Automatic Detection of the Access-restricted Sections based on Geographic Characteristics of At-grade Intersections in High Definition Maps

Lee Junhyung;  Kim Youngjin;  Kang Seungmin;  Chi-Hyun SHIN

doi:10.15793/kspr.2019.103..002

Automatic Detection of the Access-restricted Sections based on Geographic Characteristics of At-grade Intersections in High Definition Maps

The Korea Spatial Planning Review ◽

10.15793/kspr.2019.103..002 ◽

2019 ◽

Vol 103 (null) ◽

pp. 27-39

Author(s):

Lee Junhyung ◽

Kim Youngjin ◽

Kang Seungmin ◽

Chi-Hyun SHIN

Keyword(s):

Automatic Detection ◽

High Definition

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AUTOMATIC DETECTION AND RECOGNITION OF 3D MANUAL GESTURES FOR HUMAN-MACHINE INTERACTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w12-179-2019 ◽

2019 ◽

Vol XLII-2/W12 ◽

pp. 179-183 ◽

Cited By ~ 4

Author(s):

D. Ryumin ◽

I. Kagirov ◽

D. Ivanko ◽

A. Axyonov ◽

A. A. Karpov

Keyword(s):

Automatic Detection ◽

Logical Structure ◽

High Definition ◽

Human Machine Interaction ◽

Convolutional Network ◽

Sign Languages ◽

Distinctive Features ◽

The Face ◽

Machine Interaction ◽

Automatic Systems

<p><strong>Abstract.</strong> In this paper, we propose an approach to detect and recognize 3D one-handed gestures for human-machine interaction. The logical structure of the modules of the system for recording a gestural database is described. The logical structure of the database of 3D gestures is presented. Examples of frames showing gestures in the format of Full High Definition, in the map depth mode and in the infrared illustrated. Models of a deep convolutional network for detecting faces and hand shapes are described. The results of automatic detection of the area with the face and the shape of the hand are given. Identified the distinctive features of the gesture at a certain point in time. The process of recognizing 3D one-handed gestures is described. Due to its versatility, this method can be used in tasks of biometrics, computer vision, machine learning, automatic systems of face recognition, sign languages.</p>

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Routine Digital Processing of Microscope Images

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100181506 ◽

1990 ◽

Vol 48 (1) ◽

pp. 550-551

Author(s):

E. Wisse ◽

A. Geerts ◽

R.B. De Zanger

Keyword(s):

Operating System ◽

Digital Processing ◽

Hard Disks ◽

The Sun ◽

Tape Drive ◽

High Definition ◽

Fluorescent Microscope ◽

Microscope Images ◽

Helical Scan ◽

Ethernet Connection

The slowscan and TV signal of the Philips SEM 505 and the signal of a TV camera attached to a Leitz fluorescent microscope, were digitized by the data acquisition processor of a Masscomp 5520S computer, which is based on a 16.7 MHz 68020 CPU with 10 Mb RAM memory, a graphics processor with two frame buffers for images with 8 bit / 256 grey values, a high definition (HD) monitor (910 × 1150), two hard disks (70 and 663 Mb) and a 60 Mb tape drive. The system is equipped with Imaging Technology video digitizing boards: analog I/O, an ALU, and two memory mapped frame buffers for TV images of the IP 512 series. The Masscomp computer has an ethernet connection to other computers, such as a Vax PDP 11/785, and a Sun 368i with a 327 Mb hard disk and a SCSI interface to an Exabyte 2.3 Gb helical scan tape drive. The operating system for these computers is based on different versions of Unix, such as RTU 4.1 (including NFS) on the acquisition computer, bsd 4.3 for the Vax, and Sun OS 4.0.1 for the Sun (with NFS).

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Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

Perspectives of the ASHA Special Interest Groups ◽

10.1044/2019_pers-sig11-2018-0020 ◽

2019 ◽

Vol 4 (2) ◽

pp. 356-362

Author(s):

Jennifer W. Means ◽

Casey McCaffrey

Keyword(s):

Graduate Students ◽

Data Collection ◽

Real Time ◽

Clinical Supervision ◽

Video Recording ◽

Advanced Technology ◽

High Definition ◽

Recording Technology ◽

Self Confidence ◽

Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

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