Abstract: In this invention, systems and methods are described that allow performing stereo reconstruction with line-scan cameras in general configurations. Consequently, the cumbersome exact alignment of the sensor lines becomes superfluous. The proposed method requires that telecentric lenses instead of perspective lenses are mounted on the line-scan cameras. In this case, the images can be accurately rectified with stereo rectification. The rectified images can be used to perform an efficient stereo matching. The method comprises a camera model and a calibration procedure that allow to precisely model the imaging process, also including the modelling of lens distortions even if the sensor lines are not exactly mounted behind the principal points. This ensures a high accuracy of the resulting 3D reconstruction.

Abstract: In this invention, systems and methods are described that allow performing stereo reconstruction with line-scan cameras in general configurations. Consequently, the cumbersome exact alignment of the sensor lines becomes superfluous. The proposed method requires that telecentric lenses instead of perspective lenses are mounted on the line-scan cameras. In this case, the images can be accurately rectified with stereo rectification. The rectified images can be used to perform an efficient stereo matching. The method comprises a camera model and a calibration procedure that allow to precisely model the imaging process, also including the modelling of lens distortions even if the sensor lines are not exactly mounted behind the principal points. This ensures a high accuracy of the resulting 3D reconstruction.

Abstract: A method for adapting a matching model of an object comprising the step of providing an electronic image of the object; providing a matching model of the object, the matching model consisting of a plurality of points; determining a pose of the object in said electronic image by using a matching approach that uses said matching model; transforming the matching model according to said pose, yielding a transformed model; determining for at least one point of said transformed model a corresponding point in said electronic image; and adapting the matching model according to the at least one determined corresponding point.

Abstract: A method for adapting a matching model of an object comprising the step of providing an electronic image of the object; providing a matching model of the object, the matching model consisting of a plurality of points; determining a pose of the object in said electronic image by using a matching approach that uses said matching model; transforming the matching model according to said pose, yielding a transformed model; determining for at least one point of said transformed model a corresponding point in said electronic image; and adapting the matching model according to the at least one determined corresponding point.

Abstract: The present invention provides a system and method for recognizing a 3D object in a single camera image and for determining the 3D pose of the object with respect to the camera coordinate system. In one typical application, the 3D pose is used to make a robot pick up the object. A view-based approach is presented that does not show the drawbacks of previous methods because it is robust to image noise, object occlusions, clutter, and contrast changes. Furthermore, the 3D pose is determined with a high accuracy. Finally, the presented method allows the recognition of the 3D object as well as the determination of its 3D pose in a very short computation time, making it also suitable for real-time applications. These improvements are achieved by the methods disclosed herein.

Abstract: The present invention provides a system and method for detecting deformable objects in images even in the presence of partial occlusion, clutter and nonlinear illumination changes. A holistic approach for deformable object detection is disclosed that combines the advantages of a match metric that is based on the normalized gradient direction of the model points, the decomposition of the model into parts and a search method that takes all search results for all parts at the same time into account. Despite the fact that the model is decomposed into sub-parts, the relevant size of the model that is used for the search at the highest pyramid level is not reduced. Hence, the present invention does not suffer the speed limitations of a reduced number of pyramid levels that prior art methods have.

Abstract: The present invention provides a system and methods for automatic parameter determination in machine vision in general, and in object recognition in particular. Many machine vision systems use algorithms that demand the user to specify one or more parameters in order to adapt the behavior of the algorithm in dependence of the current application. This is not desirable because the complexity of the algorithm should be hidden from the user and a manual parameter determination is contrary to a desirable high degree of automation. The present invention provides a method to automatically determine the most frequently used parameters in machine vision solely based on the input image itself. The method is explained in detail using an object recognition system as an example. In particular, the model generation process based on a model image of the object is explained. However, also other systems that use edge extraction algorithms, for example, can benefit from the present invention.

Abstract: The present invention provides a system and methods for automatic parameter determination in machine vision in general, and in object recognition in particular. Many machine vision systems use algorithms that demand the user to specify one or more parameters in order to adapt the behavior of the algorithm in dependence of the current application. This is not desirable because the complexity of the algorithm should be hidden from the user and a manual parameter determination is contrary to a desirable high degree of automation. The present invention provides a method to automatically determine the most frequently used parameters in machine vision solely based on the input image itself. The method is explained in detail using an object recognition system as an example. In particular, the model generation process based on a model image of the object is explained. However, also other systems that use edge extraction algorithms, for example, can benefit from the present invention.

Abstract: The present invention provides a system and methods for automatic parameter determination in machine vision in general, and in object recognition in particular. Many machine vision systems use algorithms that demand the user to specify one or more parameters in order to adapt the behavior of the algorithm in dependence of the current application. This is not desirable because the complexity of the algorithm should be hidden from the user and a manual parameter determination is contrary to a desirable high degree of automation. The present invention provides a method to automatically determine the most frequently used parameters in machine vision solely based on the input image itself. The method is explained in detail using an object recognition system as an example. In particular, the model generation process based on a model image of the object is explained. However, also other systems that use edge extraction algorithms, for example, can benefit from the present invention.

Abstract: The present invention provides a system and methods for automatic parameter determination in machine vision in general, and in object recognition in particular. Many machine vision systems use algorithms that demand the user to specify one or more parameters in order to adapt the behavior of the algorithm in dependence of the current application. This is not desirable because the complexity of the algorithm should be hidden from the user and a manual parameter determination is contrary to a desirable high degree of automation. The present invention provides a method to automatically determine the most frequently used parameters in machine vision solely based on the input image itself. The method is explained in detail using an object recognition system as an example. In particular, the model generation process based on a model image of the object is explained. However, also other systems that use edge extraction algorithms, for example, can benefit from the present invention.

Abstract: The present invention provides a system and methods for automatic parameter determination in machine vision in general, and in object recognition in particular. Many machine vision systems use algorithms that demand the user to specify one or more parameters in order to adapt the behavior of the algorithm in dependence of the current application. This is not desirable because the complexity of the algorithm should be hidden from the user and a manual parameter determination is contrary to a desirable high degree of automation. The present invention provides a method to automatically determine the most frequently used parameters in machine vision solely based on the input image itself. The method is explained in detail using an object recognition system as an example. In particular, the model generation process based on a model image of the object is explained. However, also other systems that use edge extraction algorithms, for example, can benefit from the present invention.

Abstract: The present invention provides a system and method for detecting deformable objects in images even in the presence of partial occlusion, clutter and nonlinear illumination changes. A holistic approach for deformable object detection is disclosed that combines the advantages of a match metric that is based on the normalized gradient direction of the model points, the decomposition of the model into parts and a search method that takes all search results for all parts at the same time into account. Despite the fact that the model is decomposed into sub-parts, the relevant size of the model that is used for the search at the highest pyramid level is not reduced. Hence, the present invention does not suffer the speed limitations of a reduced number of pyramid levels that prior art methods have.

Abstract: The present invention provides a system and method for recognizing a 3D object in a single camera image and for determining the 3D pose of the object with respect to the camera coordinate system. In one typical application, the 3D pose is used to make a robot pick up the object. A view-based approach is presented that does not show the drawbacks of previous methods because it is robust to image noise, object occlusions, clutter, and contrast changes. Furthermore, the 3D pose is determined with a high accuracy. Finally, the presented method allows the recognition of the 3D object as well as the determination of its 3D pose in a very short computation time, making it also suitable for real-time applications. These improvements are achieved by the methods disclosed herein.

Abstract: The present invention provides a system and methods for automatic parameter determination in machine vision in general, and in object recognition in particular. Many machine vision systems use algorithms that demand the user to specify one or more parameters in order to adapt the behavior of the algorithm in dependence of the current application. This is not desirable because the complexity of the algorithm should be hidden from the user and a manual parameter determination is contrary to a desirable high degree of automation. The present invention provides a method to automatically determine the most frequently used parameters in machine vision solely based on the input image itself. The method is explained in detail using an object recognition system as an example. In particular, the model generation process based on a model image of the object is explained. However, also other systems that use edge extraction algorithms, for example, can benefit from the present invention.

Abstract: The present invention provides a method for the recognition of objects in an image, where the objects may consist of an arbitrary number of parts that are allowed to move with respect to each other. In the offline phase the invention automatically learns the relative movements of the single object parts from a sequence of example images and builds a hierarchical model that incorporates a description of the single object parts, the relations between the parts, and an efficient search strategy. This is done by analyzing the pose variations (e.g., variations in position, orientation, and scale) of the single object parts in the example images. The poses can be obtained by an arbitrary similarity measure for object recognition, e.g., normalized cross correlation, Hausdorff distance, generalized Hough transform, the modification of the generalized Hough transform, or the similarity measure. In the online phase the invention uses the hierarchical model to efficiently find the entire object in the search image.

Abstract: The present invention provides a method for the recognition of objects in an image, where the objects may consist of an arbitrary number of parts that are allowed to move with respect to each other. In the offline phase the invention automatically learns the relative movements of the single object parts from a sequence of example images and builds a hierarchical model that incorporates a description of the single object parts, the relations between the parts, and an efficient search strategy. This is done by analyzing the pose variations (e.g., variations in position, orientation, and scale) of the single object parts in the example images. The poses can be obtained by an arbitrary similarity measure for object recognition, e.g., normalized cross correlation, Hausdorff distance, generalized Hough transform, the modification of the generalized Hough transform, or the similarity measure. In the online phase the invention uses the hierarchical model to efficiently find the entire object in the search image.

Abstract: A method for recognizing a user-defined model object within an image is provided, which is invariant to occlusion (i.e., the object to be found is only partially visible), clutter (i.e., there may be other objects in the image, even within the model object), non-linear illumination changes, and global or local contrast reversals. The object to be found may have been distorted when compared to the user-defined model by geometric transformations of a certain class, e.g., translations, rigid transformations (translation and rotation), similarity transformations (translation, rotation, and uniform scaling), or arbitrary affine transformations.