• Journal of Sensor Science and Technology
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• v.13 no.2
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• pp.144-151
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• 2004

In this paper, we propose an active stereo surveillance system with human-like convergence function. The proposed system uses a bottom-up saliency map model with the human-like selective attention visual function to select an interesting region in each camera. and this system compares the landmarks whether the selective region in each camera finds a same region. If the left and right cameras successfully find a same landmarks, the implemented vision system focuses on the landmark. Using the motor encoder information, we can automatically obtain the depth information and resultantly construct a depth map using the depth information. Computer simulation and experimental results show that the proposed convergence method is very effective to implement the active stereo surveillance system.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2596-2598
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• 2000

In this paper, real-time vision-eyed control system is proposed that combines the information handling capability of computer with the real-time image processing capability of CCD camera, and control effectively real system in the limited environment. The control system is applied to inverted pendulum system, namely, bench marking system. Feasibility of the system is shown in a viewpoint of simulations and experiments.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1243-1253
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• 1993

Visual features of a mushroom(Lentinus Edodes L) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading looks simple, a decision making undereath the simple action comes form the results of the complex neural processing of the visual image. And processing details involved in the visual recognition of the human brain has not been fully investigated yet. Recently, however, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, a research of the neuro-net based human like information processing toward the agricultural product and processing are widely open and promising. In this pape , neuro-net based grading and sorting system was developed for a mushroom . A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features and their corresponding grades were used as input/output pairs for training the neural network and the trained results of the network were presented . The computer vision system used is composed of the IBM PC compatible 386DX, ITEX PFG frame grabber, B/W CCD camera , VGA color graphic monitor , and image output RGB monitor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1805-1810
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• 2015

Increasing of depth data accessibility, depth data is used in many researches. Motion recognition of computer vision also widely use depth image. More accuracy motion recognition system needs more stable depth data. But depth sensor has a noise. This noise affect accuracy of the motion recognition system, we should noise suppression. In this paper, we propose using spatial domain and temporal domain stabilization for depth image and makes it hardware IP. We adapted our hardware to floor removing algorithm and verification its effect. we did realtime verification using FPGA and APU. Designed hardware has maximum frequency 202.184MHz.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.1-7
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• 2005

This paper describes the system for detecting vehicles in the rear and rear-side that access between sidewalk and bus stopped to city road at day by computer vision-based method. This system informs appearance of vehicles to bus driver and passenger for the safety of a bus passenger getting off. The camera mounted on the top portion of the bus exit door gets the rear and rear-side image of the bus whenever a bus stops at the stop. The system sets search area between bus and sidewalk from this image and detects a vehicle by using change of image and sobel filtering in this area. From a central point of the vehicle detected, we can find out the distance, speed and direction by its location, width and length. It alarms the driver and passengers when it's judged that dangerous situation for the passenger getting off happens. This experiment results in a detection rate more than 87% in driving by bus on the road.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.15-20
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• 2020

Software-related training can be considered essential in situations where software is an important factor in national innovation, growth and value creation. As one of the implementation methods for engineering education, various education through virtual simulations that can educate difficult situations in a similar environment are being conducted. Recently, the construction of smart factories at production and manufacturing sites is spreading, and product inspections using vision systems are being conducted. However, it has many difficulties due to lack of operation technology of vision system, but it requires a lot of cost to construct the system for education of vision system. In this paper, provide an educational virtual simulation model that integrates computer and physics engine camera functions and can extract and transmit video. It is possible to generate an image of 30Hz or more at an average of 35.4FPS of the experimental results of the proposed model, and it is possible to send and receive images in a time of 22.7ms, which can be utilized in an educational virtual simulation educational environment.

• Journal of Biosystems Engineering
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• v.35 no.6
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• pp.443-449
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• 2010

A machine vision-based instrument to measure a droplet size spectrum of a spray nozzle was developed and tested to evaluate its accuracy on measuring spray droplet sizes and classifying nozzle sizes. The instrument consisted of a machine vision, light emitting diode (LED) illumination and a desktop computer. The illumination and machine vision were controlled by the computer through a C++ program. The program controlled the machine vision to capture droplet images under controlled illumination, and processed the droplet images to characterize the droplet size distribution of a spray nozzle. An image processing algorithm was developed to improve the accuracy of the system by eliminating random noise and out-of-focus droplets in droplet images while measuring droplet sizes. The instrument measured sizes of the three different balls (254.0, 497.8 and $793.8\;{\mu}m$) and the measurement ranges were $241.2-273.6\;{\mu}m$, $492.9-529.6\;{\mu}m$ and $800.8-824.1\;{\mu}m$ for 254.0-, 497.84- and $793.75-\;{\mu}m$ balls, respectively. Error of the measured droplet mean was less than 3.0 %. Droplet statistics, $D_{V0.1}$, $D_{V0.5}$ and $D_{V0.9}$, of a reference nozzle set were measured, and droplet size spectra of five spray nozzles covering from very fine to extremely coarse were measured to classify spray nozzle sizes. Ninety percent of the classification results of the instrument agreed with manufacturer's classification. A comparison study was carried out between developed and commercial instruments, and measurement results of the developed instrument were within 20 % of commercial instrument results.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.55-67
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• 2014

Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image's Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images' topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1194-1198
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• 2005

A computer vision system was designed and validated to recognize an individual Holstein cattle by processing images of their body patterns. This system involves image capture, image pre-processing, algorithm processing, and an artificial neural network recognition algorithm. Optimum management of individuals is one of the most important factors in keeping cattle healthy and productive. In this study, an image-processing system was used to recognize individual Holstein cattle by identifying the body-pattern images captured by a charge-coupled device (CCD). A recognition system was developed and applied to acquire images of 49 cattles. The pixel values of the body images were transformed into input data comprising binary signals for the neural network. Images of the 49 cattle were analyzed to learn input layer elements, and ten cattles were used to verify the output layer elements in the neural network by using an individual recognition program. The system proved to be reliable for the individual recognition of cattles in natural light.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.106-110
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• 2000

An integrated on-line inspection system was constructed with seven cameras, half mirrors to split images. 720 nm and 970 nm band pass filters, illumination chamber having several tungsten-halogen lamps, one main computer, one color frame grabber, two 4-channel multiplexors, and flat plate conveyer, etc. A total of seven images, that is, one color image form the top of an apple and two B/W images from each side (top, right and left) could be captured and displayed on a computer monitor through the multiplexor. One of the two B/W images captured from each side is 720nm filtered image and the other is 970 nm. With this system an on-line grading software was developed to evaluate appearance quality. On-line test results with Fuji apples that were manually fed on the conveyer showed that grading accuracies of the color, defect and shape were 95.3%, 86% and 88.6%, respectively. Grading time was 0.35 second per apple on an average. Therefore, this on-line grading system could be used for inspection of the final products produced from an apple sorting system.