SURF-based Mobile Object Image Recognition and Its Applications

• Main Authors: Jun-kai Hsiung, 熊竣凱
• Other Authors: Jia-Hong Lee
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/87561640535154214695

碩士 === 國立高雄第一科技大學 === 資訊管理研究所 === 101 === This study proposes two mobile object recognition methods based on SURF and were applied on the applications of beverage bottles and Real Estate DM, respectively. Users can use a mobile camera to shoot bottled beverage or pictures on Real Estate DM to obtain more information for the target objects. The results of the research can be extended and applied on mobile e-commerce and marketing purposes. Different to the current widespread use of QR Code, the proposed method can provide the same information services without adding extra-marks on the products. This developed mobile object recognition system is based on a Client-Server Architecture. In the client-side, the mobile device will capture target object images and then upload them to the Server-side. In the server-side, SURF based image feature extraction and image matching is performed. The matching results will send back to the client-side. In order to obtain a high performance of systems, two different preprocessing procedures for bottled drinks and Real Estate advertising were applied. In the application of bottled beverages, HSV color space transformation and K-means clustering are applied to increase the accuracy recognition rate. In the Real Estate DM application, BoF (Bag of features) is applied. The experimental results show that the proposed method can achieve high accuracy rates. The accuracy rate for packaged beverage application using the proposed method is 94% and the average execution time is 1.04 seconds; in the Realty Estate DM application, the accuracy rate is 100% and the average execution time is 5.21 seconds. Experimental results show that the proposed methods own a high degree of stability and possibility.