Gye-Young Kim

A Scheme to Reduce Packet Loss during PMIPv6 Handover considering Authentication

Mobile IPv6 (MIPv6) is a presentative protocol which supports global IP mobility. MIPv6 causes a long handover latency that a mobile node (MN) doesnpsilat send or receive packets. This latency can be reduced by using Proxy Mobile IPv6 (PMIPv6). PMIPv6 is a protocol which network supports IP mobility without participation of the MN, and is studied in Network-based Localized Mobility Management (NETLMM) working group of IETF. There is much packet loss during handover in PMIPv6, although PMIPv6 reduces handover latency. In this paper, to reduce packet loss in PMIPv6 we propose Packet Lossless PMIPv6 (PL-PMIPv6) with authentication. In PL-PMIPv6 a previous mobile access gateway (pMAG) registers to a Local Mobility Anchor (LMA) on behalf of a new MAG (nMAG) during layer 2 handoff. Then, the nMAG buffers packets during handover after registration. Therefore, PL-PMIPv6 can reduce packet loss than them in MIPv6 and PMIPv6. Also, we use Authentication,Authorization and Accounting (AAA) infrastructure to authenticate the MN and to receive MNpsilas profiles securely. We shows performance of PL-PMIPv6 through comparison of packet loss during handover of MIPv6, PMIPv6 and PL-PMIPv6.

Robust estimation of camera homography using fuzzy RANSAC

In this paper, we propose a method for robustly estimating camera homography using fuzzy RANSAC from the correspondences between consecutive two images. We use a fuzzified version of the original RANSAC algorithm to obtain accurate camera homography in the presence of outliers. The drawback of RANSAC is that its performance depends on a prior knowledge of the outlier scale. To resolve this problem, the proposed method classifies all samples into three classes (good sample set, bad sample set and vague sample set) using fuzzy classification. It then improves classification accuracy omitting outliers by iteratively sampling in only good sample set. Experimental results show the robustness of the proposed approach for computing a homography on real image sequence.

A New Incremental Watermarking Based on Dual-Tree Complex Wavelet Transform

This paper proposes a new incremental watermarking technique, which is robust for affine transformation and time-varying according to the degree of distortion, using Dual-Tree Complex Wavelet Transform (DT-CWT). At the embedding step, the proposed algorithm inserts a given watermark into the phase components of a transformed image by DT-CWT. At the extracting step, the algorithm incrementally compares the extracted with the original watermark using correlation from lowest to highest level. The proposed technique through performance evaluation displays that it was more robust in geometric distortions than a conventional CWT-based watermarking.

Model-based tracking of moving object

This paper describes a real-time tracking system which detects an object entering the field of view of a camera and executes tracking of the detected object by controlling a servo device in such a way that a target always lies at the center of an image frame. In order to detect and track a moving object, we basically apply model matching strategy. We allow the model of a target to vary dynamically during the tracking process so that it can assimilate variations of shape and intensities of a target. We also utilize a Kalman filter to encode a tracking history into state parameters of the filter. The estimated state parameters will then be used to reduce search areas for model matching and to control a servo device. Experimental results show that model adaptation allows robust tracking of a target object in dynamic environments. These experiments also confirm that the predicted values of a Kalman filter are very accurate in controlling a servo device and finding out the search areas for model matching. This paper concludes with theoretical bounds within which a tracking system can follow the movement of a target object.

The Content-Based Image Retrieval Method Using Multiple Features

In this paper, we propose an image retrieval method using color feature and shape feature. We suggest CBIR (content based image retrieval) method using color and shape information. Using just one feature information may cause inaccuracy compared with using more than two feature information. Therefore many image retrieval system use many feature information like color, shape and other features. We use two feature, HSI color information especially Hue value and CSS (curvature scale space) as shape information. We search candidate image form DB which include feature information of many images. As a result, we show that good results can be obtained by color and shape feature. When we use two features, we could approach better result.

A web-based collaborative filtering system

In this paper we describe a collaborative filtering system for automatically recommending high-quality information to users with similar interests on arbitrarily narrow information domains. It asks a user to rate a gauge set of items. It then evaluates the users rates and suggests a recommendation set of items. We interpret the process of evaluation as an inference mechanism that maps a gauge set to a recommendation set. We accomplish the mapping with fuzzy associative memory. We implemented the suggested system in a Web server and tested its performance in the domain of retrieval of technical papers, especially in the field of information technologies. The experimental results show that it may provide reliable recommendations.

Adaptive robust estimation of affine parameters from block motion vectors

In this paper, we propose an affine parameter estimation algorithm from block motion vectors for extracting accurate motion information with the assumption that the undergoing motion can be characterized by an affine model. The motion may be caused either by a moving camera or a moving object. The proposed method first extracts motion vectors from a sequence of images by using size-variable block matching and then processes them by adaptive robust estimation to estimate affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a continuous weight function based on a Sigmoid function. During the estimation process, we tune the Sigmoid function gradually to its hard-limit as the errors between the model and input data are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. Experimental results show that the suggested approach is very effective in estimating affine parameters reliably.

Video shot boundary detection algorithm

We present a newly developed algorithm for automatically segmenting videos into basic shot units. A basic shot unit can be understood as an unbroken sequence of frames taken from one camera. At first we calculate the frame difference by using the local histogram comparison, and then we dynamically scale the frame difference by Log-formula to compress and enhance the frame difference. Finally we detect the shot boundaries by the newly proposed shot boundary detection algorithm which it is more robust to camera or object motion, and many flashlight events. The proposed algorithms are tested on the various video types and experimental results show that the proposed algorithm are effective and reliably detects shot boundaries.

Kalman filter incorporated model updating for real-time tracking

This paper describes a real-time tracking system which detects an object entering into the field of view of a camera and executes the tracking of the detected object by controlling a servo device so that a target object always lies at the center of the image frame. In order to detect and track a moving object, we basically apply a model matching strategy. We allow the model to vary dynamically during the tracking process so that it can assimilate the variations of shape and intensities of the target object. We also utilize a Kalman filter so that a tracking history can be encoded into the state parameters of the Kalman filter. The estimated state parameters of the Kalman filter is then used to reduce the search areas for model matching and to control the servo device.

Texture feature-based text region segmentation in social multimedia data

This paper proposes a method of effectively segmenting text areas that exist in images by using the texture features of various types of input images obtained in social multimedia networks with an artificial neural network. The proposed text segmentation method consists of four main steps: a step for extracting candidate text areas, a step for localizing the text areas, a step for separating the text from the background, and a step for verifying the candidate text areas. In the candidate text area extraction step, candidate blocks that have any text areas are segmented in an input image on the basis of the texture features of the candidate blocks. In the text area localization step, only strings are extracted from the candidate text blocks. In the text and background separation step, the text areas are separated from the background area in the localized text blocks. In the candidate text area verification step, an artificial neural network is used to verify whether the extracted text blocks include actual text areas and exclude non-text areas. In the experimental results, the proposed method was applied to various types of news and non-news images, and it was found that the proposed method extracted text regions more accurately than existing methods.