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Image Registration is the process of aligning two or more images of the same scene taken from different viewpoints, at different times, from different sensors. Image registration aligns two images, i.e., base image and reference image, geometrically. There are different approaches of image registration and these approaches are categorized according to their nature that is areas based and feature based. These approaches are also categorized according to the four simple steps of image registration procedure: feature detection, feature matching, function mapping and transformation and re-sampling. Advantages and disadvantages of different methods are discussed in the paper. The main aim of this paper is to provide the knowledge of different image registrations methods used in different application area.

This paper presents an effective approach to minimize recursive computations for balancing stereo pairs by using disparity vector errors and its directional histogram. A stereo balancing function is computed from the correspondent pixels between two images, and a simple approach is to find the matching blocks of two images. However, this procedure requires recursive operation, and its computation cost is very high. Therefore, in this paper, we propose an efficient balance method using structural similarity index and a partial re-searching scheme to reduce the computation cost considerably. For this purpose, we determine if re-searching for each block is necessary or not by using the errors and the directional histogram of disparity vectors. Experiment results show that the performance of the proposed approach can save the computations significantly with ignorable image quality degradation compared with full re-search approach.

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera's mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2-3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

We presented a mapping the work function of the vanadium pentoxide ($V_2O_5$) nanonet structures by scanning Kelvin probe microscopy (SKPM). In this measurement, the $V_2O_5$ nanonet was self-assembled via dropping the solution of $V_2O_5$ nanowires (NWs) onto the $SiO_2$ substrate and drying the solvent, resulting in the networks of $V_2O_5$ NWs. We found that the SKPM signal as a surface potential of $V_2O_5$ nanonet is attributed to the contact potential difference (CPD) between the work functions of the metal tip and the $V_2O_5$ nanonet. We generated the histograms of the CPD signals obtained from the SKPM mapping of the $V_2O_5$ nanonet as well as the highly ordered pyrolytic graphite (HOPG) which is used as a reference for the calibration of the SKPM tip. By using the histogram peaks of the CPD signals, we successfully estimated the work function of ~5.1 eV for the $V_2O_5$ nanonet structures. This work provides a possibility of a nanometer-scale imaging of the work function of the various nanostructures and helps to understand the electrical characteristics of the future electronic devices.

Mismatch or non-isomorphic mapping between form and function is prevalent in natural languages. English is no exception in this respect. It displays various instances of mismatch phenomena that can be classified into two main groups: complexity and content mismatch. This paper discusses several instances of these two types of mismatch in English and sketches how the lexicalist grammar with parallel architecture can license such non-isomorphic relations or no direct correspondences between form and meaning.

The goal of this study was to analyze landslide susceptibility using two different models and compare the results. For this purpose, a landslide inventory map was produced from a field survey, and the inventory was divided into two groups for training and validation, respectively. Sixteen landslide conditioning factors were considered. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the FR (Frequency Ratio) and EBF (Evidential Belief Function) models. The LSI (Landslide Susceptibility Index) maps that were produced were validated using the ROC (Relative Operating Characteristics) curve and the SCAI (Seed Cell Area Index). The AUC (Area under the ROC Curve) values of the FR and EBF LSI maps were 80.6% and 79.5%, with prediction accuracies of 72.7% and 71.8%, respectively. Additionally, in the low and very low susceptibility zones, the FR LSI map had higher SCAI values compared to the EBF LSI map, as high as 0.47%p. These results indicate that both models were reasonably accurate, however that the FR LSI map had a slightly higher accuracy for landslide susceptibility mapping in the study area.

여러 지질재해 중에서 산사태로부터 피해를 최소화하기 위해서는 미래의 산사태에 대해 취약한 지역의 추정이 필요하다. 산사태 위험성의 정량적 분석을 목적으로, 본 논문에서는 확률론적 공간통합 방법인 베이지안 기법의 적용가능성에 대해서 논의하고자 한다. 우선 산사태 발생과 관련이 있는 여러 공간자료의 확률론적 표현을 위해 우도비 함수를 사용하였으며, 베이지안 결합 규칙을 이용하여 최종적으로 통합된 검증을 수행하였다. 이러한 방법의 적용가능성을 검토하기 위하여 1998년 여름 산사태 공간 분포의 분할을 통한 검증을 수행하였다. 이러한 방법의 적용가능성을 검토하기 위하여 1998년 여름 산사태로 피해를 입은 경기도 장흥지역을 대상으로 사례연구를 수행하였다. 사례연구 수행 결과, 우도비에 기반한 베이지안 공간 통합 기법은 효율적으로 다양한 공간 자료를 통합할 수 있었으며, 검증결과는 해석과 의사결정 보조자료로 이용될 수 있을 것으로 기대된다.

Landslides, as a geological hazard, have caused extensive damage to property and sometimes result in loss of life. Thus, it is necessary to assess vulnerable areas for future possible landslides in order to mitigate the damage they cause. For this purpose, spatial data integration has been developed and applied to landslide hazard mapping. Among various models, this paper investigates and discusses the effectiveness of the Bayesian spatial data integration approach to landslide hazard mapping. In this study, several data sets related to landslide occurrences in Jangheung, Korea were constructed using GIS and then digitally represented using the likelihood ratio function. By computing the likelihood ratio, we obtained quantitative relationships between input data and landslide occurrences. The likelihood ratio functions were combined using the Bayesian combination rule. In order for predicted results to provide meaningful interpretations with respect to future landslides, we carried out validation based on the spatial partitioning of the landslide distribution. As a result, the Bayesian approach based on a likelihood ratio function can effectively integrate various spatial data for landslide hazard mapping, and it is expected that some suggestions in this study will be helpful to further applications including integration and interpretation stages in order to obtain a decision-support layer.

본 논문에서는 VVC의 LMCS에서 휘도 신호 매핑 방법의 부호화 효율을 향상시키기 위한 휘도 신호 매핑 함수 생성 방법을 제안한다. 본 논문에서 제안하는 방법은 기존 LMCS에서 지역적 특징을 반영하기 위하여 사용하는 지역적 공간 분산에 합성 곱 신경망의 어텐션 맵을 곱하여 인지 지각적 특징을 추가적으로 반영한다. 제안하는 방법의 성능 평가를 위하여 AI (All Intra) 조건에서 VVC 표준 실험 영상의 A1, A2, B, C, D 클래스를 이용하여 VTM-12.0과 BD-rate 성능을 비교한다. 실험 결과로서 본 논문에서 제안하는 방법이 VTM-12.0 대비 BD-rate 성능 관점에서 휘도 성분이 평균 -0.07%의 성능 향상을 보이고, 부/복호화 시간은 거의 동일하다.

In this paper, we propose a method for generating luma signal mapping function to improve the coding efficiency of luma signal mapping methods in LMCS. In this paper, we propose a method to reflect the cognitive and perceptual features by multiplying the attention map of convolutional neural networks on local spatial variance used to reflect local features in the existing LMCS. To evaluate the performance of the proposed method, BD-rate is compared with VTM-12.0 using classes A1, A2, B, C and D of MPEG standard test sequences under AI (All Intra) conditions. As a result of experiments, the proposed method in this paper shows improvement in performance the average of -0.07% for luma components in terms of BD-rate performance compared to VTM-12.0 and encoding/decoding time is almost the same.

In this paper, the structure of CMAC address mapping is first revisited, and the address hashing function and the random mapping is discussed in the conventional CMAC implementation. Then the effective size of CMAC memory is derived from the modulus property of the CMAC address vector, and a new hashing function for the effective memory mapping is proposed for a CMAC implementation with feasible memory size and no troublesome random mapping. Finally, the performance of the conventional CMAC learning algorithm and that of the proposed new CMAC scheme arc compared via simulations.

통신에서 이미지의 보안을 유지하는 일이 점점 중요한 이슈가 되고 있다. 그러나 기존의 여러 암호화 기법 통신상의 다양한 보안요구를 충족하지 못하고 있는 것이 현실이다. 이 논문에서는 새로운 암호화 전략이 제안되고 구현된다. 이는 통상적으로 카오스라 하는 암화 이미지와 원 이미지 간에 관계가 카오스적인, 패턴이 없는 무질서와 혼합적 구조를 가지고 있으나 그럼에도 불구하고 원래의 이미지로 돌아 올 수 있다. 여기에서는 이러한 카오스적 형태의 암호화 전략을 제안하고 구현하여 향후 나아간 연구를 자극하고자 한다.

본 연구는 이동 객체 추출 및 추적 방법 및 장치에 관한 것으로, 특히 인접 영상 간의 차영상을 이용하여 객체를 추출하고, 추출된 객체의 위치정보를 지속적으로 전달함으로써 적어도 하나의 이동 객체의 정확한 위치정보를 토대로 이동 객체를 추적하는 이동 객체 추출 및 추적 방법 및 장치에 관한 것이다. 사람과 컴퓨터의 상호작용의 표현에서 시작된 사람추적은 로봇학습, 객체의 카운팅, 감시 시스템 등의 많은 응용분야에서 사용되고 있으며, 특히 보안 시스템분야에서 카메라를 이용하여 사람을 인식하고 추적하여 위법행위를 자동적으로 찾아낼 수 있는 감시 시스템 개발의 중요성이 나날이 커져 가고 있다.

This study relates to a method and device for extracting and tracking moving objects. In particular, objects are extracted using different images between adjacent images, and the location information of the extracted object is continuously transmitted to provide accurate location information of at least one moving object. It relates to a method and device for extracting and tracking moving objects based on tracking moving objects. People tracking, which started as an expression of the interaction between people and computers, is used in many application fields such as robot learning, object counting, and surveillance systems. In particular, in the field of security systems, cameras are used to recognize and track people to automatically detect illegal activities. The importance of developing a surveillance system, that can detect, is increasing day by day.

본 논문에서는 복잡한 카오스 신호를 발생시키는 HVPM(hyperchaotic volume preserving maps) 모델과 HVPM 모델의 구현회로를 제안한다. 랜덤한 카오스 신호를 발생시키기 위하여 3차원 이산시간(discrete-time) 연산과 비선형 사상(maps)으로 모듈러(modulus) 함수를 이용하여 하이퍼카오스 신호를 발생시킨다. 그리고 HVPM 모델은 여러 가지 시스템 파라미터들을 변화시키면 다양한 카오스 신호를 발생시킬 수 있으며, 출력되는 카오스 신호는 비주기성을 갖게 된다. 이러한 특징을 갖는 HVPM 모델의 회로 구현을 위하여 2단 N형의 함수를 CMOS와 선형 연산증폭기 및 비교기를 이용하여 보드상에서 구현하여, 다양한 하이퍼카오스 신호를 확인할 수 있었다.

본 연구에서는 AI 진단 및 학습 추천을 하기 위한 학습 맵(지식그래프)의 매핑 기능과 관련하여, 효율적인 매핑 기능 개발을 위한 그래프 데이터베이스의 기능을 비교 분석하였다. Neo4j, Microsoft Azure Cosmos DB, Aerospike 등 주요 그래프 데이터베이스별 기능의 공통점과 차이점을 바탕으로 데이터 모델링, 쿼리 언어 및 API, 데이터 무결성 및 보안, 성능 및 수평적 확장성, 커뮤니티 및 지원이라는 5가지 비교 기준을 설정하였다. 이를 바탕으로, 학습 맵 매핑의 효과적인 개발을 위한 적합한 그래프 데이터베이스 선정 시 고려해야 할 사항을 도출하였다. 이러한 고려 사항은 학습 맵을 활용한 기능 개발 시 적합한 그래프 데이터베이스 선택의 기반이 될 수 있을 것이다. 본 연구는 향후 그래프 데이터베이스를 활용한 학습 맵 구축 시 실질적인 참고 자료로 활용될 것으로 기대된다.

In this study, the function of the graph database for efficient mapping function development was compared and analyzed in relation to the mapping function of the learning map (knowledge graph) for AI diagnosis and learning recommendation. Based on the commonalities and differences of functions by major graph database such as Neo4j, Microsoft Azure Cosmos DB, and Aerospike, five comparison criteria were set: data modeling, query language and API, data integrity and security, performance and horizontal scalability, community and support. Based on this, matters to be considered when selecting an appropriate graph database for the effective development of learning map mapping were derived. These considerations can be the basis for selecting an appropriate graph database when developing functions using a learning map. This study is expected to be used as a practical reference material when constructing a learning map using a graph database in the future.

A new control method using average comparison strategy have been proposed in this paper. This control method realizes sinusoidal input and output current. unity input displacement factor regardless of load power factor. Moreover, compensation of the asymmetrical and/or harmonic containing input voltage is automatically realized, and calculation time of control function is reduced.

A new error estimation method is proposed. This utilizes the variation of energy functional about the mapping function between the global and the master elements. The resultant system of equations is the weak form of the generalized conservation checks. However, This formulation has an important information about the relations between the connected elements. In other words, some relations between the connected elements are obtained and these can be used very usefully to measure it posteriori error. In this paper, the explicit formulations are presented for the 1-dimensional model and the 2-dimensional model problems. Numerical results are provided for first order shear deformation theody of beam model and the plane stress problem.

A new control method using average comparison strategy have been proposed in this paper. This control method realizes sinusoidal input and output current. unity input displacement factor regardless of load power factor. Moreover compensation of the asymmetrical and harmonic containing input voltage is sautomatically realized and calculation time of control function is reduced.

메시지 처리기란 클라이언트들로부터 수신된 다양한 포맷의 메시지를 수신 메시지 별 쓰레드를 생성하여 처리하고 그 결과를 목적지로 전달하는 서버 소프트웨어를 말한다. 본 논문에서는 SEDA(The Stage Event Driven Architecture) 구조의 메시지 처리기에서 각 수행함수가 CPU 또는 IO bound 특성을 가질 수 있다는 점에 착안하여, 수행 함수 별 특성을 고려해 쓰레드를 각 코어에 매핑하는 FC-TM(Function Characteristic aware Thread Mapping) 기법을 제안하였다. 본 논문에서는 메시지 처리에 필요한 각 수행함수의 특성이 정적인 것으로 가정하였다. 따라서 수행 가능한 함수를 미리 프로파일링(profiling)하여 특성을 분석한 후, 실제 수신되는 메시지에 따라 단위시간 당 처리량(throughput)이 최대가 되도록 쓰레드를 매핑하였다. 성능 평가 결과 IO bound한 함수가 특정 비율 이상 있을 경우, 기존 연구보다 단위시간당 처리량이 최대 72%까지 증가하였다.

A message processor is server software that receives various message formats from clients, creates the corresponding threads to process them, and lastly delivers the results to the destination. Considering that each function of an SEDA-based message processor has its own characteristics such as CPU-bound or IO-bound, this paper proposes a thread-mapping strategy called "FC-TM" (function-characteristic aware thread mapping) that schedules the threads to the cores based on the function characteristics in multi-core environments. This paper assumes that message-processor functions are static in the sense that they are pre-defined when the message processor is built; therefore, we profile each function in advance and map each thread to a core using the information in order to maximize the throughput. The benchmarking results show that the throughput increased by up to a maximum of 72 % compared with the previous studies when the ratio of the IO-bound functions to the CPU-bound functions exceeds a certain percentage.