Abstract: An image processor (10) has a window selector for choosing a detection window within the image, and a self similarity computation part (40) for determining self-similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in any other part of the detector window, and for repeating the determination for groups in all parts of the detection window, to generate a global self similarity descriptor for the detection window. A classifier (50) is used for classifying whether an object is present based on the global self-similarity descriptor. By using global self-similarity rather than local similarities more information is captured which can lead to better classification. In particular, it helps enable recognition of more distant self-similarities inherent in the object, and self-similarities present at any scale.

Abstract: An image processor (10) has a window selector for choosing a detection window within the image, and a self similarity computation part (40) for determining self-similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in any other part of the detector window, and for repeating the determination for groups in all parts of the detection window, to generate a global self similarity descriptor for the detection window. A classifier (50) is used for classifying whether an object is present based on the global self-similarity descriptor. By using global self-similarity rather than local similarities more information is captured which can lead to better classification. In particular, it helps enable recognition of more distant self-similarities inherent in the object, and self-similarities present at any scale.

Abstract: An image processor (10) has a window selector for choosing a detection window within the image, and a self similarity computation part (40) for determining self-similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in any other part of the detector window, and for repeating the determination for groups in all parts of the detection window, to generate a global self similarity descriptor for the detection window. A classifier (50) is used for classifying whether an object is present based on the global self-similarity descriptor. By using global self-similarity rather than local similarities more information is captured which can lead to better classification. In particular, it helps enable recognition of more distant self-similarities inherent in the object, and self-similarities present at any scale.

Abstract: A motion estimating device first detects mobile objects Oi and Oi? in continuous image frames T and T?, and acquires image areas Ri and Ri? corresponding to the mobile objects Oi and Oi?. Then, the motion estimating device removes the image areas Ri and Ri? corresponding to the mobile objects Oi and Oi? in the image frames T and T?, extracts corresponding point pairs Pj of feature points between the image frames T and T? from the image areas having removed the image areas Ri and Ri?, and carries out the motion estimation of the autonomous mobile machine between the image frames T and T? on the basis of the positional relationship of the corresponding point pairs Pj of feature points.

Abstract: A motion estimating device first detects mobile objects Oi and Oi? in continuous image frames T and T?, and acquires image areas Ri and Ri? corresponding to the mobile objects Oi and Oi?. Then, the motion estimating device removes the image areas Ri and Ri? corresponding to the mobile objects Oi and Oi? in the image frames T and T?, extracts corresponding point pairs Pj of feature points between the image frames T and T? from the image areas having removed the image areas Ri and Ri?, and carries out the motion estimation of the autonomous mobile machine between the image frames T and T? on the basis of the positional relationship of the corresponding point pairs Pj of feature points.

Abstract: The invention relates to a crystalline micronisate of (1 ?,2?,4?,5?,7?)-7-[(hydroxydi-2-thienylacetyl)oxy]-9,9-dimethyl-3-oxa-9-azoniatricyclo[3.3.1.02,4]nonane-bromide, processes for preparing it and its use for preparing a pharmaceutical composition, particularly for preparing a pharmaceutical composition with an anticholinergic activity.

Abstract: The invention relates to a crystalline micronisate of (1?,2?,4?,5?,7?)-7-[(hydroxydi-2-thienylacetyl)oxy]-9,9-dimethyl-3-oxa-9-azoniatricyclo[3.3.1.02,4]nonane-bromide, processes for preparing it and its use for preparing a pharmaceutical composition, particularly for preparing a pharmaceutical composition with an anticholinergic activity.

Abstract: The invention relates to a crystalline micronisate of (1 ?,2?,4?,5?,7?)-7-[(hydroxydi-2-thienylacetyl)oxy]-9,9-dimethyl-3-oxa-9-azoniatricyclo[3.3.1.02,4]nonane-bromide, processes for preparing it and its use for preparing a pharmaceutical composition, particularly for preparing a pharmaceutical composition with an anticholinergic activity.

Abstract: The invention relates to a crystalline micronisate of (1&agr;,2&bgr;,4&bgr;,5&agr;,7&bgr;)-7-[(hydroxydi-2-thienylacetyl)oxy]-9,9-dimethyl-3-oxa-9-azoniatricyclo[3.3.1.02,4]nonane-bromide, processes for preparing it and its use for preparing a pharmaceutical composition, particularly for preparing a pharmaceutical composition with an anticholinergic activity.