• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06c
• /
• pp.405-408
• /
• 2011

In this paper, we propose a robot remote control system based on mouth tracking. The main idea behind the work is to help disabled people who cannot operate a joystick or keyboard to control a robot with their hands. The mouth detection method in this paper is mainly based on the Adaboost feature detection approach. By using the proposed new Haar-like features for detecting the corner of mouth, the speed and accuracy of detection are improved. Combined with the Kalman filter, a continuous and accurate mouth tracking has been achieved. Meanwhile, the gripping commands of the robot manipulator were also achieved by the recognition of the user.s mouth shape, such as 'pout mouth' or 'grin mouth'. To assess the validity of the method, a mouth detection experiment and a robot cargo transport experiment were applied. The result indicated that the system can realize a quick and accurate mouse tracking; and the operation of the robot worked successfully in moving and bringing back items.

• Journal of Veterinary Science
• /
• v.23 no.5
• /
• pp.32.1-32.11
• /
• 2022

Background: Classical swine fever (CSF) is a severe infectious disease of pigs that causes significant economic losses to the swine industry. Objectives: This study developed a solid-phase blocking enzyme-linked immunosorbent assay (spbELISA) method for the specific detection of antibodies against the CSF virus (CSFV) in porcine serum samples. Methods: A spbELISA method was developed based on the recombinant E2 expressed in Escherichia coli. The specificity of this established spbELISA method was evaluated using reference serum samples positive for antibodies against other common infectious diseases. The stability and sensitivity were evaluated using an accelerated thermostability test. Results: The spbELISA successfully detected the antibody levels in swine vaccinated with the C-strain of CSFV. In addition, the detection ability of spbELISA for CSFV antibodies was compared with that of other commercial ELISA kits and validated using an indirect immunofluorescence assay. The results suggested that the spbELISA provides an alternative, stable, and rapid serological detection method suitable for the large-scale screening of CSFV serum antibodies. Conclusions: The spbELISA has practical applications in assessing the vaccination status of large pig herds.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.3
• /
• pp.89-97
• /
• 2014

In this paper, we propose real-time program that measure open-close frequency of mouth and eyes to detect drowsiness of a driver. This program detects a face to the CCD camera image using OpenCV library. Then that extracts each area using CDF for eye detection and Active Contour for mouth detection based on detected face. This system measures each frequency of Open-Close using extracted area data of eyes and mouth. We propose foundation technique how to perform sleepiness decision of users based on measurement data.

• Korean Journal of Veterinary Service
• /
• v.35 no.4
• /
• pp.255-261
• /
• 2012

Foot and mouth disease (FMD) is highly infectious disease of cloven-hoofed animals, particularly cattle, sheep, pigs and goats. Last outbreak reported in November, 2010 induced the enormous social and economical impacts. Culling of infected animals, movement control, and vaccination are the major control measures of FMD. The aim of this study was to detection foot-and-mouth disease virus (FMDV) in the soil and leachate from modeling burial for pig carcass as measured by real-time reverse transcriptase polymerase chain reaction (RT-PCR). FMDV and Coxsakievirus B1 (CVB1) were detected in soil by week 16 and Coxsakievirus B1 (CVB1) by weeks 12, respectively. FMDV and CVB1 also detected by weeks 8 in the leachate. Results from this study provides an evidence that FMDV could be inactivated for safe of pig carcasses infected with FMDV within 4 month in the carcass burial site.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.425-428
• /
• 2009

We have researched and developed the caricature generation system PICASSO. PICASSO outputs the deformed facial caricature by comparing input face with prepared mean face. We specialized it as PICASSO-2 for exhibiting a robot at Aichi EXPO2005. This robot enforced by PICASSO-2 drew a facial caricature on the shrimp rice cracker with the laser pen. We have been recently exhibiting another revised robot characterized by a brush drawing. This system takes a couple of facial images with CCD camera, extracts the facial features from the images, and generates the facial caricature in real time. We experimentally evaluated the performance of the caricatures using a lot of data taken in Aichi EXPO2005. As a result it was obvious that this system were not sufficient in accuracy of eyebrow region extraction and mouth detection. In this paper, we propose the improved methods for eyebrow region extraction and mouth detection.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.6B
• /
• pp.1041-1049
• /
• 2000

A face recognition algorithm for the user identification procedure of automatic teller machine(ATM), as an application of the still image processing techniques is proposed in this paper. In the proposed algorithm, face recognition techniques, especially, face region detection, eye and mouth detection schemes, which can distinguish abnormal faces from normal faces, are proposed. We define normal face, which is acceptable, as a face without sunglasses or a mask, and abnormal face, which is non-acceptable, as that wearing both, or either one of them. The proposed face recognition algorithm is composed of three stages: the face region detection stage, the preprocessing stage for facial feature detection and the eye and mouth detection stage. Experimental results show that the proposed algorithm can distinguish abnormal faces from normal faces accurately from restrictive sample images.

• Journal of Korea Multimedia Society
• /
• v.10 no.12
• /
• pp.1591-1600
• /
• 2007

Camera position information from 2D face image is very important for that make the virtual 3D face model synchronize to the real face at view point, and it is also very important for any other uses such as: human computer interface (face mouth), automatic camera control etc. We present an algorithm to detect human face region and mouth, based on special color features of face and mouth in $YC_bC_r$ color space. The algorithm constructs a mouth feature image based on $C_b\;and\;C_r$ values, and use pattern method to detect the mouth position. And then we use the geometrical relationship between mouth position information and face side boundary information to determine the camera position. Experimental results demonstrate the validity of the proposed algorithm and the Correct Determination Rate is accredited for applying it into practice.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.1
• /
• pp.35-44
• /
• 2010

In this paper, we propose a method about producing skin color model adaptively in input image and face detection. The principle process which we proposed is finding eyes candidates by applying the eye features to neural network, and then using the around color to find the distribution of color value. There will be a verification process that producing face region by using color value distribution which is detected as skin region and find mouth candidate in corresponding face region; if eye candidate and mouth candidate's connection structure is similar with face structure, then it can be judged as a face. Because this method can detect skin region adaptively by finding eyes, we solve the rate of false positive about the distorted skin color which is used by existing face detection methods. The experiment was performed about detecting the eye, the skin, the mouth and the face individually. The results revealed that the proposed technique is better than the traditional techniques.

• Journal of the Korean Society of Industry Convergence
• /
• v.24 no.6_2
• /
• pp.707-713
• /
• 2021

Symptoms of foot-and-mouth disease include fever and drooling a lot around the hoof, blisters in the mouth, poor appetite, blisters around the hoof, and blisters around the hoof. Research is underway on smart barns that remotely manage these symptoms through cameras. Visible light cameras can measure the condition of livestock such as blisters, but cannot measure body temperature. On the other hand, infrared thermal imaging cameras can measure body temperature, but it is difficult to measure the condition of livestock. In this paper, we propose an object detection system using deep learning-based livestock detection using visible and infrared thermal imaging composite camera modules for preemptive response

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.783-788
• /
• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.