Abstract: A system for detecting a state of a current collector of an electrically driven vehicle includes a video camera device for digitally recording images of the current collector and an image-evaluating device for the data evaluation of the image recordings. The current collector has optically detectable markings, the position and/or shape and/or surface area and/or color of which can be detected by the image-evaluating device in an automated manner. A system that makes the automatic state detection faster and more reliable is thereby provided.

Abstract: A system for detecting a state of a current collector of an electrically driven vehicle includes a video camera device for digitally recording images of the current collector and an image-evaluating device for the data evaluation of the image recordings. The current collector has optically detectable markings, the position and/or shape and/or surface area and/or color of which can be detected by the image-evaluating device in an automated manner. A system that makes the automatic state detection faster and more reliable is thereby provided.

Abstract: A method of detecting an object in image data that is deemed to be a threat includes annotating sections of at least one training image to indicate whether each section is a component of the object, encoding a pattern grammar describing the object using a plurality of first order logic based predicate rules, training distinct component detectors to each identify a corresponding one of the components based on the annotated training images, processing image data with the component detectors to identify at least one of the components, and executing the rules to detect the object based on the identified components.

Abstract: A speed limit assistant (SLA) system includes a camera based SLA, a map based SLA, and a fusion unit. The camera based SLA is configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs. The map based SLA is configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits. The fusion unit is configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.

Abstract: First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grammars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.

Abstract: A method for specifying design rules for a manufacturing process includes providing a training set of 3D point meshes that represent an anatomical structure, for each 3D point mesh, finding groupings of points that define clusters for each shape class of the anatomical structure, calculating a prototype for each shape class cluster, and associating one or more manufacturing design rules with each shape class prototype. The method includes providing a new 3D point mesh that represents an anatomical structure, calculating a correspondence function that maps the new 3D point mesh to a candidate shape class prototype by minimizing a cost function, calculating a transformation that aligns points in the new 3D point mesh with points in the candidate shape class prototype, and using the rules associated with the shape class prototype, if the candidate shape class prototype is successfully aligned with the new 3D point mesh.

Abstract: A method of detecting an object in image data that is deemed to be a threat includes annotating sections of at least one training image to indicate whether each section is a component of the object, encoding a pattern grammar describing the object using a plurality of first order logic based predicate rules, training distinct component detectors to each identify a corresponding one of the components based on the annotated training images, processing image data with the component detectors to identify at least one of the components, and executing the rules to detect the object based on the identified components.

Abstract: First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grmmars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.

Abstract: A method for specifying design rules for a manufacturing process includes providing a training set of 3D point meshes that represent an anatomical structure, for each 3D point mesh, finding groupings of points that define clusters for each shape class of the anatomical structure, calculating a prototype for each shape class cluster, and associating one or more manufacturing design rules with each shape class prototype. The method includes providing a new 3D point mesh that represents an anatomical structure, calculating a correspondence function that maps the new 3D point mesh to a candidate shape class prototype by minimizing a cost function, calculating a transformation that aligns points in the new 3D point mesh with points in the candidate shape class prototype, and using the rules associated with the shape class prototype, if the candidate shape class prototype is successfully aligned with the new 3D point mesh.

Abstract: A method for training a system for detecting multi-class objects in an image or a video sequence is described. A common ensemble of weak classifiers for a set of object classes is identified. For each object class, a separate weighting scheme is adapted for the ensemble of weak classifiers. A method for detecting objects of multiple classes in an image or a video sequence is also disclosed. Each class is assigned a detector that is implemented by a weighted combination of weak classifiers such that all of the detectors are based on a common ensemble of weak classifiers. Then weights are individually set for each class.

Abstract: A speed limit assistant (SLA) system includes a camera based SLA, a map based SLA, and a fusion unit. The camera based SLA is configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs. The map based SLA is configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits. The fusion unit is configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.

Abstract: A method for classifying pulmonary structures in digitized images includes providing approximate target structure locations of one or more target structures in a digitized 3-dimensional (3D) image, fitting an anisotropic Gaussian model about said approximate target locations to generate more precise 3D target models and center locations of said one or more target structures, warping each said 3D target model into a 3D sphere, constructing a bounding manifold about each said warped 3D sphere, and identifying clusters on said bounding manifold wherein said one or more target structures are classified.

Abstract: A method for detecting and recognizing at least one traffic sign is disclosed. A video sequence having a plurality of image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.

Abstract: A method for classifying pulmonary structures in digitized images includes providing approximate target structure locations of one or more target structures in a digitized 3-dimensional (3D) image, fitting an anisotropic Gaussian model about said approximate target locations to generate more precise 3D target models and center locations of said one or more target structures, warping each said 3D target model into a 3D sphere, constructing a bounding manifold about each said warped 3D sphere, and identifying clusters on said bounding manifold wherein said one or more target structures are classified.

Abstract: A computer-implemented method for supervised learning for classification that unifies generative and discriminative methods in a variational framework includes providing training data for determining a classifier, defining a cost functional based on a kernel density, finding a function of the cost functional by searching for a zero crossing of joint probabilities for a label for a given data point, optimizing the cost functional using a gradient descent, and outputting the classifier comprising an optimized cost functional for classifying data.

Abstract: A method for detecting and recognizing at least one traffic sign is disclosed. A video sequence having a plurality of image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.

Abstract: A method for training a system for detecting multi-class objects in an image or a video sequence is described. A common ensemble of weak classifiers for a set of object classes is identified. For each object class, a separate weighting scheme is adapted for the ensemble of weak classifiers. A method for detecting objects of multiple classes in an image or a video sequence is also disclosed. Each class is assigned a detector that is implemented by a weighted combination of weak classifiers such that all of the detectors are based on a common ensemble of weak classifiers. Then weights are individually set for each class.