DB model are calculated, and the minimum discordance value is selected. This minimum value is the final discordance value between the experimental object and the corresponding DB model, and the DB model with the minimum discordance value is selected as the recognition result for the experimental object. The proposed object recognition method obtains satisfactory recognition results using only partial information of the experimental object.

샴 네트워크 기반의 적은 데이터셋을 이용한 유사한 형태의 특정 객체 인식 연구

곽정훈,양견모,이종일,김민규,서갑호 제어·로봇·시스템학회 2022 제어·로봇·시스템학회 논문지 Vol.28 No.9

Target object recognition by a mobile robot is based on deep learning. To train a deep learning model to differentiate between target and non-target objects, a sufficiently large dataset that can extract features for classifying objects is required. In a general working environment, securing a sufficiently large dataset is difficult because the number of people designated as workers is constantly changing. Classifying the shapes of objects that are similar requires larger datasets than classifying the shapes of objects that are not similar. Therefore, a method for recognizing target objects with similar shapes using a small dataset is required. This paper proposes a Siamese-network–based target object recognition method for recognizing objects with similar shapes based on a small dataset. A trained Siamese network is used to recognize whether the input object is the target object based on its similarity to the target object. The results of target object recognition using the proposed method were experimentally analyzed. Further, ResNet-50 was used to evaluate the performance of the proposed method. Our findings show that the proposed method recognized objects with a difference of approximately 6% using a small dataset, indicating its higher efficiency than the classification-based object recognition method. .

Automatic Object Recognitions by Assessing Weight Strengths

Ahn, Kyung-Hee 인제대학교 기초과학연구소 2000 자연과학 Vol.4 No.-

Computer image내에 목적물들의 인지는 다양한 Algorithm을 이용하여 해당 목적물들을 추출하여 인지한다. Computer image 들은 정보량과 image pattern이 무수히 많고 image 특성상 인지과정이 복잡하고 인지가 불가능한 경우가 잇다. 본 논문에서는 image weight strength를 이용한 algorithm을 개발하여 대상 목적물의 형태를 추출하여 수량화 함으로서 인지시간을 줄이고 인지율을 높이는 새로운 자동 목적물 인지시스템을 소개한다. The paper introduces a new recognition system which assesses the weight strengths of objects in the input images. The part strengths of an object could be used to recognize the object. The conventional approaches have many technical difficulties for recognizing patterns or objects. The proposed technique has been made to reduce the difficulties and enable the development of a fast and reliable object recognition system. The automatic object recognition with the new approach has been developed to make simple recognitions.

Object Detection System for the Blind with Voice Command and Guidance

Sanghyeon Lee,Moonsik Kang 대한전자공학회 2019 IEIE Transactions on Smart Processing & Computing Vol.8 No.5

As object recognition technology has developed recently, various technologies have been applied to autonomous vehicles, robots, and industrial facilities. However, the benefits of these technologies are not reaching the visually impaired, who need it the most. In this paper, we proposed an object detection system for the blind using deep learning technologies. We use voice recognition technology in order to know what objects a blind person wants, and then to find the objects via object recognition. Furthermore, a voice guidance technique is used to inform sightimpaired persons as to the location of objects. The object recognition deep learning model utilizes the Single Shot Multibox Detector (SSD) neural network architecture, and voice recognition is designed through speech-to-text (STT) technology. In addition, a voice announcement is synthesized using text-to-speech (TTS) to make it easier for the blind to get information about objects. The control system is based on the Arduino microprocessor. As a result, we implement an efficient object-detection system that helps the blind find objects in a specific space without help from others, and the system is analyzed through experiments to verify performance.

Object Recognition Algorithm with Partial Information

Yoo, Suk Won The International Promotion Agency of Culture Tech 2019 International Journal of Advanced Culture Technolo Vol.7 No.4

Due to the development of video and optical technology today, video equipments are being used in a variety of fields such as identification, security maintenance, and factory automation systems that generate products. In this paper, we investigate an algorithm that effectively recognizes an experimental object in an input image with a partial problem due to the mechanical problem of the input imaging device. The object recognition algorithm proposed in this paper moves and rotates the vertices constituting the outline of the experimental object to the positions of the respective vertices constituting the outline of the DB model. Then, the discordance values between the moved and rotated experimental object and the corresponding DB model are calculated, and the minimum discordance value is selected. This minimum value is the final discordance value between the experimental object and the corresponding DB model, and the DB model with the minimum discordance value is selected as the recognition result for the experimental object. The proposed object recognition method obtains satisfactory recognition results using only partial information of the experimental object.

Object Recognition Algorithm with Partial Information

류석원 국제문화기술진흥원 2019 International Journal of Advanced Culture Technolo Vol.7 No.4

Due to the development of video and optical technology today, video equipments are being used in a variety of fields such as identification, security maintenance, and factory automation systems that generate products. In this paper, we investigate an algorithm that effectively recognizes an experimental object in an input image with a partial problem due to the mechanical problem of the input imaging device. The object recognition algorithm proposed in this paper moves and rotates the vertices constituting the outline of the experimental object to the positions of the respective vertices constituting the outline of the DB model. Then, the discordance values between the moved and rotated experimental object and the corresponding DB model are calculated, and the minimum discordance value is selected. This minimum value is the final discordance value between the experimental object and the corresponding DB model, and the DB model with the minimum discordance value is selected as the recognition result for the experimental object. The proposed object recognition method obtains satisfactory recognition results using only partial information of the experimental object

Object Recognition using Comparison of External Boundary

Suk Won Yoo 국제문화기술진흥원 2019 International Journal of Advanced Culture Technolo Vol.7 No.3

As the 4th industry has been widely distributed, there is a need for a process of real-time image recognition in various fields such as identification of company employees, security maintenance, and development of military weapons. Therefore, in this paper, we will propose an algorithm that effectively recognizes a test object by comparing it with the DB model. The proposed object recognition system first expresses the outline of the test object as a set of vertices with the distances of predefined length or more. Then, the degree of matching of the structures of the two objects is calculated by examining the distances to the outline of the DB model from the vertices constituting the test object. Because the proposed recognition algorithm uses the outline of the object, the recognition process is easy to understand, simple to implement, and a satisfactory recognition result is obtained.