WorldCat Identities

Nagao, Kenji

Overview
Works: 5 works in 7 publications in 2 languages and 7 library holdings
Roles: Author
Classifications: Q335.M41,
Publication Timeline
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Most widely held works by Kenji Nagao
Kōmuin shiken kakomon sūteki suiri ga surasura tokeru by kenji Nagao( Book )

2 editions published in 2007 in Japanese and held by 2 WorldCat member libraries worldwide

Recognizing faces by weakly orthogonalizing against perturbations by Kenji Nagao( )

1 edition published in 1998 in English and held by 1 WorldCat member library worldwide

Object recognition by alignment using invariant projections of planar surfaces by Kenji Nagao( Book )

2 editions published in 1994 in English and held by 1 WorldCat member library worldwide

In order to recognize an object in an image, we must determine the best transformation from object model to the image. In this paper, we show that for features from coplanar surfaces which undergo linear transformations in space, there exist projections invariant to the surface motions up to rotations in the image field. To use this property, we propose a new alignment approach to object recognition based on centroid alignment of corresponding feature groups. This method uses only a single pair of 2D model and data. Experimental results show the robustness of the proposed method against perturbations of feature positions. Object recognition, Invariant properties, Recognition by alignment
Direct Object Recognition Using No Higher Than Second or Third Order Statistics of the Image( )

1 edition published in 1995 in English and held by 0 WorldCat member libraries worldwide

Novel algorithms for object recognition are described that directly recover the transformations relating the image to its model. Unlike methods fitting the conventional framework, these new methods do not require exhaustive search for each feature correspondence in order to solve for the transformation. Yet they allow simultaneous object identification and recovery of the transformation. Given hypothesized corresponding regions in the model and data (2D views) - which are from planar surfaces of the 3D objects these methods allow direct computation of the parameters of the transformation by which the data may be generated from the model. We propose two algorithms: one based on invariants derived from no higher than second and third order moments of the image, the other via a combination of the affine properties of geometrical and differential attributes of the image. Empirical results on natural images demonstrate the effectiveness of the proposed algorithms. A sensitivity analysis of the algorithm is presented. We demonstrate in particular that the differential method is quite stable against perturbations - although not without some error when compared with conventional methods. We also demonstrate mathematically that even a single point correspondence suffices, theoretically at least, to recover affine parameters via the differential method
Recognizing 3D Object Using Photometric Invariant( )

1 edition published in 1995 in English and held by 0 WorldCat member libraries worldwide

In this paper we describe a new efficient algorithm for recognizing 3D objects by combining photometric and geometric invariants. Some photometric properties are derived, that are invariant to the changes of illumination and to relative object motion with respect to the camera and/or the lighting source in 3D space. We argue that conventional color constancy algorithms can not be used in the recognition of 3D objects. Further we show recognition does not require a full constancy of colors, rather, it only needs something that remains unchanged under the varying light conditions and poses of the objects. Combining the derived color invariants and the spatial constraints on the object surfaces, we identify corresponding positions in the model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. Tests are given to show the stabillty and efficiency of our approach to 3D object recognition
 
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Audience level: 0.94 (from 0.83 for Object rec ... to 0.99 for Recognizin ...)

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