• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.157-169
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• 2013

Using a target detection task, this study compared the processing automaticity of phonemes and features in spoken syllable stimuli to determine the primitive representation in speech perception, phoneme or distinctive feature. For this, we modified the visual search task(Treisman et al., 1992) developed to investigate the processing of visual features(ex. color, shape or their conjunction) for auditory stimuli. In our task, the distinctive features(ex. aspiration or coronal) corresponded to visual primitive features(ex. color and shape), and the phonemes(ex. /$t^h$/) to visual conjunctive features(ex. colored shapes). The automaticity is measured by the set size effect that was the increasing amount of reaction time when the number of distracters increased. Three experiments were conducted. The laryngeal features(experiment 1), the manner features(experiment 2), and the place features(experiment 3) were compared with phonemes. The results showed that the distinctive features are consistently processed faster and automatically than the phonemes. Additionally there were differences in the processing automaticity among the classes of distinctive features. The laryngeal features are the most automatic, the manner features are moderately automatic and the place features are the least automatic. These results are consistent with the previous studies(Bae et al., 2002; Bae, 2010) that showed the perceptual hierarchy of distinctive features.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.940-943
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• 1996

In teleoperating, as seeing the monitor screen obtained from a camera instituted in the working environment, human operator generally controls the slave arm. Because we can see only 2-D image in a monitor, human operator does not know the depth information and can not work with high accuracy. In this paper, we proposed a traded control method using an visual sensor for the purpose of solving this problem. We can control a teleoperation system with precision when we use the proposed algorithm. Not only a human operator command but also an autonomous visual sensor feedback command is given to a slave arm for the purpose of coincidence current image features and target image features. When the slave arm place in a distant place from the target position, human operator can know very well the difference between the desired image features and the current image features, but calculated visual sensor command have big errors. And when the slave arm is near the target position, the state of affairs is changed conversely. With this visual sensor feedback, human does not need coincide the detail difference between the desired image features and the current image features and proposed method can work with higher accuracy than other method without, sensor feedback. The effectiveness of the proposed control method is verified through series of experiments.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1318-1330
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• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• Journal of Mechanical Science and Technology
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• v.16 no.6
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• pp.762-769
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• 2002

This paper presents how effective it is to use many features for improving the speed and accuracy of visual servo systems. Some rank conditions which relate the image Jacobian to the control performance are derived. The focus is to describe that the accuracy of the camera position control in the world coordinate system is increased by utilizing redundant features in this paper. It is also proven that the accuracy is improved by increasing the number of features involved. Effectiveness of the redundant features is evaluated by the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm robot manipulator made by Samsung Electronic Co. Ltd..

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.57-61
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• 2019

Nowadays, portable digital cameras such as smart phone cameras are being popularly used for entertainment and visual information recording. Given a database of geo-tagged images, a visual location recognition system can determine the place depicted in a query photo. One of the most common visual location recognition approaches is the bag-of-words method where local image features are clustered into visual words. In this paper, we propose a new bag-of-words-based visual location recognition algorithm using time-series streetview database. The proposed algorithm selects only a small subset of image features which will be used in image retrieval process. By reducing the number of features to be used, the proposed algorithm can reduce the memory requirement of the image database and accelerate the retrieval process.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.1
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• pp.27-34
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• 2014

This paper proposes a new approach to the designed of visual feedback control system based on visual servoing method. The main focus of this paper is presented how it is effective to use many features for improving the accuracy of the visual feedback control of industrial articulated robot for assembling and inspection of parts. Some rank conditions, which relate the image Jacobian, and the control performance are derived. It is also proven that the accuracy is improved by increasing the number of features. The effectiveness of redundant features is verified by the real time experiments on a SCARA type robot(FARA) made in samsung electronics company.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.1
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• pp.35-42
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• 2013

This paper proposes a new approach to the designed of visual feedback control system based on visual servoing method. The main focus of this paper is presented how it is effective to use many features for improving the accuracy of the visual feedback control of industrial articulated robot for assembling and inspection of parts. Some rank conditions, which relate the image Jacobian, and the control performance are derived. It is also proven that the accuracy is improved by increasing the number of features. The effectiveness of redundant features is verified by the real time experiments on a SCARA type robot(FARA) made in samsung electronics company.

• Journal of KIISE
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• v.44 no.12
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• pp.1313-1318
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• 2017

Aggregating local features in a single vector is a fundamental problem in an image search. In this process, the image search process can be speeded up if binary features which are extracted almost two order of magnitude faster than gradient-based features are utilized. However, in order to utilize the binary features in an image search, it is necessary to study the techniques for clustering binary features to generate binary visual words. This investigation is necessary because traditional clustering techniques for gradient-based features are not compatible with binary features. To this end, this paper studies the techniques for clustering binary features for the purpose of generating binary visual words. Through experiments, we analyze the trade-off between the accuracy and computational efficiency of an image search using binary features, and we then compare the proposed techniques. This research is expected to be applied to mobile applications, real-time applications, and web scale applications that require a fast image search.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1650-1654
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• 2004

This paper presents how it is effective to use many features for improving the speed and the accuracy of the visual servo systems. Some rank conditions which relate the image Jacobian and the control performance are derived. It is also proven that the accuracy is improved by increasing the number of features. Effectiveness of the redundant features is evaluated by the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm Robot manipulator made in Samsung Electronic Co. Ltd.