Earticle

In this paper, we propose a simple and efficient retrieval technique using the feature value of the corner region, which is one of the shape information attributes of images. The proposed algorithm extracts the edges and corner points of the image and rearranges the feature values of the corner regions doubly, and then measures the similarity with the image in the database using the correlation of these feature values as the feature vector. The proposed algorithm is confirmed to be more robust to rotation and size change than the conventional image retrieval method using the corner point.

In autonomous driving, environmental perception and decision-making are technologies that acquire signals based on various sensors and generate information that enables obstacle avoidance, emergency stops, and path planning. Such environmental perception technologies are limited in that they depend on expensive sensors such as LIDAR and RADAR, so research on environmental perception technologies using only cameras is actively being conducted. Studies predicting traffic accidents based on dashcam footage have also been performed as part of such research. This is challenging because only limited forward-looking footage can be acquired, and the surrounding environment is dynamic and changes quickly, making analysis difficult. Existing research has focused on learning the spatialtemporal feature representation to solve these problems. This paper extracts the trajectories of the ego-vehicle and surrounding vehicles using Visual SLAM and Multiple Object Tracking algorithms and uses them as inputs to the graph convolutional neural network(GCN) to learn the spatialtemporal feature representation. In addition, the features learned through the GCN are used as inputs to a bayesian neural network(BNN) to predict the probability of accidents, and its ability to predict accidents in advance has been verified by comparison with existing studies.

본 논문은 영상 속 객체와 배경의 컬러 특징을 이용한 주요 객체의 자동 추출 방법에 관한 연구이다. 인간 이 객체를 판단할 때에는 배경과 객체의 색상 차이를 이용하는데 이러한 요소를 객체 추출 방법에 적용시키기 위해 서는 배경과 객체의 색차를 강조하여야 한다. 따라서 본 논문에서는 원 RGB 영상을 인간의 시각 시스템과 유사한 HSV 색 공간으로 변환하고 각기 다른 분포도의 메디안 필터를 적용한 두 개의 영상을 생성한 뒤 두 개의 메디안 필 터가 적용된 영상들을 합산하였고 데이터 군집화 방법인 Mean Shift 알고리즘을 적용하여 색상 특징을 그룹화 하였 다. 마지막으로 이진화 작업을 위하여 영상의 채널 수를 3 채널에서 1 채널로 정규화 한 뒤 영상 내 픽셀들의 평균 값을 임계값으로 이용하는 이진화 방법으로 객체 지도 영상을 생성하였고 주요 객체를 추출하였다.

This paper is a study on an object extraction method which using color features of an object and background in the image. A human recognizes an object through the color difference of object and background in the image. So we must to emphasize the color's difference that apply to extraction result in this image. Therefore, we have converted to HSV color images which similar to human visual system from original RGB images, and have created two each other images that applied Median Filter and we merged two Median filtered images. And we have applied the Mean Shift algorithm which a data clustering method for clustering color features. Finally, we have normalized 3 image channels to 1 image channel for binarization process. And we have created object map through the binarization which using average value of whole pixels as a threshold. Then, have extracted major object from original image use that object map.

자연어 처리를 활용한 생성 AI 기술의 최근 발전은 텍스트, 이미지 및 비디오 제작에 큰 영향을 미쳤다. 이러한 발전에도 불구하고, AI가 생성한 출력의 일관성 및 재사용 가능성과 관련하여 상당한 문제가 있다. 이는 캐릭터와 특정 객체를 생성하는 것이 중요한 만화 제작 분야에서 문제가 될 수 있다. 이를 해결하기 위해 언어 분석 기반 요구사항 엔지니어링과 만화 엔지니어링의 접목을 제안한다. 제안된 방법은 자연어 분석을 위한 Chomsky와 Fillmore의 언어학을 적용하고 객체의 상호작용을 표현하기 위한 UML 시퀀스 모델 사용하여 일관적인 3D Objects를 생성한는 것이다. 또한 자연어 입력에서 창작자의 의도를 체계적 해석한다. 이를 통해 캐릭터 또는 객체가 정의되면 다양한 패널과 에피소드에서 정확하게 재사용해 시각적, 맥락적 무결성을 유지하게 한다. 이 접근 방식은 만화에서 캐릭터 묘사의 정확성과 일관성을 향상시켜 캐릭터와 장면이 원래 요구 사항과 밀접하게 일치시킨다. 따라서 본 연구에서 제안하는 방법은 자연어 텍스트에서 복잡한 시각적 콘텐츠의 재현이 필요한 다른 분야에서도 적용할 수 있을 것으로 기대된다.

Recent advances in generative AI technologies using natural language processing have critically impacted text, image, and video production. Despite these innovations, we still need to improve the consistency and reusability of AI-generated outputs. These issues are critical in cartoon creation, where the inability to consistently replicate characters and specific objects can degrade the work's quality. We propose an integrated adaption of language analysis-based requirement engineering and cartoon engineering to solve this. The proposed method applies the linguistic frameworks of Chomsky and Fillmore to analyze natural language and utilizes UML sequence models for generating consistent 3D representations of object interactions. It systematically interprets the creator's intentions from textual inputs, ensuring that each character or object, once conceptualized, is accurately replicated across various panels and episodes to preserve visual and contextual integrity. This technique enhances the accuracy and consistency of character portrayals in animated contexts, aligning closely with the initial specifications. Consequently, this method holds potential applicability in other domains requiring the translation of complex textual descriptions into visual representations.

최근에 미국, 영국, 일본에서 폭발물, 독성 화학물 등에 의한 테러가 공공장소에서 시도되고 있다. 위험물을 공공장소에서 두고 가는 방식은 탐지하기 어려운 방법 중에 하나로 인식되고 있다. 공공장소에는 곳곳에 카메라가 영상 을 녹화하고 있지만, 그 영상을 사람이 일일이 모니터링 하는 것은 쉽지 않은 일이다. 최근에는 자동으로 영상을 분석하 는 지능형 소프트웨어를 유기물 탐지에 이용하고 있다. Lin 등의 방식은 비교적 높은 유기물 탐지율을 보이고 있으나, 단기 배경 영상의 특성으로 유기물에 관련한 픽셀의 수가 시간이 지날수록 급격히 감소하는 경향이 있어 그 형상 정보 를 얻기가 어렵다. 본 논문에서는 면적의 변화 특성을 분석함으로써 유기물의 형태를 성공적으로 추출하기 위한 새로운 기법을 제안한다. 제안한 방식에 대해 실험을 한 결과 선행 연구보다 형태 추출에서 우수한 성능을 보인다.

Recently the terrors have been attempted in the public places of the nations such as United states, England and Japan by explosive things, toxic materials and so on. It is understood that the method in which dangerous objects are put in public places is one of the difficult types in detection. While there are the cameras recording videos for many spots in public places, it is very hard for the security personnel to monitor every videos. Nowadays the smart softwares which can analyzing videos automatically are utilized to detect abandoned objects. The method by Lin et al. shows comparatively high detection rates for abandoned objects but it is not easy to obtain the shape information because there is a tendency that the number of the pixels decreases abruptly along the time goes due to the characteristics of short-term background images. In this research a novel method is proposed to successfully extract the shape of the abandoned object by analysing the characteristics of area variation. The experiment results show that the proposed method has better performance in extracting shape information in comparison with the precedent approach.

최근 들어 영상처리는 여러 분야에서 사용되어지고 있다. 영상처리에서 많이 연구되어지고 있는 기술은 실시간으로 객체를 추적하는 기술이다. 객체를 추적하는 방법은 보행자를 추적하는 HOG(Histogram of Oriented Gradients), 전경과 배경 분리 방법을 사용하는 Codebook 같은 방법 들이 많이 알려져 있다. 그러나 객체가 움직이거나 동적인 배경, 조명변화가 심할 경우 객체 추출이 어려워진다. 본 논문에서는 ROI(Region of Interest)기반 깊이영상과 컬러영상의 특징을 이용해 객체를 추출하는 방법을 제안한다. 첫 번째, 깊이 영상에서 배경분리를 통해 객체의 위치를 찾아 ROI로 설정해준다. 두 번째, 컬러영상을 이용하여 영상의 특징점을 찾는다. 세 번째, 특징점과 객체의 볼록헐(convex hull) 구성점들을 이용하여 새로운 윤곽을 만들어 더 정확한 객체를 추출하도록 한다. 마지막으로 본 논문에서 제안한 방법과 기존 방법과의 비교를 통해 제안한 방법의 결과가 좀 더 정확한 객체를 추출하고 있음을 검증하였다.

Recently, Image processing has been used in many areas. In the image processing techniques that a lot of research is tracking of moving object in real time. There are a number of popular methods for tracking an object such as HOG(Histogram of Oriented Gradients) to track pedestrians, and Codebook to subtract background. However, object extraction has difficulty because that a moving object has dynamic background in the image, and occurs severe lighting changes. In this paper, we propose a method of object extraction using depth image and color image features based on ROI(Region of Interest). First of all, we look for the feature points using the color image after setting the ROI a range to find the location of object in depth image. And we are extracting an object by creating a new contour using the convex hull point of object and the feature points. Finally, we compare the proposed method with the existing methods to find out how accurate extracting the object is.

연구목적: 2022년 산업재해 현황 부가통계에서 건설업 사망사고자 현황을 보면 건설업 전체 사망사고자 의 27.8%가 건설장비로 인해 발생하고 있다. 현장 대형화, 고층화 등으로 발생하는 순회 및 점검의 한계를 극복하기 위해 컴퓨터 비전 기술을 활용해 건설장비를 추출할 수 있는 모델을 구축하고 해당 모델의 정확 도 및 현장 적용성에 대해 분석하고자 한다. 연구방법: 본 연구에서는 건설장비 중 굴착기, 덤프트럭, 이동 식 크레인의 이미지 데이터를 딥러닝 학습시킨 뒤 학습 결과를 평가 및 분석하고 건설현장에 적용하여 분 석한다. 연구결과: ‘A’ 현장에서는 굴착기 및 덤프트럭의 객체를 추출하였으며, 평균 추출 정확도는 굴착 기 81.42%, 덤프트럭 78.23%를 나타냈다. ‘B’ 현장의 이동식 크레인은 78.14%의 평균 정확도를 보여줬다. 결론: 현장 안전관리의 효율성이 증가할 수 있고, 재해발생 위험요인을 최소화 할 수 있을것이라 본다. 또 한, 본 연구를 기반으로 건설현장에 스마트 건설기술 도입에 관한 기초적인 자료로 활용이 가능하다.

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site ‘A’, objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site ‘B’ showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.

기존의 GrabCut 알고리즘은 자동 객체 추출이 아닌 사용자가 객체 영역에 사각형 윈도우를 설정해야하는 알고리즘이다. 본 논문에서는 자동 시스템으로 변환하기 위해 인간의 시각 시스템을 기반으로 영상에서 가장 눈에 띄는 영역을 탐지하는 방법을 연구하였다. 주의 시각 영역인 Saliency Map을 생성하기 위해서 인간이 색채를 감지하는 ‘적/녹’ ‘황/청’의 대립색설을 기반으로 하는 Lab 색공간을 이용하여 생성한다. 생성된 Saliency Map을 주파수 공간으로 변환하여 저주파 영역에 국부적인 경계를 나타내고 경계를 탐지해내어 Saliency Point를 생성한다. 이렇게 생성된 Saliency Point의 좌표 값을 이용하여 윈도우를 자동으로 생성한 후 GrabCut 알고리즘을 기반으로 객체를 추출하였다. 다양한 영상에 제안한 알고리즘을 적용한 결과 객체 영역에 자동으로 윈도우가 생성되었고 객체가 추출되었다.

Conventional GrabCut algorithm is semi-automatic algorithm that user must be set rectangle window surrounds the object. This paper studied automatic object detection to solve these problem by detecting salient region based on Human Visual System. Saliency map is computed using Lab color space which is based on color opposing theory of 'red-green' and 'blue-yellow'. Then Saliency Points are computed from the boundaries of Low-Frequency region that are extracted from Saliency Map. Finally, Rectangle windows are obtained from coordinate value of Saliency Points and these windows are used in GrabCut algorithm to extract objects. Through various experiments, the proposed algorithm computing rectangle windows of salient region and extracting objects has been proved

최근 AI 기술의 발전으로 다양한 분야에서 활용되고 있다. 그러나 AI의 동작 원리를 확인할 수 없어 생성물의 품질을 보장하기 어렵다. 따라서 본 연구에서는 대화형 AI 기술에 소프트웨어 공학의 요구공학을 접목하여 절차적 공정을 진행한다. 기존의 요구공학 연구는 문법 중심 분석 방식을 사용하여 비정형 요구사항의 의미적 측면을 충분히 해석하지 못하는 한계를 가진다. 이를 해결하기 위해, 우리는 촘스키의 구문 구조 분석 이론과 필모어의 의미역 이론의 통합을 제안한다. 또한, 우리는 이전 연구를 확장하여 다양한 문장 구조에서 사용되는 형용사 분석을 추가한다. 이는 만화 속 주인공에 대한 정확한 감정 분석이 가능하다. 분석 결과를 기반으로, 우리는 상태 다이어그램의 상태를 객체의 감정 상태로 적용한다. 우리는 객체의 상태 다이어그램의 감정 상태 기반으로 Three.js를 통해, 3D 객체 모델 생성과 와 상태를 표현시킨다. 이를 통해, 객체의 감정을 표현하기를 기대한다.

Recent advancements in AI technology have led to its application across various fields. However, the lack of transparency in AI operations makes it challenging to guarantee the quality of its outputs. Therefore, we integrate requirements engineering in software engineering with conversational AI technology to ensure procedural fairness. Traditional requirements engineering research uses grammar-centered analysis, which often fails to fully interpret the semantic aspects of natural language. To solve this, we suggest combining Noam Chomsky's syntactic structure analysis with Charles Fillmore's semantic role theory. Additionally, we extend our previous research by analyzing adjectives in informal requirement sentence structures. This enables precise emotional analysis of the main characters in comics. Based on the results of the analysis, we apply the emotional states of the objects to the states in the UML state diagram. Then, we create the 3D object with Three.js based on the object that reflects the emotional states in the state diagram. With this approach, we expect to represent the emotional state of a 3D object.

실사 및 그래픽 기반 가상현실 콘텐츠는 360도 영상을 기반으로 하며, 시청자의 의도나 자동 추천 기능을 통한 뷰포트 추출이 필수적이다. 본 논문은 360도 영상에서 다중 객체 추적 기반의 뷰포트 추출 시스템을 설계하고, 다중 뷰포트 추출에 필요한 병렬화된 연산 구조를 제안한다. 360도 영상에서 뷰포트 추출 과정을 ERP 좌표의 3D 구 표면 좌표 변환과 3D 구 표면 좌표의 뷰포트 내 2D 좌표 변환 과정을 순서대로 픽셀 단위의 스레드로 구성하여 연산을 병렬화하였다. 제안 구조는 항공 360도 영상 시퀀스들에 대하여 최대 30개의 뷰포트 추출 과정에 대한 연산 시간이 평가되었으며, 뷰포트 수에 정비례하는 CPU 기반 연산 시간에 비해 최대 5240배 가속화됨을 확인하였다. ERP 프레임 I/O 시간을 줄일 수 있는 고속의 I/O나 메모리 버퍼를 사용 시 뷰포트 추출 시간을 7.82배 추가 가속화가 가능하다. 제안하는 뷰포트 추출 병렬화 구조는 360도 비디오나 가상현실 콘텐츠들에 대한 동시 다중 접속 서비스나 사용자별 영상 요약 서비스 등에 활용될 수 있다.

Realistic and graphics-based virtual reality content is based on 360-degree videos, and viewport extraction through the viewer's intention or automatic recommendation function is essential. This paper designs a viewport extraction system based on multiple object tracking in 360-degree videos and proposes a parallel computing structure necessary for multiple viewport extraction. The viewport extraction process in 360-degree videos is parallelized by composing pixel-wise threads, through 3D spherical surface coordinate transformation from ERP coordinates and 2D coordinate transformation of 3D spherical surface coordinates within the viewport. The proposed structure evaluated the computation time for up to 30 viewport extraction processes in aerial 360-degree video sequences and confirmed up to 5240 times acceleration compared to the CPU-based computation time proportional to the number of viewports. When using high-speed I/O or memory buffers that can reduce ERP frame I/O time, viewport extraction time can be further accelerated by 7.82 times. The proposed parallelized viewport extraction structure can be applied to simultaneous multi-access services for 360-degree videos or virtual reality contents and video summarization services for individual users.

3차운 물체 인식 중 오목과 볼록을 갖고 있는 물체의 인식은 대단히 어려운 문제이다. 본 논문에서는 물체의 인식을 위한 피처(Feature)의 추출 방법으로 오픈-볼(Open-Ball)을 제안한다. 이 새로운 방법은 물체의 크기, 이동고 회전에 불변성을 갖는 피처(Feature)를 생성하는 것뿐만이 아니라, 비교되는 물체를 인식하는 것을 상대적인 닮음 정도 측정으로 구현한다.

Recognition of three-dimensional objects with convexities and concavities is a hard and challenging problem. This paper presents a feature extraction method out of three-dimensional objects for the purpose of classification. This new method not only provides invariance to scale, translation, and rotation $R^3$ but also distinguishes any three-dimensional model objects with concavities and convexities by measuring a relative similarity in the information space where a set of characteristics features of objects is mapped.

Kidnapping is a crime that can have disastrous results for the victim and their family. It is important to develop effective systems to detect and prevent such incidients. This paper proposes a Kidnapping Detection Systems that uses Real-Time Object Detection YOLO-v7, and Skeleton Extraction module AlphaPose to detect and track potential kidnapping event in real-time. The system utilizes a number of surveillance cameras that are already installed in Korea. It employs surveillance camera system as an edge module and a GPU system as a server module. By performing deep detection only when there is a high likelihood of a kidnapping event at the edge device, we can reduce inference costs. We have also built a dataset by recording simulated kidnapping scenarios, which can serve as a substitute for actual kidnapping events. Based on our dataset, we achieved an accuracy of 90.3% on the test set using a rule-based approach that considers the angle of the legs and occlusion with people and a car. Our system shows a promising solution for enhancing public safety and preventing crimes.

Automatic video segmentation plays an important role in real-time MPEG-4 encoding systems. This paper presents a video segmentation algorithm for MPEG-4 camera system with change detection, background registration techniques and real time adaptive threshold techniques. This algorithm can give satisfying segmentation results with low computation load. Besides, it has shadow cancellation mode, which can deal with light changing effect and shadow effect. Furthermore, this algorithm also implemented real time adaptive threshold techniques by which the parameters can be decided automatically.

Human object extraction from infrared image has broad applications, and has become an active research area in image processing community. Combined with chaos differential evolution (CDE) algorithm and morphological operators, a novel infrared human target extraction method is proposed based on nonextensive fuzzy entropy. Firstly, the image was transformed into a fuzzy domain by fuzzy membership function, and the image nonextensive fuzzy entropy was constructed. Then, the image was segmented by thresholding based on the maximum entropy principle and the pseudoadditivity rule of nonextensive entropy. In order to reduce the search time of optimal threshold selection, the CDE algorithm was presented. Finally, the object was extracted using morphological operators to denoise, fill cavity on the threshold segmented image. Experimental results show that the proposed method is efficient and requires less computation time.

Currently product reviews information has become the focus of increasing concern to these comments often containing more information on consumer evaluation of various attributes of goods, and therefore the evaluation of mining comment object has become a hot topic. This paper presents a method called evaluation object extraction based on Bootstrapping in Chinese commodity comments, this method first comment summed up a lot of information on the properties of 10 kinds of speech rule templates, and then determined the integrity of the candidate meets these templates phrases, and these have complete of candidates as a seed, extended new property template by Bootstrapping method, and finally removed in line with all the attributes of a template as a final evaluation of the object phrase, and through experiments to calculate the accuracy, harmonic mean of recall and pr

Quan, Feng & Suh, Jinhee. 2014. A Phase Inheritance-based Account on A’-extraction of Double Object Constructions. Korean Journal of Linguistics, 39-1, 181-204. This paper attempts to explain the A’-extraction of Double Object Constructions from a cross-linguistic perspective. Three hypotheses are adopted. Firstly, every functional head may bear an EPP feature (Chomsky 1998, Landau 2007, Saito 2011), namely the generalized EPP regardless whether it is a Phase head or not, while the Phase head bears an extra EPP feature, namely the Phase EPP. Secondly, the Phase EPP feature may be handed over to the head immediately below it. Thirdly, the Mixed Chains Constraint should be respected (Chomsky 2005). In American English type-languages, since the Phase EPP is handed over to the applicative head from the little v, only the DO is allowed to undergo A’-movement to Spec CP. In Korean type-languages, since the Phase EPP feature is retained on the little v, only the IO is allowed to undergo A’-movement to Spec CP. In Polish type-languages, since the Phase EPP feature is optionally handed over to the applicative head from the little v, either the IO or the DO can undergo A’-movement to Spec CP. (Yonsei University)

In this paper, we propose a semi-automatic method for semantic video object extraction which extracts meaningful objects from an input sequence with one correctly segmented training image. Given one correctly segmented image acquired by the user's interaction in the first frame, the proposed method automatically segments and tracks the objects in the following frames. We formulate the semantic object extraction procedure as an energy minimization problem at the fragment level instead of pixel level. The proposed energy function consists of two terms: data term and smoothness term. The data term is computed by considering patch similarity, color, and motion information. Then, the smoothness term is introduced to enforce the spatial continuity. Finally, iterated conditional modes (ICM) optimization is used to minimize energy function in a globally optimal manner. The proposed semantic video object extraction method provides faithful results for various types of image sequences.