• Korean Institute of Interior Design Journal
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• v.21 no.5
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• pp.77-84
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• 2012

Single formative language created by the standardization of industrial society carries a cell constructive aspect. Such space structural form made gigantic buildings, which has a symbolism as an independent object. Such space shows a morphological symbolism by public preference but it did not establish essential meaning of a shape or concentrated relation. In this regard, this paper tries to show organic similarity of structural formality of small commercial space (patterned space), which was made by the continuity of concentrated patterns not an object of dualistic unit features. Therefore, this study analyzed the cultural, commercial and public space based upon systematical concept and features. Systematical space formality that makes multilateral relation between human, environment and a thing is a concentrated view point by relational features not by the cluster displayed by hierarchical features. Systematical space of small patterned space emphasized its appropriateness of expansion and creating diversified spaces unlike gigantic symbolic buildings.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.135-142
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• 2003

Robust extraction of 3D object's features, shape and global motion information from 2D image sequence is described. The object's 21 feature points on the pyramid type synthetic object are extracted automatically using color transform technique. The extracted features are used to recover the 3D shape and global motion of the object using stereo paraperspective camera model and sequential SVD(Singuiar Value Decomposition) factorization method. An inherent error of depth recovery due to the paraperspective camera model was removed by using the stereo image analysis. A 30 synthetic object with 21 features reflecting various position was designed and tested to show the performance of proposed algorithm by comparing the recovered shape and motion data with the measured values.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.692-704
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• 2002

In this paper, we present am image retrieval method based on color-spatial features using quadtree and texture information extracted from object MBRs in an image. Tile proposed method consists of creating a DC image from an original image, changing a color coordinate system, and decomposing regions using quadtree. As such, conditions are present to decompose the DC image, then the system extracts representative colors from each region. And, image segmentation is used to search for object MBRs, including object themselves, object included in the background, or certain background region, then the wavelet coefficients are calculated to provide texture information. Experiments were conducted using the proposed similarity method based on color-spatial and texture features. Our method was able to refute the amount of feature vector storage by about 53%, but was similar to the original image as regards precision and recall. Furthermore, to make up for the deficiency in using only color-spatial features, texture information was added and the results showed images that included objects from the query images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.12
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• pp.1465-1474
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• 1992

This paper describes a new overlapped object recognition method using extended local features. At first, we extract the extended local features consisting of corners, arcs, parallel-lines, and corner-arcs from the images consisting of model objects. Based on the extended local features we construct a knowledge-base. In order to match objects, we also extract the extended local features from the input image, and then check the compatibility between the extracted features and the features in the knowledge-base. From the set of compatible features, we compute geometric transforms. If any geometric transforms are clustered, we generate the hypothesis of the objects as the centers of the clusters, and then verify the hypothesis by a reverse geometric transform. An experiment shows that the proposed method increases the recognition rate and the accuracy as compared with existing methods.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.45-54
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• 2009

In this paper we propose a new approach that recognizes the similar categories by weighting distinctive features. The approach is based on the PLSA that is one of the effective methods for the object categorization. PLSA is introduced from the information retrieval of text domain. PLSA, unsupervised method, shows impressive performance of category recognition. However, it shows relatively low performance for the similar categories which have the analog distribution of the features. In this paper, we consider the effective object categorization for the similar categories by weighting the mainly distinctive features. We present that the proposed algorithm, weighted PLSA, recognizes similar categories. Our method shows better results than the standard PLSA.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.263-269
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• 2008

Visual odometry is a popular approach to estimating robot motion using a monocular or stereo camera. This paper proposes a novel visual odometry scheme using a stereo camera for robust estimation of a 6 DOF motion in the dynamic environment. The false results of feature matching and the uncertainty of depth information provided by the camera can generate the outliers which deteriorate the estimation. The outliers are removed by analyzing the magnitude histogram of the motion vector of the corresponding features and the RANSAC algorithm. The features extracted from a dynamic object such as a human also makes the motion estimation inaccurate. To eliminate the effect of a dynamic object, several candidates of dynamic objects are generated by clustering the 3D position of features and each candidate is checked based on the standard deviation of features on whether it is a real dynamic object or not. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with both IMU and wheel-based odometry. It is shown that the proposed scheme works well when wheel slip occurs or dynamic objects exist.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.505-514
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• 2002

In this Paper, we propose a method of content-based image classification using neural network. The images for classification ate object images that can be divided into foreground and background. To deal with the object images efficiently, object region is extracted with a region segmentation technique in the preprocessing step. Features for the classification are texture and shape features extracted from wavelet transformed image. The neural network classifier is constructed with the extracted features and the back-propagation learning algorithm. Among the various texture features, the diagonal moment was more effective. A test with 300 training data and 300 test data composed of 10 images from each of 30 classes shows correct classification rates of 72.3% and 67%, respectively.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.2
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• pp.63-70
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• 1997

This paper proposes a new algorithm for recognizing polyhedral objects using a single 2-D image. It is base don a new representation scheme having two level hierarchey. In the lower level, geometrical features of each primitive surface are represented using their signatures and the variation of signature due to rotation is represented suing the rotation map. In the higher level, topological features are represented in the inter-surface description table(SDT). Based on the proposed representaton scheme, loer level database searched to find a matching primitive surface. The srotation map determines the degree of rotation as well as the matchness. If all surfaces in a test object find their matching primitive surfaces, its structural information is compared with the SDTs of object models. If primitive surfaces of a test object equal to tha tof certain model and satisfy inter-surfaces relationship in SDT, a test object is recognized as the model.