• The Journal of Korea Robotics Society
• /
• v.12 no.1
• /
• pp.33-41
• /
• 2017

We present a region-based approach for accurate pose estimation of small mechanical components. Our algorithm consists of two key phases: Multi-view object co-segmentation and pose estimation. In the first phase, we explain an automatic method to extract binary masks of a target object captured from multiple viewpoints. For initialization, we assume the target object is bounded by the convex volume of interest defined by a few user inputs. The co-segmented target object shares the same geometric representation in space, and has distinctive color models from those of the backgrounds. In the second phase, we retrieve a 3D model instance with correct upright orientation, and estimate a relative pose of the object observed from images. Our energy function, combining region and boundary terms for the proposed measures, maximizes the overlapping regions and boundaries between the multi-view co-segmentations and projected masks of the reference model. Based on high-quality co-segmentations consistent across all different viewpoints, our final results are accurate model indices and pose parameters of the extracted object. We demonstrate the effectiveness of the proposed method using various examples.

• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.399-408
• /
• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.2 s.40
• /
• pp.25-33
• /
• 2006

We propose a method that automatically classifies the images into the object and non-object images. An object image is the image with object(s). An object in an image is defined as a set of regions that lie around center of the image and have significant color distribution against the other surround (or background) regions. We define four measures based on the characteristics of an object to classify the images. The center significance is calculated from the difference in color distribution between the center area and its surrounding region. Second measure is the variance of significantly correlated colors in the image plane. Significantly correlated colors are first defined as the colors of two adjacent pixels that appear more frequently around center of an image rather than at the background of the image. Third one is edge strength at the boundary of candidate for the object. By the way, it is computationally expensive to extract third value because central objects are extracted. So, we define fourth measure which is similar with third measure in characteristic. Fourth one can be calculated more fast but show less accuracy than third one. To classify the images we combine each measure by training the neural network and SYM. We compare classification accuracies of these two classifiers.

• Journal of the Korean Institute of Rural Architecture
• /
• v.17 no.2
• /
• pp.9-16
• /
• 2015

This study comparatively analyzed thermal characteristics of the green color, which is currently used the most, and other various colors of a rooftop urethane water proofing sheet. This study also analyzed the cooling performance by color of the water proofing sheet that fused cooling paints, and presented the effective water proofing sheet color for building energy savings. The experimental results are as follows: (1) The value of L (brightness) diminished, and brilliance also became lower from the white color to the black color, and thus, it was confirmed that relatively more heat was absorbed. In a and b chromaticity, which is the color attribute that ignores brightness, no special relationship was identified. (2) Considering that the cooling performance effect is bigger in summer than winter, due to heat reflection, the white water proofing sheet is more effective in building energy savings than the green water proofing sheet that is currently used the most. (3) The water proofing sheet's color has an impact more on cooling performance than the color of the background side of a structure on which water proofing sheet is installed. The experiment object of gray, of which background side is similar to cement mortar, was lower by $5.7^{\circ}C$ than the white background side.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.4
• /
• pp.343-350
• /
• 2016

In this paper, We proposed a realtime smoke detection using hidden markov model and DWT. The smoke type is not clear. The color of the smoke, form, spread direction, etc., are characterized by varying the environment. Therefore, smoke detection using specific information has a high error rate detection. Dynamic Object Detection was used a robust foreground extraction method to environmental changes. Smoke recognition is used to integrate the color, shape, DWT energy information of the detected object. The proposed method is a real-time processing by having the average processing speed of 30fps. The average detection time is about 7 seconds, it is possible to detect early rapid.

• Journal of Korea Multimedia Society
• /
• v.12 no.7
• /
• pp.941-946
• /
• 2009

As supplying of automatic surveillance or patrol systems based on image processing, the needs on object extraction technology from images increases. The extraction is more difficult when the lighting condition is changed from time to time. There are many approaches to extract objects from images excluding shadow. They have a common problem something like loss of object region according with shadow removal. In this paper a restoration method using color information of objects to complement the problem is presented. The usefulness of the method is verified using images taken from different lighting conditions and selected from well-known DB.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.889-894
• /
• 2009

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance, and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., the proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.6
• /
• pp.731-736
• /
• 2008

In this paper, we propose the movement detection algorithm by using virtual skeleton model. To do this, first, we eliminate error values by using conventioanl method based on RGB color model and eliminate unnecessary values by using the HSI color model. Second, we construct the virtual skeleton model with skeleton information of 10 peoples. After matching this virtual model to original image, we extract the real head silhouette by using the proposed circle searching method. Third, we extract the object by using the mean-shift algorithm and this head information. Finally, we validate the applicability of the proposed method through the various experiments in a complex environments.

• Journal of Fashion Business
• /
• v.23 no.6
• /
• pp.1-15
• /
• 2019

Appropriation is of considerable significance in a cultural trend of thought, as one of the means of realizing the post-modernism period. With the increasing use of appropriation techniques in modern fashion, it is necessary to study the external performance and internal aesthetic value of appropriation in fashion. In the book of cultural appropriation, American scholar James o young divides into three categories of appropriation in culture, namely: object appropriation, content appropriation, and subject appropriation. Based on James O Young's three types of appropriation techniques summarized in the theory of the cultural appropriation, the purpose of this study is through the appropriation of the poncho of traditional Mexican clothing in modern fashion as an example; analyzing the external appropriation characteristics and internal aesthetic significance of different appropriation type. The results are as follows. First, designers take the Originality in modern fashion by expressing Mexican Poncho's form, color, pattern, and material as it is through object appropriation technique. Second, through the Mexican folk poncho's style, designers used these to show the similarity produced by content appropriation in modern fashion. Third, designers used the poncho's design concept or poncho's culture, blending the theme of the collection, adding different color, pattern or materials such as fur, lace, and wool, and presenting a new image different from folk costumes through creative subject appropriation technique.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11a
• /
• pp.669-672
• /
• 2002

최근 멀티미디어 기술의 급속한 발달로 인해 개인의 신원 확인, 보안 시스템 등의 영역에서 얼굴과 관련된 연구가 활발히 진행 되고 있다. 기존의 연구에서는 원거리 추적이 어려우며, 연산시간, 잡음(noise), 배경과 조명등에 따라 추적 효율이 낮은 단점을 가지고 있다. 본 논문에서는 빠르고 정확한 얼굴 추적을 위한 차 영상 기법(differential image method)을 이용한 분할영역(segmentation region)에서 움직임(motion)과 피부색(skin color) 특성 기반의 객체분할추적(Tracking Of Object segmentation) 방법을 이용하였다. 객체분할추적은 얼굴을 하나의 객체(object)로 인식하고 제안한 방법으로 얼굴 부분만 분할하는 단계와 얼굴특징추출 단계를 적용하여 피부색 기반의 연구에서 나타난 입력영상(Current Frame)에서의 유동적인 피부색의 노출 대한 얼굴 추적 연구의 문제점을 해결했다. 시스템은 현재 컴퓨터에 일반적으로 사용되는 카메라를 이용하여 구현 하였고, 실시간(real-time) 영상에서 비교적 성공적인 얼굴 추적을 하였다[4].