Indexing for local appearance-based recognition of planar objects
TL;DR: An optimal feature extraction technique that selects only the salient features of an object that exploits the fact that features tend to form clusters in the feature space based on their similarity of appearances is proposed.
About: This article is published in Pattern Recognition Letters.The article was published on 2002-01-01. It has received 3 citations till now. The article focuses on the topics: Feature (computer vision) & Feature extraction.
Citations
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Patent•
26 Dec 2005TL;DR: In this paper, the authors presented an image processing system, a learning device and a method, and a program capable of easily extracting a characteristic amount used for recognition processing. But this method was not applied to a robot.
Abstract: There are provided an image processing system, a learning device and a method, and a program capable of easily extracting a characteristic amount used for recognition processing. A characteristic point is extracted from a learning model image. According to the characteristic point, a characteristic amount is extracted. The characteristic amount is registered in a leaning model dictionary registration unit (23). Similarly, a characteristic point is extracted from a leaning input image containing a model object contained in the learning model image. According to the characteristic point, a characteristic amount is extracted. The characteristic amount is compared to the characteristic amount registered in the learning model registration unit (23). As the comparison result, the characteristic amount which has become a pair most frequently is registered as a characteristic amount used for recognition processing in a model dictionary registration unit (12). The present invention may be applied to a robot.
51 citations
Patent•
26 Dec 2005
TL;DR: In this article, the authors propose a method to improve the quality of the data collected by the data collection system by using the information gathered from the sensor nodes of the sensor board.
Abstract: 本発明は、簡便に、認識処理に用いる特徴量を抽出できるようにする画像処理システム、学習装置および方法、並びにプログラムに関する。学習用モデル画像から特徴点が抽出され、その特徴点を基に、特徴量が抽出され、その特徴量が学習用モデル辞書登録部２３に登録される。同様に、学習用モデル画像に含まれるモデル物体を含む学習用入力画像から特徴点が抽出され、その特徴点を基に、特徴量が抽出され、その特徴量と、学習用モデル登録部２３に登録されている特徴量が比較される。その比較の結果、最も対になった回数が多い特徴量が、認識処理に用いられる特徴量として、モデル辞書登録部１２に登録される。本発明は、ロボットに適用することができる。
References
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23 Jun 1998
TL;DR: It is shown that the method for estimating the coefficients of eigenimages can be applied to convolved and subsampled images yielding the same value of the coefficients, which enables an efficient multiresolution approach, where the values of the coefficient can directly be propagated through the scales.
Abstract: Recently, we have proposed a new approach to estimation of the coefficients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise. In this paper we show that our method for estimating the coefficients can be applied to convolved and subsampled images yielding the same value of the coefficients. This enables an efficient multiresolution approach, where the values of the coefficients can directly be propagated through the scales. This property is used to extend our robust method to the problem of scaled images. We performed extensive experimental evaluations to confirm our theoretical results.
35 citations