• Journal of Broadcast Engineering
• /
• v.19 no.2
• /
• pp.166-173
• /
• 2014

Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

• The KIPS Transactions:PartB
• /
• v.14B no.4
• /
• pp.263-272
• /
• 2007

This paper proposes a skeleton-based hierarchical shape description scheme, called a skeleton tree, for accurate shape-based image retrieval. A skeleton tree represents an object shape as a hierarchical tree where high-level nodes describe parts of coarse trunk regions and low-level nodes describe fine details of boundary regions. Each node refines the shape of its parent node. Most of the noise disturbances are limited to bottom level nodes and the boundary noise is reduced by decreasing weights on the bottom levels. The similarity of two skeleton trees is computed by considering the best match of a skeleton tree to a sub-tree of another skeleton tree. The proposed method uses a hybrid similarity measure by employing both Fourier descriptors and moment invariants in computing the similarity of two skeleton trees. Several experimental results are presented demonstrating the validity of the skeleton tree scheme for the shape description and indexing.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.7
• /
• pp.3272-3285
• /
• 2016

Recently, Vector of Locally Aggregated Descriptors (VLAD) has been proposed to index image by compact representations, which encodes powerful local descriptors and makes significant improvement on search performance with less memory compared against the state of art. However, its performance relies heavily on the size of the codebook which is used to generate VLAD representation. It indicates better accuracy needs higher dimensional representation. Thus, more memory overhead is needed. In this paper, we enhance VLAD image representation by using two level hierarchical-codebooks. It can provide more accurate search performance while keeping the VLAD size unchanged. In addition, hierarchical-codebooks are used to construct multiple inverted files for more accurate non-exhaustive search. Experimental results show that our method can make significant improvement on both VLAD image representation and non-exhaustive search.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.6_2
• /
• pp.643-651
• /
• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.7
• /
• pp.3599-3619
• /
• 2019

In human activity recognition system both static and motion information play crucial role for efficient and competitive results. Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components. Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor. First, static features are learned by two-stream 3D convolutional neural network. Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity. Then, shape and motion descriptors are obtained along with key points by using SIFT flow. Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors. Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction. To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e. UCF101, HMDB51 and YouTube. Findings shows that the resulting recognition system is on par with state-of-the-art methods.

• Proceedings of the Korea Contents Association Conference
• /
• 2013.05a
• /
• pp.43-44
• /
• 2013

In my paper, an evaluation method on naturalness and urbanness characterizations of animation image is proposed. In contrast to the prior works, we intend to give the exact degree of naturalness and urbanness by combining the low-level visual feature-based schemes with the biological visual schemes. In our method, four descriptors are extracted and pseudoinverse is adopted. By this method, we can experiment on the exact degree of naturalness and urbanness with satisfactory results.

• Journal of Korean Elementary Science Education
• /
• v.43 no.2
• /
• pp.284-300
• /
• 2024

This study elucidates the achievement standards statements of the 2022 revised elementary school science curriculum to identify specific achievement standards for the upcoming curriculum. Therefore, the researcher analyzed the statements of the overall elementary school achievement standards based on Bloom's taxonomy of new educational objectives. The results are as follows. First, the achievement standards statements are biased toward certain knowledge and cognitive process dimensions; this aspect is not consistent with the goals of the 2022 revised curriculum and the teaching and learning directions of the science department. Thus, achievement standards that enable various types of activities and inquiry learning should be developed. Second, a need emerges for the hierarchization of knowledge and cognitive levels by grade level. The proportions of low levels of knowledge and cognitive process dimensions increased in the upper grades, such that a systematic hierarchy should be considered. Third, the need to diversify the use of the descriptors of achievement standards is also identified. Although the tendency to rely on specific descriptors decreased during the previous curriculum, approx imately half of the descriptors were only used once or twice. Therefore, balancing the use of various descriptors is necessary. To ensure that the results are reflected in the achievement standards for elementary school science textbooks under the revised science curriculum for elementary schools in 2022, a discussion is required on the design of achievement standards statements. As a follow-up study, the researcher proposes a comparative analysis of the achievement standards of science curricula for middle and high schools to explore the wording of achievement standards appropriate for elementary school science education considering its nature, goals, and contents and to analyze the hierarchy and continuity of the entire science curriculum.

• Journal of the Korea Safety Management & Science
• /
• v.11 no.4
• /
• pp.153-159
• /
• 2009

This study was conducted in search of the acceptance level of air traffic services from domestic airlines pilot's perspective in comparison with male controllers and female controllers. Pilots responded to the questionnaire that female ATC controllers are of significance to male controllers in terms of pronunciation, accuracy of English grammar, attitude and kindness. Besides, The ICAO aviation English proficiency level four test revealed that female controllers were found superior to male controllers in terms of rating scales of holistic descriptors.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.4
• /
• pp.322-329
• /
• 2005

This paper considers the application of mobile robot to the herding problem. The herding problem involves a ‘pursuer’ trying to herd a moving ‘evader’ to a predefined location. In this paper, two mobile robots act as pursuer and evader in the fenced area, where the pursuer robot uses a fuzzy cooperative decision strategy (FCDS) in the herding algorithm. To herd evader robot to a predefined position, the pursuer robot calculates strategic herding point and then navigates to that point using FCDS. FCDS consists of a two-level hierarchy: low level motion descriptors and a high level coordinator. In order to optimize the FCDS, we use the multi­thread evolutionary programming algorithm. The proposed algorithm is implemented in the real mobile robot system and its performance is demonstrated using experimental results.

• Journal of Broadcast Engineering
• /
• v.7 no.2
• /
• pp.96-106
• /
• 2002

We introduce a novel scheme to characterize and index events in TV golf programs using MPEG-7 descriptors. Our goal is to identify and localize the golf events of interest to facilitate highlight-based video indexing and summarization. In particular, we analyze multiple (low-level) visual features using domain-specific model to create a perceptual relation for semantically meaningful(high-level) event identification. Furthermore, we summarize a TV golf program with TV-Anytime segmentation metadata, a standard form of an XML-based metadata description, in which the golf events are represented by temporally localized segments and segment groups of highlights. Experimental results show that our proposed technique provides reasonable performance for identifying a variety of golf events.