• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.17-28
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• 2019

The touchscreen device is now commonly found in the form of mobile phones, tablet PCs, and other devices. Varied physical and visual experiences can be experienced through touchscreens. This study intended to explore how the physical and visual experiences provided by the touchscreen would affect people through their existing associations of behavior-attitude. Previous studies have found that certain behaviors affect attitudes. In particular, the approach-avoidance behavior has been noted to influence both social and personal attitudes. It was thus deemed necessary to ascertain the approach-avoidance effect exerted by touchscreens on the attitudes of users as the technology is widely used today. Experiment 1 provided an approach-avoidance experience via a touchscreen and demonstrated that touchscreen-based approach-avoidance dragging behavior on the touchscreen can affect a user's preference and purchase intent. It was found that a product that had been approached showed both higher preference and higher purchase intent than a product that had been avoided. Experiment 2 investigated whether a similar effect would occur when only the visual experience of approach-avoidance was provided. The outcome proved that products that had been visually approached had higher scores than products that had been avoided, both in terms of preference and purchase intent. The movement of the arm on the touchscreen (Experiment 1) and the visual perception of the approach-avoidance experience (Experiment 2) were both shown to influence participants' attitudes toward products. The results of this study suggest that the behavior and perception of users may be an important factor in designing touchscreen interfaces for online shopping.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.550-555
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• 1993

The aim of this paper is to present the development of visual seam tracking system equipped with visual range finder. The visual range finder, which consists of a CCD camera and a diode laser system with line generating optics, developed to recognize the types of weld joints and detect the location of weld joints. In practical applications, however, images of the weld joints are often degraded due to spatters, are flares, surface specularity, and welding smoke. To overcome the problem, this paper proposes a syntactic approach which is a class of artificial intelligence techniques. In the approach, the type of weld joint is inferred based upon the production rules which are linguiques grammars consisting of a set of line and junction primitives of laser strip image projected on weld joint. The production rules eliminate several noisy primitives to create new primitives through the merging process of primitives. After the recognition of weld joint, arc welding is started and the location of weld joints is repeatedly detected using a spring model-based template matching in which the template model is a by-product of the recognition process of weld joint. To show the effectiveness of the proposed approach a series of experiments-identification and robotic tracking-are conducted for four different types of weld joints.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.555-564
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• 2016

Space perception is generally treated as a problem relevant to the ability to recognize objects. Alternatively, the data from shape perception studies contributes to discussions about the geometry of visual space. This geometry is generally acknowledged not to be Euclidian, but instead, elliptical, hyperbolic or affine, which is to say, something that admits the distortions found in so many shape perception studies. The purpose of this review article is to understand perceived shape and the geometry of visual space in the context of visually guided action. Thus, two prominent approaches that explain the relation between perception and action were compared. It is important to understand the fundamental information of how human perceive visual space and perform visually guided action for the convergence on embodied cognition, and further on artificial intelligence researches.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.165-166
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• 2008

• 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 Costume
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• v.65 no.8
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• pp.77-94
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• 2015

Much western philosophical thinking is constrained by ocularcentrism. Hence, multi-sensory experiences hold the most prominent position in contemporary art, and visual tactility, where feelings of touch through sight, become a powerful medium of expression in fashion. This study analyzes visual tactility in Alexander McQueen's design, where design elements are in focus. This investigation aims to bring the concept of visual tactility in fashion, and by doing so, enhance valuable aesthetic possibility. The result of the study is as follows: First, Creative silhouette is something that is constructed by his impeccable cutting and tailoring skills. In fabrics, he often suggests unconventional materials by uncompromising approach to fashion. Color contrasts refer to color sensibility, which reminds of grotesque image. McQueen's aggressive aesthetic awareness results in visual tactility in fashion.

• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.782-788
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• 2000

In this paper, we present a new approach to visual feedback control using image-based visual servoing with stereo vision. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only around a desired location but also at other locations. The suggested technique can guide a robot manipulator to the desired location without providing a priori knowledge such as the relative distance to the desired location or the model of an object even when the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by experimental results and compared with conventional control methods for an assembly robot.

• 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 Korea Multimedia Society
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• v.23 no.10
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• International Journal of Contents
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• v.4 no.2
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.