• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.388-394
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• 2014

In this paper, we propose a three-dimensional (3D) image correlator by use of computational integral imaging reconstruction based on the modified convolution property of periodic functions (CPPF) for recognition of partially occluded objects. In the proposed correlator, elemental images of the reference and target objects are picked up by a lenslet array, and subsequently are transformed to a sub-image array which contains different perspectives according to the viewing direction. The modified version of the CPPF is applied to the sub-images. This enables us to produce the plane sub-image arrays without the magnification and superimposition processes used in the conventional methods. With the modified CPPF and the sub-image arrays, we reconstruct the reference and target plane sub-image arrays according to the reconstruction plane. 3D object recognition is performed through cross-correlations between the reference and the target plane sub-image arrays. To show the feasibility of the proposed method, some preliminary experiments on the target objects are carried out and the results are presented. Experimental results reveal that the use of plane sub-image arrays enables us to improve the correlation performance, compared to the conventional method using the computational integral imaging reconstruction algorithm.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1061-1062
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• 2008

Rodents demonstrate an outstanding capability for tactile perceptions using their whiskers. The mechanoreceptors in the whisker follicles are responsive to the deflections or vibrations of the whisker beams. It is believed that the sensor processing can determine the location of an object in touch, that is, the angular position and direction of the object. We designed artificial whiskers modelling the real whiskers and tested tactile localization. The robotic system needs to adjust its position against an object to help the shape recognition. We show a robotic adjustment of position based on tactile localization. The behaviour uses deflection curves of the whisker sensors for every sweep of whiskers and estimates the location of a target object.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.562-568
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• 2016

In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.155-157
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• 2012

In this paper, we propose a nonlinear 3D image correlator using computational reconstruction of 3D images based on integral imaging. In the proposed method, the elemental images for reference 3D object and target 3D object are recorded through the lens array. The recorded elemental images are reconstructed as reference plane image and target plane images using the computational integral imaging reconstruction algorithm and the nonolinear correlation between them is performed for object recognition. To show the usefulness of the proposed method, the preliminary experiments are carried out and the experimental results are presented compared with the conventional results.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.79-88
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• 1999

In this paper we address the tracking method which tracks only target object in image sequence including moving object. We use a contour tracking algorithm based on intensity and motion boundaries. The motion of the moving object contour in the image is assumed to be well describable by an affine motion model with a translation, a change in scale and a rotation. The moving object contour is represented by B-spline, the position and motion of which is estimated along the image sequence. we use pattern recognition to identify target object. In order to use linear Kalman Filters we decompose the estimation process into two filters. One is estimating the affine motion parameters and the other the shape of moving object contour. In some experiments with dial plate we show that this method enables us to obtain the robust motion estimates and tracking trajectories even in case of including obstructive object.

• Smart Media Journal
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• v.4 no.1
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• pp.16-24
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• 2015

In this paper, we propose a method for multi-touch tracking using MRF-based particle filters and gesture likelihood estimation Each touch (of one finger) is considered to be one object. One of frequently occurring issues is the hijacking problem which means that an object tracker can be hijacked by neighboring object. If a predicted particle is close to an adjacent object then the particle's weight should be lowered by analysing the influence of neighboring objects for avoiding hijacking problem. We define a penalty function to lower the weights of those particles. MRF is a graph representation where a node is the location of a target object and an edge describes the adjacent relation of target object. It is easy to utilize MRF as data structure of adjacent objects. Moreover, since MRF graph representation is helpful to analyze multi-touch gestures, we describe how to define gesture likelihoods based on MRF. The experimental results show that the proposed method can avoid the occurrence of hijacking problems and is able to estimate gesture likelihoods with high accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.670-678
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• 2014

This paper presents a method to extract objects in radiographic images where all the allowable combinations of segmented regions are compared to a target object using Fourier descriptor. In the object extraction for usual images, a main problem is occlusion. In radiographic images, there is an advantage that the shape of an object is not occluded by other objects. It is because radiographic images represent the amount of radiation penetrated through objects. Considering the property of no occlusion in radiographic images, the shape based descriptors can be very effective to find objects. After all, the proposed object extraction method consists of three steps of segmenting regions, finding all the combinations of the segmented regions, and matching the combinations to the shape of the target object. In finding the combinations, we reduce a lot of computations to remove unnecessary combinations before matching. In matching, we employ Fourier descriptor so that the proposed method is rotation and shift invariant. Additionally, shape normalization is adopted to be scale invariant. By experiments, we verify that the proposed method works well in extracting objects.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.69-75
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• 2014

Existing People Counting System using a single overhead mounted camera has limitation in object recognition and counting in various environments. Those limitations are attributable to overlapping, occlusion and external factors, such as over-sized belongings and dramatic light change. Thus, this paper proposes the new concept of People Counting System by Facial Age Group using two depth cameras, at overhead and frontal viewpoints, in order to improve object recognition accuracy and robust people counting to external factors. The proposed system is counting the pedestrians by five process such as overhead image processing, frontal image processing, identical object recognition, facial age group classification and in-coming/out-going counting. The proposed system developed by C++, OpenCV and Kinect SDK, and it target group of 40 people(10 people by each age group) was setup for People Counting and Facial Age Group classification performance evaluation. The experimental results indicated approximately 98% accuracy in People Counting and 74.23% accuracy in the Facial Age Group classification.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.43-52
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• 2014

In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.