• Annual Conference of KIPS
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• 2019.10a
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• pp.1101-1104
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• 2019

키넥트는 RGBD 카메라로 인체의 뼈대와 관절을 3D 공간에서 스켈레톤 데이터수집을 가능하게 해주었다. 스켈레톤 데이터를 활용한 행동 분류는 RNN, CNN 등 다양한 인공 신경망으로 접근하고 있다. 본 연구는 키넥트를 이용해서 Skeleton Joints를 수집하고, DNN 기반 스켈레톤 모델링 학습으로 행동을 분류한다. Skeleton Joints Processing 과정은 키넥트의 Depth Map 기반의 Skeleton Tracker로 25가지 Skeleton Joints 좌표를 얻고, 학습을 위한 전처리 과정으로 각 좌표를 상대좌표로 변경하고 데이터 수를 제한하며, Joint가 트래킹 되지 않은 부분에 대한 예외 처리를 수행한다. 스켈레톤 모델링 학습 과정에선 3계층의 DNN 신경망을 구축하고, softmax_cross_entropy 함수로 Skeleton Joints를 집는 모션, 내려놓는 모션, 팔짱 낀 모션, 얼굴을 가까이 가져가는 모션 해서 4가지 행동으로 분류한다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.362-363
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• 2012

본 논문에서는 한 장의 실제 나무 영상이 주어졌을 시, 사실적인 3차원 나무 모델링(modeling) 및 자가생장(self-growth) 표현을 위한 방법을 소개하도록 한다. 스켈레톤기반의 간략화(skeleton-based abstraction)를 이용하여 동일한 나무 몸통(trunk)을 갖는 다양한 나무 모델생성과 함께 나무의 다면체구조(manifold structure)를 고려한 지오데식 커널(geodesic kernel)을 이용하여 나무의 자가생장을 표현한다. 나무의 자가생장은 사전 정의된 나무 굵기, 전체 크기, 그리고 가지증식 순서정보와 같은 상대적 성장정보(allometric information)를 동시 이용하여 상대적인 나무 생장(allometric tree growth)을 표현하도록한다. 한편, 보여지지않는 나무 가지와 잎들에 대해선, 나무구조는 로컬하게 자기유사성(local self-similarity)을 갖는다라는 고전적인 절차적(conventional procedural) 가정을 이용하여 자동적으로 생성토록한다. 실제영상을 이용한 몇몇들의 실험을 통해 보다 효과적으로 나무 모델 및 생장 표현이 가능함을 보여주도록한다.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.133-140
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• 2005

It is important matter to develop game that language should consider improving game development environment. It is hardly any language that is designed to develop a game. Most developer used general purpose programming language in these situations, and directly modeling with engine. For this reasons, developers cannot easily develop games and must programming on engine level. It is hard to develop a game in this environment that was not able to understand an engine well. Moreover, developer and planner want to use an abstracted high-level language on planner phase and are going to easily develop a game. It was not have communication tool between developer and planner. Therefore, we defined a script language for modeling based on abstract engine level. In addition, we did build IDE to develop game using abstracted high-level language. It was able to develop a game on high-speed development environment. Therefore, developer does not must develop with engine phase but can develop a game with planners. In addition, game planner and developer can use a communication tool because it is able to develop skeleton game.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.