• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.108-115
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• 2011

This paper describes a new sensor system for mobile robot motion estimation using stereo infrared light sources and a camera. Visibility is being applied to robotic obstacle avoidance path planning and localization. Using simple visibility computation, the environment is partitioned into many visibility sectors. Based on the recognized edges, the sector a robot belongs to is identified and this greatly reduces the search area for localization. Geometric modeling of the vision system enables the estimation of the characteristic pixel position with respect to the robot movement. Finite difference analysis is used for incremental movement and the error sources are investigated. With two characteristic points in the image such as vertices, the robot position and orientation are successfully estimated.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.17 no.2
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• pp.77-89
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• 2013

Objectives Tongue diagnosis is an important diagnostic method in traditional Eastern medicine, and it has a high potential to be used in the future healthcare because of easy, quick, and non-contact measuring features. Recently, research and development efforts on computerized tongue diagnosis systems (CTDS) have been active that led to the technical advancements in the field of photographing techniques, image extraction and classification algorithms. In this study, we analyzed the trends in the CTDS patents. Using the WIPS search engine (www.wipsglobal.com), quantitative and qualitative patent analyses were performed through Korea, China, Japan, U.S.A and Europe. Methods For a systematic search and data analysis, we defined patent categories based on the application area and technical details. By applying thus-obtained categorical key words, we obtained 360 relevant patents on photographing techniques, image extraction and classification algorithms for the purpose of diagnosis or security. Results As a result, companies related to image acquisition, medical imaging and mobile devices and research groups of universities in East Asia were major patent applicants. In all the five countries, the number of patents have been increasing since 1980. In particular, technology related to color correction and image segmentation were most actively patented categories, and expected to continue a high application rate.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.41.3-41
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• 2002

Motion estimation using Full-Search(FS) and Block-Matching Algorithm(BMA) is often used in the case of moving object tracking by vision sensors. However these methods often miss the real-time vision data because these schemes suffer the heavy computational load. When the image size of moving object is changed in an image frame according to the distance between the camera of mobile robot and the moving object, the tracking performance of a moving object may decline with these methods because of the shortage of active handling. In this paper, the variable-representative block that can reduce a lot of data computations, is defined and optimized by changing the size of representative block accor...

• Journal of the Korean Society of Costume
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• v.62 no.4
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• pp.58-68
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• 2012

Personal image concerns a person's talent, expertise, as well as the internal and external image. It is a core value that differentiates one individual from another. As personal branding via personal image management has become more important, there is a fast-growing number of online systems that provide self-test programs to analyze one's style and habits and also provide expert advice for not only styles but lifestyles as well. This study develops a systematic and objective personal image consulting system and offers basic information for the research of personal image making. For that purpose, the study attempts to examine the present state of global companies that use online image consulting programs and analyze their digital content. The results are as follows: 1) two domestic companies, Colorz and Atzine, and seven foreign companies, notably Covet and Boutique, were brisk in business; 2) two types of personal image-diagnosis programs - Visual search and Virtual matching - are now in operation; and 3) mobile applications exist as an evolved personal image-diagnosis program. With an increased interest in such programs, various companies at home and abroad are establishing systematic and scientific analysis systems, which are needed for personal image-making online. Under these circumstances, domestic companies are also urged to enhance levels of image-diagnosis content and actual commercialization and utilization, to develop programs that enable objectified, systematic personal image-making. To this end, the results of this study may serve as a helpful tool to consider future directions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1166-1191
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• 2011

This paper presents an adaptive active tracking system with sector based scanning for a single PTZ camera. Dividing sectors on an image reduces the search space to shorten selection time so that the system can cover many targets. Upon the selection of a target, the system estimates the target trajectory to predict the zooming location with a finite amount of time for camera movement. Advanced estimation techniques using probabilistic reason suffer from the unknown object dynamics and the inaccurate estimation compromises the zooming level to prevent tracking failure. The proposed system uses the simple piecewise estimation with a few frames to cope with fast moving objects and/or slow camera movements. The target is tracked in multiple steps and the zooming time for each step is determined by maximizing the zooming level within the expected variation of object velocity and detection. The number of zooming steps is adaptively determined according to target speed. In addition, the iterative estimation of a zooming location with camera movement time compensates for the target prediction error due to the difference between speeds of a target and a camera. The effectiveness of the proposed method is validated by simulations and real time experiments.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1214-1222
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• 2020

Pervasive augmented reality (AR) technology can be used to efficiently search for the required information regarding products in stores through text augmentation in an Internet of Things (IoT) environment. The evolution of context awareness and image processing technologies are the main driving forces that realize this type of AR service. One of the problems to be addressed in the service is that augmented objects are fixed and cannot be replaced efficiently in real time. To address this problem, a real-time mobile AR framework is proposed. In this framework, an optimal object to be augmented is selected based on object similarity comparison, and the augmented objects are efficiently managed using distributed metadata servers to adapt to the user requirements, in a given situation. To evaluate the feasibility of the proposed framework, a prototype system was implemented, and a qualitative evaluation based on questionnaires was conducted. The experimental results show that the proposed framework provides a better user experience than existing features in smartphones, and through fast AR service, the users are able to conveniently obtain additional information on products or objects.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1020-1033
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• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1074-1081
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• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.51-61
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• 2017

This paper proposes a method to generate panoramic images by combining conventional feature extraction algorithms (e.g., SIFT, SURF, MPEG-7 CDVS) with sensed data from an inertia sensor to enhance the stitching results. The challenge of image stitching increases when the images are taken from two different mobile phones with no posture calibration. Using inertia sensor data obtained by the mobile phone, images with different yaw angles, pitch angles, roll angles are preprocessed and adjusted before performing stitching process. Performance of stitching (e.g., feature extraction time, inlier point numbers, stitching accuracy) between conventional feature extraction algorithms is reported along with the stitching performance with/without using the inertia sensor data.