Abstract: A method is provided for training and using an object classifier to identify a class object from a captured image. A plurality of still images is obtained from training data and a feature generation technique is applied to the plurality of still images for identifying candidate features from each respective image. A subset of features is selected from the candidate features using a similarity comparison technique. Identifying candidate features and selecting a subset of features is iteratively repeated a predetermined number of times for generating a trained object classifier. An image is captured from an image capture device. Features are classified in the captured image using the trained object classifier. A determination is made whether the image contains a class object based on the trained object classifier associating an identified feature in the image with the class object.

Abstract: A computer-implemented method is provided for statistical data learning under privacy constraints. The method includes: receiving, by a processor, a plurality of pieces of statistical information relating to a statistical object and aggregating, by the processor, the plurality of pieces of statistical information so as to provide an estimation of the statistical object. Each piece of statistical information includes an uncertainty variable, the uncertainty variable being a value determined from a function having a predetermined mean. The number of pieces of statistical information aggregated is proportional to the reliability of the estimation of the statistical object.

Abstract: Complex-object detection using a cascade of classifiers for identifying complex-objects parts in an image in which successive classifiers process pixel patches on condition that respective discriminatory features sets of previous classifiers have been identified and selecting additional pixel patches from a query image by applying known positional relationships between an identified complex-object part and another part to be identified.

Abstract: Complex-object detection using a cascade of classifiers for identifying complex-objects parts in an image in which successive classifiers process pixel patches on condition that respective discriminatory features sets of previous classifiers have been identified and selecting additional pixel patches from a query image by applying known positional relationships between an identified complex-object part and another part to be identified.

Abstract: Complex-object detection using a cascade of classifiers for identifying complex-objects parts in an image in which successive classifiers process pixel patches on condition that respective discriminatory features sets of previous classifiers have been identified and selecting additional pixel patches from a query image by on the basis of probability data.

Abstract: An X-ray system is arranged for providing X-ray exposure to a target volume. The aforesaid X-ray system comprises an X-ray source and at least one focusing lens. The provided exposure is distributed over a volume of the target in a substantially uniform manner.

Abstract: A system and method may compare an image vector representing an image feature of a first image fragment of an image to database vectors representing the image feature of database image fragments of database images. It may be determined based on the comparison a first matching database vector of the database vectors which most closely, among the database vectors, describes the first image feature represented by the image vector. The system or method may determine, using a data structure in conjunction with the first matching database vector and previously matched database vectors, a second of the database vectors which includes the first matching database vector and the previously matched database vectors and most closely describes a second image fragment including the first image fragment. The system or method may determine an object feature based on the second database vector.

Abstract: A method is provided for training and using an object classifier to identify a class object from a captured image. A plurality of still images is obtained from training data and a feature generation technique is applied to the plurality of still images for identifying candidate features from each respective image. A subset of features is selected from the candidate features using a similarity comparison technique. Identifying candidate features and selecting a subset of features is iteratively repeated a predetermined number of times for generating a trained object classifier. An image is captured from an image capture device. Features are classified in the captured image using the trained object classifier. A determination is made whether the image contains a class object based on the trained object classifier associating an identified feature in the image with the class object.

Abstract: In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d?) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed.

Abstract: In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d?) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed.

Abstract: In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d?) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed.

Abstract: A fastening apparatus for a shoe including an adjustable gap and a fastener with two elements, wherein one fastener element is attached to the first side of the adjustable gap. Two laces are attached at a first end to the second fastener element, and extending from the second fastener element to the second side of the adjustable gap, and at least one lace extends from the second side of the adjustable gap to the first side of the adjustable gap. For tightening the shoe, the second fastener element is pulled towards the first side of the adjustable gap, and is attached to the first fastener element for keeping the shoe fastened.

Abstract: A fastening apparatus for a shoe including an adjustable gap with two sides and a fastener with two elements, wherein one fastener element is attached to the first side of the adjustable gap. At least one lace is threaded through holes along the two sides of the adjustable gap, and two ends of the lace are fastened together between the second fastening element and the second side of the adjustable gap. The lace is threaded at one end through an opening in the second fastener element and forms at least one lace segment that extends from the second fastener element to the second side of the adjustable gap, and at least one lace segment extends from the second side of the adjustable gap to the first side of the adjustable gap. The two ends of the at least one lace may be fastened together by a knot. For tightening the shoe, the second fastener element is pulled towards the first side of the adjustable gap, and is attached to the first fastener element for keeping the shoe fastened.

Abstract: A method and apparatus for producing an insole for a foot including defining a flexible insole housing in which is disposed a deformable material impregnated with an uncured resin, activating the resin for initiating curing thereof, locating the foot on the insole housing and allowing the resin to harden and thus, to preserve the configuration defined by the bottom of the foot.

Abstract: A method and apparatus for producing an insole for a foot including defining a flexible insole housing in which is disposed a deformable material impregnated with an uncured resin, activating the resin for initiating curing thereof, locating the foot on the insole housing and allowing the resin to harden and thus, to preserve the configuration defined by the bottom of the foot.

Abstract: A method for casting in situ an insole on a foot comprising the steps of preparing a mold defining an open top recess, providing a casting material in the recess, placing the foot inside the recess at a predetermined angle and position relative to the mold, engaging the casting material by the plantar surface of the foot, permitting the casting material to conform to the shape of the plantar surface of the foot, and permitting the casting material to harden.