• Title/Summary/Keyword: Range Segmentation

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New Surface Segmentation and Feature Description Technique from 2-D object image (2차원 물체영상으로부터의 새로운 면 분할 및 특징표현기법)

  • Lee, Boo-Hyoung
    • Journal of the Korean Institute of Telematics and Electronics T
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    • v.36T no.4
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    • pp.1-8
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    • 1999
  • This paper presents a new algorithm for surface segmentation and feature description. In the first stage of proposed algorithm, the signature of an edge image of object is extracted. The signature technique represents a surface using the distance from the mass center to the boundary of the image as a function of angle rotating counterclockwise. If there exists a range in the angle axis where more than two signatures form a closed curve, we can conclude there is a surface inside the range. Using this feature of the signature, surface can be segmented. The surface features such as number of vertices, number of edges, convex and type of surface can also be extracted from segmented surfaces. This algorithm has distinguished advantages; it can easily recover the lost part in the edge image using the curve fitting method; it extracts surface features correctly regardless of the rotation of the surface in 3-D space.

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An Anomaly Detection Algorithm for Cathode Voltage of Aluminum Electrolytic Cell

  • Cao, Danyang;Ma, Yanhong;Duan, Lina
    • Journal of Information Processing Systems
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    • v.15 no.6
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    • pp.1392-1405
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    • 2019
  • The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuates greatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detection algorithm based on sliding window was proposed. The algorithm combines the time series segmentation linear representation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficient than the direct detection of the original sequence. The algorithm first segments the cathode voltage time series, then calculates the length, the slope, and the mean of each line segment pattern, and maps them into a set of spatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns according to the local anomaly factor and the pattern length. The experimental results showed that the algorithm can effectively detect the abnormal range of cathode voltage.

3D Environmental Walkthrough Using The Integration of Multiple Segmentation Based Environment Models (다중 분할 기반 환경 모델의 통합에 의한 3차원 환경 탐색)

  • Ryoo, Seung-Taek
    • The Journal of Korean Association of Computer Education
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    • v.8 no.1
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    • pp.105-115
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    • 2005
  • An environment model that is constructed using a single image has the problem of a blurring effect caused by the fixed resolution, and the stretching effect of the 3D model caused when information that does not exist on the image occurs due to the occlusion. This paper introduces the registration and integration method using multiple images to resolve the above problem. This method can represent parallax effect and expand the environment model to represent wide range of environment. The segmentation-based environment modeling method using multiple images can build a detail model with optimal resolution.

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Accurate Parked Vehicle Detection using GMM-based 3D Vehicle Model in Complex Urban Environments (가우시안 혼합모델 기반 3차원 차량 모델을 이용한 복잡한 도시환경에서의 정확한 주차 차량 검출 방법)

  • Cho, Younggun;Roh, Hyun Chul;Chung, Myung Jin
    • The Journal of Korea Robotics Society
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    • v.10 no.1
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    • pp.33-41
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    • 2015
  • Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

A Study on Segmentation of Building Points Utilizing Scan-line Characteristic of Airborne Laser Scanner (항공레이저측량 자료의 스캔라인 특성을 활용한 건물 포인트 분리에 관한 연구)

  • Han, Su-Hee;Lee, Jeong-Ho;Yu, Ki-Yun;Kim, Yong-Il;Lee, Byung-Kil
    • Journal of Korean Society for Geospatial Information Science
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    • v.13 no.4 s.34
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    • pp.33-38
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    • 2005
  • The goal of this study is to segment building points effectively utilizing scan-line characteristics of airborne laser scanner. Points are classified as to their altitude similarity and adjacency with other classified points, and point searching range for the classification is restricted within some number of scan-lines, preventing classification speed from lowering as the process goes on. Besides, we detected wrong discrimination of one object into more than two classes, then integrated them into a single class. Consequently we could discriminate points of each building from others, its annexes and none building points simultaneously.

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Benefits Sought and Knitwear Purchasing Behavior of Female College Students in the U.S. (미국 여대생의 의복 추구 혜택과 니트웨어 구매 행동)

  • Lee, Ok Hee
    • The Research Journal of the Costume Culture
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    • v.14 no.4
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    • pp.542-555
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    • 2006
  • The main objective of this study was to investigate the relationship between benefits segmentation and knitwear purchasing behavior of college female students in the U.S. The questionnaires for this survey were developed to measure knitwear purchasing behavior and benefits segmentation. The questionnaire was administered to 119 female college students in the University of California. The data was analyzed by percentage, frequency, mean, factor analysis, Cluster Analysis and ANOVA, Duncan Multiple Range test. The female college students in the U.S. were classified into fourth subdivisions by the cluster analysis. In the case of fashion information sources of knit wear, significant differences were found according to benefits sought subdivision in observation of famous people's clothing, fashion articles in magazines and newspapers, TV advertisements, Newspaper advertisements, advice of salespeople, and Catalogs. The evaluation criteria of knit wear product of consumers were significantly different depending on benefits sought subdivision in design/style, quality of construction, fashionable, brand and store name, pleasing to others, prestige, and sexy. The store attributes of knitwear product of consumers were significantly different depending on benefits sought subdivision in friendliness of sales personnel, product knowledge of sales personnel, brand names, new fashion, and variety of products. The outlook for the industry of knitwear look to remain bright, there should be a continuous effort to research and invest in consumer satisfaction of knitwear.

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A Study on Market Segmentations and Shopping Orientations of Home Shopping User: Based on Mixture Model (Mixture model에 의한 홈쇼핑 이용자 시장세분화와 쇼핑성향)

  • Seo, Jeong-Ah;Lee, Jin-Hwa;Hong, Jae-Won
    • Journal of the Korean Society of Clothing and Textiles
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    • v.32 no.7
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    • pp.1023-1033
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    • 2008
  • The purpose of this study was to segment home-shopper market by using the demographic characteristics. This study enables a better unders landing of home-shoppers and improving the strategy of marketing. The specific objects of this study are as follow: First, it was to exam market segmentations by demographic factors using mixture model. Second, it was to exam shopping orientations of fashion merchandise according to segmentation groups. The data was collected from 637 subjects who had used the home shopping more than one time in a year. The data was analysised through frequencies, factor analysis, ANOVA, Duncan's mutiple range tests with SPSS 12.0 and Mixture model. The results of data are as follows: 1. The result of market segmentation as demographic factor using Mixture model was extracted to 4 market segments called 20's/ unmarried stage, 30's/ children bearing & rearing stage, 40's/ families with children's education stage, 50's/ aging stage. 2. Shopping orientations were extracted to 5 factors called a pleasure oriented, convenience oriented, off-line oriented, human oriented, thrift oriented.

A Prostate Segmentation of TRUS Image using Average Shape Model and SIFT Features (평균 형상 모델과 SIFT 특징을 이용한 TRUS 영상의 전립선 분할)

  • Kim, Sang Bok;Seo, Yeong Geon
    • KIPS Transactions on Software and Data Engineering
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    • v.1 no.3
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    • pp.187-194
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    • 2012
  • Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease, transrectal ultrasound(TRUS) images are being used because the cost is low. But, accurate detection of prostate boundaries is a challenging and difficult task due to weak prostate boundaries, speckle noises and the short range of gray levels. This paper proposes a method for automatic prostate segmentation in TRUS images using its average shape model and invariant features. This approach consists of 4 steps. First, it detects the probe position and the two straight lines connected to the probe using edge distribution. Next, it acquires 3 prostate patches which are in the middle of average model. The patches will be used to compare the features of prostate and nonprostate. Next, it compares and classifies which blocks are similar to 3 representative patches. Last, the boundaries from prior classification and the rough boundaries from first step are used to determine the segmentation. A number of experiments are conducted to validate this method and results showed that this new approach extracted the prostate boundary with less than 7.78% relative to boundary provided manually by experts.

Cluster-Based Spin Images for Characterizing Diffuse Objects in 3D Range Data

  • Lee, Heezin;Oh, Sangyoon
    • Journal of Sensor Science and Technology
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    • v.23 no.6
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    • pp.377-382
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    • 2014
  • Detecting and segmenting diffuse targets in laser ranging data is a critical problem for tactical reconnaissance. In this study, we propose a new method that facilitates the characterization of diffuse irregularly shaped objects using "spin images," i.e., local 2D histograms of laser returns oriented in 3D space, and a clustering process. The proposed "cluster-based spin imaging" method resolves the problem of using standard spin images for diffuse targets and it eliminates much of the computational complexity that characterizes the production of conventional spin images. The direct processing of pre-segmented laser points, including internal points that penetrate through a diffuse object's topmost surfaces, avoids some of the requirements of the approach used at present for spin image generation, while it also greatly reduces the high computational time overheads incurred by searches to find correlated images. We employed 3D airborne range data over forested terrain to demonstrate the effectiveness of this method in discriminating the different geometric structures of individual tree clusters. Our experiments showed that cluster-based spin images have the potential to separate classes in terms of different ages and portions of tree crowns.

A new cyclopoid copepod from Korean subterranean waters reveals an interesting connection with the Central Asian fauna (Crustacea: Copepoda: Cyclopoida)

  • Karanovic, Tomislav;Yoo, Hyunsu;Lee, Wonchoel
    • Journal of Species Research
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    • v.1 no.2
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    • pp.156-174
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    • 2012
  • Monchenkocyclops gen. nov. was erected to accommodate M. changi sp. nov. from South Korea (type species), M. mirabdullayevi sp. nov. from Kazakhstan, M. biarticulatus (Monchenko, 1972) comb. nov. from Uzbekistan, and M. biwensis (Ishida, 2005) comb. nov. from Japan. The latter species was originally described from surface-water habitats of the ancient Lake Biwa in the genus Diacyclops Kiefer, 1927, while two Central Asian species were previously collected from groundwater habitats and assigned to the genus Acanthocyclops Kiefer, 1927. Monchenkocyclops changi is also found in subterranean waters, and described here in detail. It is morphologically most similar to its Uzbek congener (not to the Japanese one), which rises some interesting zoogeographical questions about the disjunct distribution of this genus. Range fragmentation is a more plausible explanation for this distribution pattern than range expansion, and we emphasize four lines of evidence that support this hypothesis. Four species of Monchenkocyclops share not only the same segmentation of the swimming legs, but also the exact same armature formula of all swimming legs, in addition to many other morphological characters, such as the caudal rami shape and armature, absence of exopod on the antenna, similar shape of the seminal receptacle, fifth leg, etc. They can be distinguished mostly by the relative length of different armature elements, such as the innermost terminal caudal setae, and inner setae and apical spines on the third endopodal segment of the fourth leg. A dichotomous key to species is provided.