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Training-Free Object Matching and Retrieval Using Speeded Up Robust Features | ||
| Journal of Computing and Security | ||
| مقاله 5، دوره 2، شماره 2، تیر 2015، صفحه 141-153 اصل مقاله (3.1 M) | ||
| نویسندگان | ||
| Reza Nasiripour؛ Hassan Farsi* ؛ Sajad Mohamadzadeh | ||
| چکیده | ||
| Traditionally, object retrieval methods require a set of images of a specific object for training. In this paper, we propose a new object retrieval method using a single query image, without training, for a global object. The query image could be a typical real image of the object. The object is constructed based on Speeded Up Robust Features (SURF) points acquired from the image. Information of relative positions, scale and orientation between SURF points are calculated and constructed into an object model. The ability to match partially affine transformed object images results from the robustness of SURF points and the flexibility of the model. Occlusion is handled by specifying the probability of a missing SURF point in the model. Experimental results show that this matching technique is robust under partial occlusion and rotation. The obtained results illustrate that the proposed method improves efficiency, speeds up recovery and reduces the storage space. | ||
| کلیدواژهها | ||
| Object retrieval؛ Speeded Up Robust Features (SURF)؛ Training-Free | ||
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آمار تعداد مشاهده مقاله: 9,236 تعداد دریافت فایل اصل مقاله: 596 |
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