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Camera-based document image retrieval is a task of searching document images from the database based on query images captured using digital cameras. For this task, it is required to solve the problem of " perspective distortion " of images, as well as to establish a way of matching document images efficiently. To solve these problems we have proposed a(More)
This paper presents a new method of indexing and retrieval of planar objects based on feature points and its application to document image retrieval using cameras. As the indexing method we propose a method based on local combinations of projective invariants calculated from feature points. As the retrieval method we employ a voting technique for efficiency(More)
We demonstrate how information about eye blink frequency and head motion patterns derived from Google Glass sensors can be used to distinguish different types of high level activities. While it is well known that eye blink frequency is correlated with user activity, our aim is to show that (1) eye blink frequency data from an unobtrusive, commercial(More)
In this paper, we propose improvements of our camera-based document image retrieval method with Locally Likely Arrangement Hashing (LLAH). While LLAH has high accuracy , efficiency and robustness, it requires a large amount of memory. It is also required to speed up the retrieval of LLAH for applications to real-time document image retrieval. For these(More)
We propose a method of document image retrieval using digital cameras. The proposed method takes as input a part or the whole of a document acquired as a query by a digital camera, and retrieves a document image that includes the query. For this purpose, it is required to solve the problem of " perspective distortion " of images, as well as to establish a(More)
—We have introduced the three improvements of Locally Likely Arrangement Hashing (LLAH) in ICDAR2011 to reduce a required amount of memory and increase discrimination power of features. In this paper, we show the experimental results which is obtained on a larger-scale database than that utilized for ICDAR2011. From experimental results, we have confirmed(More)
—This paper presents a real-time document image retrieval method for a large-scale database with Locally Likely Arrangement Hashing (LLAH). In general, when a database is scaled up, a large amount of memory is required and retrieval accuracy drops due to insufficient discrimination power of features. To solve these problems, we propose three improvements:(More)