• Archives of design research
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• v.21
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• pp.77-86
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• 1997

Modern society has created the age of mass media image. Advertising media strongly stimulate and influence the image contact and the frequency of receivers by visual communication media in everyday life. Until today no consciousness concerning the necessity of typographic visual image and it's selection for newspaper advertising has been developed. The necessity of headline typography visual image in newspaper advertising and it's selection should be considered a problem that must be solved by a more reasonable and logical approach, like it is done in the case of advertising copy, in order to increase the efficiency of advertising for the consumer.

• Korean Journal of Plant Taxonomy
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• v.47 no.2
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• pp.124-131
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• 2017

Species identification is a fundamental and routine process in plant systematics, and linguistic-based dichotomous keys are widely used in the identification process. Recently, novel tools for species identification have been developed to improve the accuracy, ease to use, and accessibility related to these tasks for a broad range of users given the advances in information and communications technology. A visual identification key is such an approach, in which couplets consist of images of plants or a part of a plant instead of botanical terminology. We developed a visual identification key for 101 taxa of Orchidaceae in Korea and evaluated its performance. It uses short statements for image couplets to avoid misinterpretations by users. The key at the initial steps (couplets) uses relatively easy characters that can be determined with the naked eye. The final steps of the visual key provide images of species and information about distributions and flowering times to determine the species that best fit the available information. The number of steps required to identify a species varies, ranging from three to ten with an average of 4.5. A performance test with senior college students showed that species were accurately identified using the visual key at a rate significantly higher than when using a linguistic-based dichotomous key and a color manual. The findings presented here suggest that the proposed visual identification key is a useful tool for the teaching of biodiversity at schools, for the monitoring of ecosystems by citizens, and in other areas that require rapid, easy, and accurate identifications of species.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.319-334
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• 2001

Extracting primitives from imagery plays an important task in visual information processing since the primitives provide useful information about characteristics of the objects and patterns. The human visual system utilizes features without difficulty for image interpretation, scene analysis and object recognition. However, to extract and to analyze feature are difficult processing. The ultimate goal of digital image processing is to extract information and reconstruct objects automatically. The objective of this study is to develop robust method to achieve the goal of the image processing. In this study, an adaptive strategy was developed by implementing Gabor filters in order to extract feature information and to segment images. The Gabor filters are conceived as hypothetical structures of the retinal receptive fields in human vision system. Therefore, to develop a method which resembles the performance of human visual perception is possible using the Gabor filters. A method to compute appropriate parameters of the Gabor filters without human visual inspection is proposed. The entire framework is based on the theory of human visual perception. Digital images were used to evaluate the performance of the proposed strategy. The results show that the proposed adaptive approach improves performance of the Gabor filters for feature extraction and segmentation.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.2
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• pp.269-282
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• 1998

The visual preference of interiors focusing on lobbies was investigated as a function of six predictor variable on the base of the Informational Approach: complexity, coherence, mystery, spaciousness, brightness, plant. The Common Fcator Analysis of preference ratings yielded six common factors which helped to account for 22.3 percent of the variance in preference response to the scene. Among these factors, the factor defined as 'bright with many plants' was the most preferred and the factor defined as 'simple and closed' was the least preferred. The environmental attributes reflected in six groups of scenes were colour, resting place, window and the six predictors. In the commercial building scenes, complexity, spaciousness, coherence, brightness and mystery out of six predictors accounted for 74 percent of preference variance as the significant contributors. In the business building scenes, three predictors which are brightness, complexity, spaciousness accounted for 84 percent of preference variance. 'The amount of plant' not only influenced the preference indirectly through the intervening variable, complexity, but also was moderately correlated with brightness. The overall pattern of the resulted confirmed the usefulness of the Informational Approach to predict the preference in interiors focusing on lobbies.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1183-1195
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• 2020

Visual saliency detection is an essential task because it is an important part of various vision-based applications. There are many techniques for saliency detection in color images. However, the number of methods for saliency detection in infrared images is limited. In this paper, we introduce a simple approach for saliency detection in infrared images based on the thresholding technique. The input image is thresholded into several Boolean maps, and an initial saliency map is calculated as a weighted sum of the created Boolean maps. The initial map is further refined by using thresholding, morphology operation, and a Gaussian filter to produce the final, high-quality saliency map. The experiment showed that the proposed method has high performance when applied to real-life data.

• Current Optics and Photonics
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• v.4 no.3
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• pp.200-209
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• 2020

We describe an approach that uses a bilateral filter to reduce the ringing artifact in motion-blurred restoration image. It takes into account the specific physical structure of the ringing artifact combined with the properties of the human visual system. To properly reduce the ringing artifact, each of the adjacent pixels is limited in a straight line which has a given direction. To protect the edges and the texture regions of an image, our algorithm divides the image into texture regions and flat regions, and the artifact reduction algorithm is only applied to the flat region. Finally, we use 8 typical images and 5 objective quality evaluation indices to evaluate our algorithm. Experimental results show that our algorithm can obtain better results in subjective visual effect and in objective image quality evaluation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.8-16
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• 2014

We consider the visual barcode recognition problem in a noisy video data setup. Unlike most existing single-frame recognizers that require considerable user effort to acquire clean, motionless and blur-free barcode signals, we eliminate such extra human efforts by proposing a robust video-based barcode recognition algorithm. We deal with a sequence of noisy blurred barcode image frames by posing it as an online filtering problem. In the proposed dynamic recognition model, at each frame we infer the blur level of the frame as well as the digit class label. In contrast to a frame-by-frame based approach with heuristic majority voting scheme, the class labels and frame-wise noise levels are propagated along the frame sequences in our model, and hence we exploit all cues from noisy frames that are potentially useful for predicting the barcode label in a probabilistically reasonable sense. We also suggest a visual barcode tracking approach that efficiently localizes barcode areas in video frames. The effectiveness of the proposed approaches is demonstrated empirically on both synthetic and real data setup.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.851-854
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• 2000

Digital halftoning is a technique to make an equivalent binary image from scanned photo or graphic images. Low pass filtering characteristic of human visual system can be applied to get the effect of spatial averaging of local area consisted of black and white pixels for gray image. The overlapping of black dot decreases brightness and black dot is very sensitive to human visual system in the bright region. In this paper, for gray-level expression, only bright gray region in the color image is considered for blue noise mask (BNM) approach. To solve this problem, BNM with CMY dot is used for the bright region instead of black dot. Dot-on-dot model with single mask causes the problem making much black dot overlap, color distortion. Therefore approach with three masks for C, M and Y each is proposed to decrease pixel overlap and color distortion.

• Journal of Korean Neurosurgical Society
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• v.45 no.6
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• pp.375-377
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• 2009

A 49-year-old woman presented with headache, vomiting and visual disturbance. Neurological examination revealed bitemporal hemianopsia with poor visual acuity. Magnetic resonance imaging showed a bulky intra-suprasellar mass, which was isointense with brain parenchyma on T1-weighted images, and slightly hyperintense on T2-weighted images. After gadolinium administration, the mass was homogeneously enhanced. The mass was partially removed by the endonasal transsphenoidal approach and then the remnant mass was totally removed by the transcranial approach five months later. We found a yellowish mass which was attached to the diaphragm sellae in operation field. Histopathological examination of the tumor revealed the characteristic features of a schwannoma. We report an unusual case of an intra-suprasellar schwannoma resembling a non-functioning pituitary macroadenoma both clinically and radiologically.

• ETRI Journal
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• v.41 no.4
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.