We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. For this purpose, we have developed a hybrid appearance-based approach where objects are encoded as loos...
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We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. For this purpose, we have developed a hybrid appearance-based approach where objects are encoded as loose collections of parts and relations between neighboring parts. The key features of this approach are: part decomposition based on local structure segmentation derived from multi-scale wavelet filters, flexible and efficient recognition by combining weak structural constraints, and learning and generalization of generic object categories (with possibly large intra-class variability) from real examples.
Thin network extraction from three dimensional (3D) images is a new issue in image processing. It is of major importance in medical vascular imaging for diagnostics, therapy planning and surgery. Here, the authors dev...
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Thin network extraction from three dimensional (3D) images is a new issue in image processing. It is of major importance in medical vascular imaging for diagnostics, therapy planning and surgery. Here, the authors develop a framework for automatic vascular network extraction from the volumic image. The approach consists in treating the 3D image as a hyper-surface of IR/sup 4/. It is shown that the crest points of this hyper-surface correspond to the center line of the thin network in the image. Promising results are shown on synthetic and real vascular images.
The individuality of a human face depends on the fine details of the facial components, and it is necessary to extract and to describe these detailed patterns in order to recognize human faces. We propose a method to ...
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The individuality of a human face depends on the fine details of the facial components, and it is necessary to extract and to describe these detailed patterns in order to recognize human faces. We propose a method to describe the eye figure with small parameters by classifying their patterns to typical groups. First, an eye image is divided into parts such as eyelid and inner corner, and a set of 1-dimensional slit projections is obtained from the 2-dimensional intensity array. Then, the principal component analysis is applied to these projections to find the major axes which have typical features. The individuality of each eye is parameterized by the principal component scores. The effectiveness of the description is evaluated by generating sketch images based on the parameters extracted from real eye images.
Retrieving images from very large collections, using image content as a key, is becoming an important problem. Finding objects in image databases is a big challenge in the field. The paper describes our approach to ob...
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Retrieving images from very large collections, using image content as a key, is becoming an important problem. Finding objects in image databases is a big challenge in the field. The paper describes our approach to object recognition, which is distinguished by: a rich involvement of early visual primitives, including color and texture; hierarchical grouping and learning strategies in the classification process; the ability to deal with rather general objects in uncontrolled configurations and contexts. We illustrate these properties with three case studies: one demonstrating the use of color and texture descriptors; one learning scenery concepts using grouped features; and one demonstrating a possible application domain in detecting naked people in a scene.
This article proposes a new approach for verification of people. The model consists of two parts: face and signature analysis. For face information processing morphological filtering is used to enhance the intrinsic f...
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This article proposes a new approach for verification of people. The model consists of two parts: face and signature analysis. For face information processing morphological filtering is used to enhance the intrinsic features of a face, reduce the influence of rotation in depth, changes in facial expression, hair style, glasses and lighting conditions. The filtered images are then a subject for learning by a modified high order neural network. In signature analysis the model first traces the signature to extract the dynamical information that is usually lost in an off-line mode. After this step a neural network (neocognitron with switching attention) is used to recognize and finally verify the signature. These two parts can work independently and finally their outputs can be used to form a complex person verifier.
Statistical pattern classifiers are designed by population parameters of pattern distributions estimated by a set of training samples. Therefore, classification performance depends considerably on training sample size...
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This paper presents a new technique for the recognition of hand-printed Latin characters using machinelearning. Conventional methods have relied on manually constructed dictionaries which are tedious to construct and...
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We describe a system which is capable of learning the presentation of document logical structures, exemplarily shown for business letters. Presenting a set of instances to the system, it clusters them into structural ...
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This paper presents the algorithms for recognition and beautification which are used in incremental graphic design applications. These applications propose multimodal interfaces integrating handwriting, gesture, and s...
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A new non-hierarchical spatial data structure named four directional adjacency graphs (FDAG) is proposed. In the FDAG vertical and horizontal neighborhood relationship between rectangles is well represented so that st...
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