• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.167-175
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• 2020

In this paper, we propose a traffic accident detection on vehicle-mounted camera. In the proposed method, the minimum bounding box coordinates the central coordinates on the bird's eye view and motion vectors of each vehicle object, and ego-motions of the vehicle equipped with dash-cam are extracted from the dash-cam video. By using extracted 4 kinds features as the input of Bi-LSTM (bidirectional LSTM), the accident probability (score) is predicted. To investigate the effect of each input feature on the probability of an accident, we analyze the performance of the detection the case of using a single feature input and the case of using a combination of features as input, respectively. And in these two cases, different detection models are defined and used. Bi-LSTM is used as a cascade, especially when a combination of the features is used as input. The proposed method shows 76.1% precision and 75.6% recall, which is superior to our previous work.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.35-41
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• 2015

When a video surveillance system tracks a specific object, it is very important to get quickly the information of the object through fast image processing. Usually one camera surveillance system for tracking the object made results in various problems such like occlusion, image noise during the tracking process. It makes difficulties on image based moving object tracking. Therefore, to overcome the difficulties the multi video surveillance system which installed several camera within interested area and looking the same object from multi angles of view could be considered as a solution. If multi cameras are used for tracking object, it is capable of making a decision having high accuracy in more wide space. This paper proposes a method of recognizing and tracking a specific object like a car using the homography in which multi cameras are installed at the crossroad.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.95-101
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• 2007

Recently, small-animal imaging technology has been rapidly developed for longitudinal screening of laboratory animals such as mice and rats. One of newly developed imaging modalities for small animals is an x-ray micro-CT (computed tomography). We have developed two types of x-ray micro-CT systems for small animal imaging. Both systems use flat-panel x-ray detectors and micro-focus x-ray sources to obtain high spatial resolution of $10{\mu}m$. In spite of the relatively large field-of-view (FOV) of flat-panel detectors, the spatial resolution in the whole-body imaging of rats should be sacrificed down to the order of $100{\mu}m$ due to the limited number of x-ray detector pixels. Though the spatial resolution of cone-beam CTs can be improved by moving an object toward an x-ray source, the FOV should be reduced and the object size is also limited. To overcome the limitation of the object size and resolution, we introduce zoom-in micro-tomography for high-resolution imaging of a local region-of-interest (ROI) inside a large object. For zoom-in imaging, we use two kinds of projection data in combination, one from a full FOV scan of the whole object and the other from a limited FOV scan of the ROI. Both of our micro-CT systems have zoom-in micro-tomography capability. One of both is a micro-CT system with a fixed gantry mounted with an x-ray source and a detector. An imaged object is laid on a rotating table between a source and a detector. The other micro-CT system has a rotating gantry with a fixed object table, which makes whole scans without rotating an object. In this paper, we report the results of in vivo small animal study using the developed micro-CTs.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.778-781
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• 2015

The paper presents a new way of visual descriptor for deformable object retrieval on the basis of partition based description. The proposed descriptor technology partitions a given object into boundary area and interior area and extracts a descriptor from each area. The final descriptor combines these descriptors. From a given image, deformable object is segmented. The center position of the deformable object is calculated. The object is partitioned into N × N blocks on the basis of the given center position. Blocks are classified as boundary area and interior area depending on the pixels in the block. The proposed descriptor consists of extracted MPEG-7 dominant descriptors from both the boundary and interior area. The performance of proposed method is tested on a database of 1,973 handbag images constructed with view point changes. ARR (Average Retrieval Rate) is used for the retrieval accuracy of the proposed algorithm, compared with MPEG-7 dominant color descriptor.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.882-897
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• 2020

Recently, the use of immersive media contents representing Panorama and 360° video is increasing. Since the viewing angle is limited to generate the content through a general camera, image stitching is mainly used to combine images taken with multiple cameras into one image having a wide field of view. However, if the parallax between the cameras is large, parallax distortion may occur in the stitched image, which disturbs the user's content immersion, thus an image stitching overcoming parallax distortion is required. The existing Seam Optimization based image stitching method to overcome parallax distortion uses energy function or object segment information to reflect the location information of objects, but the initial seam generation location, background information, performance of the object detector, and placement of objects may limit application. Therefore, in this paper, we propose an image stitching method that can overcome the limitations of the existing method by adding a weight value set differently according to the type of object to the energy value using object detection based on deep learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.279-282
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• 2017

Kinect depth sensor, a depth camera developed by Microsoft as a natural user interface for game appeared as a very useful tool in computer vision field. In this paper, due to kinect's depth sensor and its high frame rate, we developed a distance measurement system using Kinect camera to test it for unmanned vehicles which need vision systems to perceive the surrounding environment like human do in order to detect objects in their path. Therefore, kinect depth sensor is used to detect objects in its field of view and enhance the distance measurement system from objects to the vision sensor. Detected object is identified in accuracy way to determine if it is a real object or a pixel nose to reduce the processing time by ignoring pixels which are not a part of a real object. Using depth segmentation techniques along with Open CV library for image processing, we can identify present objects within Kinect camera's field of view and measure the distance from them to the sensor. Tests show promising results that this system can be used as well for autonomous vehicles equipped with low-cost range sensor, Kinect camera, for further processing depending on the application type when they reach a certain distance far from detected objects.

• Korean Journal of Computational Design and Engineering
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• v.12 no.2
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• pp.137-145
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• 2007

This paper concerns about residential environment analysis program implementation for design and analysis on public housing complexes such that view and daylight analysis processes are automated and integrated into existing design routine to achieve better design efficiency. Considering the architectural design trends this paper chooses ArchiCAD as a platform for a CAD system, which contains the concepts such as integrated object-oriented CAD, virtual building and BIM. Residential environment analysis system consists of three components. The first component is the 3D modeling part defining 3D form information for external geographic contour models, site models and interior/exterior of apartment buildings. The second is the parametric library part handling the design parameters for view and daylight analysis. The last is the user interface for the input/output and integration of data for the environment analysis. Daylight analysis shows rendered images as well as results of daylight reports and grades per time and performs the calculations for floor shadow. It separates the site-only analysis from the analysis of site and exterior environmental parameters. View analysis considers horizontal and vertical view angles to produce view image from each unit and uses the bitmap analysis method to determine opening ratio, scenery ratio and void ratio. We could expect better performance and precision from this residential environment analysis system than the existing 2D drawing based view and daylight analysis methods and overcome the existing one-way flow of design information from 3D form to analysis reports so that site design modifications are automatically reflected on analysis results. Each part is developed in a module so that further integration and extension into other related estimation and construction management systems are made possible.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.228-237
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• 2013

In this paper, we propose a normalized cross correlation(NCC)-based multiview background subtraction method which is robust when an object and background have similar color. When the background of the capturing environment is not artificially composed, the regions in the background images which would be occluded by an object tends to have difference colors. The colors of those regions, however, becomes similar when an object enters the capturing environment. Based on this assumption, this paper proposes a concept of GoNCC(Graph of Normalized Cross Correlation). GoNCC is the distribution of NCC between a pixel in an image and pixels related by epipolar constraints with the pixel. The proposed multiview background subtraction method is performed by comparing GoNCC of the current images with the background images. To reduce computational complexity, we perform multiview background subtraction only to the pixels undetermined by single view background subtraction. Experimental results show that the proposed method is more robust to color similarity between an object and background than a single-view background subtraction method and a previous multiview background subtraction method.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.261-274
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• 1997

Most of the data sets to which the conventional discriminant rules have been applied contain only those which belong to one and only one class among the classes of interest. However the extension of the bivalence to multivlaence like Fuzzy concepts strongly influence the traditional view that an object must belong to only class. Thus the goal of this paper is to develop new discriminant rules which can handle the data each object of which may belong to moer than two classes with certain degrees of belongings. A calibration model is used for the relationship between the feature vector of an object and the degree of belongings and a Bayesian inference is made with the Metropolis algorithm on the degree of belongings when a feature vector of an object whose membership is unknown is given. An evalution criterion is suggested for the rules developed in this paper and comparision study is carried using two training data sets.

• Journal of Broadcast Engineering
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• v.14 no.1
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• pp.81-84
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• 2009

This paper explores the possibility of describing objects from the change in the shape according to the change in viewpoint. Specifically, we sample the shapes from various viewpoints of a 3-D model, and apply dimension reduction by locally linear embedding. A low dimensional distribution of points are constructed, and characteristics of the object are described from this distribution. Also, we propose two 3-D retrieval methods by applying the iterative closest point algorithm, and by applying Fourier transform and measuring similarity by modified Housdorff distance, and present experimental results. The proposed method shows that the change of shape according to the change in viewpoint can describe the characteristics of an object.