• Korean Institute of Interior Design Journal
• /
• v.19 no.5
• /
• pp.48-55
• /
• 2010

Marcel Wanders, one of the greatest designers in the world of contemporary design, was born in the Netherlands. His works run the gamut from interior design to furniture design to lighting design, building a unique world of works. He started to gain fame when he presented "Knotted Chair" at Droog Design in 1996, which was made out of aramid ropes and later became his symbol. In 2000, he established "moooi," a world-renowned design label. By giving characteristic qualities, his works are given meaning, and like a fantastical dream, their images are extremely fantastical and stimulating. As can be seen in his character cover, he puts emphasis on the harmony between minimalism and decoration, establishing his own unique design concept. In this thesis, based on Marcel Wander's design philosophy, his overall design characteristics were classified into theatrical effects and storytelling. Expressive elements depaysement, eclectic mixture, and scale modification were derived from theatrical effects and analyzed; for storytelling, object, semantic cues, and dream and fantasy were derived and analyzed. A distinguishing feature of such analysis is his meaning-centric design approach, the principle by which to form long-term relationships with the users by creating user-centric designs that make them find meaning and values in diverse experiences in their daily routine, giving them familiar yet unique experience.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.3
• /
• pp.1264-1274
• /
• 2015

Human action recognition received many interest in the computer vision community. Most of the existing methods focus on either construct robust descriptor from the temporal domain, or computational method to exploit the discriminative power of the descriptor. In this paper we explore the idea of using local action attributes to form an action descriptor, where an action is no longer characterized with the motion changes in the temporal domain but the local semantic description of the action. We propose an novel framework where introduces local action attributes to represent an action for the final human action categorization. The local action attributes are defined for each body part which are independent from the global action. The resulting attribute descriptor is used to jointly model human action to achieve robust performance. In addition, we conduct some study on the impact of using body local and global low-level feature for the aforementioned attributes. Experiments on the KTH dataset and the MV-TJU dataset show that our local action attribute based descriptor improve action recognition performance.

• Journal of the Korean Society of Costume
• /
• v.65 no.4
• /
• pp.19-30
• /
• 2015

In recent years, various studies about 'Biomorphic design' have been conducted and accelerated among many recent design concepts and methodology. Therefore, this study classifies the types of biomorphic fashion design based on literature review, and select biomorphic fashion designs in the latest fashion designer's collection. This study aimed to determine the types and characteristics of the biomorphic design in fashion design, and analyze the characteristics and the interpreted intrinsic meanings through Greimas Semiotic rectangle model based on the Binary-Opposition of meaning and Isotophy. As the result of analysis, biomorphic designs in fashion are classified as three types: 'representational imitation of form', 'technical imitation of functional features', and 'imitation of symbolic attribute'. 'Representational imitation of form' was derived from an organic design through atypical forms, repetition and extension of figurative forms of nature, and 'the functionalities of the nature' are interpreted as the feature to maintain the condition of the life itself and to attempt to regulate the status of self-autonomy. Lastly, 'the imitation of symbolic attributes' is designing the process of creation, growth, expansion and destruction from circulation of nature.

• Korean Institute of Interior Design Journal
• /
• v.22 no.1
• /
• pp.183-193
• /
• 2013

The study aimed to find the fundamental differences of aesthetics in Korea, China, and Japan by analyzing expressive features and structural meanings of the storage furniture from $17^{th}$ to $19^{th}$ century. The study was performed in four steps; analysis of expressive features, isotopic analysis, semantic structure analysis, and comprehensive interpretation. The results showed that three countries had linear shapes with curvilinear elements, closed forms with open spaces, natural material hues with change of tone or color, and symmetrical forms with asymmetrical patterns and structures in common. Korea comparatively accented on the natural material colors and wood grains. China stressed on the big and wide faces using three-dimensional carving. Japan accented on the linear elements with strong color contrast and decorative metal fixtures. These features were caused by the traditional thoughts and according aesthetic principles. Korea and China were affected by the Confucianism focused on establishing the order of rank. Meanwhile, Japan was more influenced by the Buddhism emphasized on the individuality and communication. Therefore, the differences of the expressive features in furniture among the three countries were inevitable consequences of the different ideologies.

• The KIPS Transactions:PartB
• /
• v.19B no.2
• /
• pp.135-148
• /
• 2012

This paper proposes a model for recognizing sentential paraphrases through Predicate-Argument Tuple (PAT)-based approximate alignment between two texts. We cast the paraphrase recognition problem as a binary classification by defining and applying various alignment features which could effectively express the semantic relatedness between two sentences. Experiment confirmed the potential of our approach and error analysis revealed various paraphrase patterns not being solved by our system, which can help us devise methods for further performance improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4080-4097
• /
• 2020

In recent years, convolutional neural network (CNN) has become the primary method for face detection. But its shortcomings are obvious, such as expensive calculation, heavy model, etc. This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities. Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet. Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision. First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels. Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features. Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment. Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision. Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%. For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.1
• /
• pp.213-231
• /
• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

• Journal of Biomedical Engineering Research
• /
• v.27 no.4
• /
• pp.170-179
• /
• 2006

The purpose of this study was to investigate the spatiotemporal cortical activation pattern and functional connectivity during visual perception of words. 61 channel recordings of electroencephalogram were obtained from 15 subjects while they were judging the meaning of Korean, English, and Chinese words with concrete meanings. We examined event-related potentials (ERP) and applied independent component analysis (ICA) to find and separate simultaneously activated neural sources. Spectral analysis was also performed to investigate the gamma-band activity (GBA, 30-50 Hz) which is known to reflect feature binding. Five significant ERP components were identified and left hemispheric dominance was observed for most sites. Meaningful differences of amplitudes and latencies among languages were observed. It seemed that familiarity with each language and orthographic characteristics affected the characteristics of ERP components. ICA helped confirm several prominent sources corresponding to some ERP components. The results of spectral and time-frequency analyses showed distinct GBAs at prefrontal, frontal, and temporal sites. The GBAs at prefrontal and temporal sites were significantly correlated with the LPC amplitude and response time. The differences in spatiotemporal patterns of GBA among languages were not prominent compared to the inter-individual differences. The gamma-band coherence revealed short-range connectivity within frontal region and long-range connectivity between frontal, posterior, and temporal sites.

• Journal of Information Processing Systems
• /
• v.5 no.3
• /
• pp.159-166
• /
• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.659-666
• /
• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.