Abstract: A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.

Abstract: A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.

Abstract: A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.

Abstract: An apparatus for providing pattern detection may include a processor. The processor may be configured to iteratively test different models and corresponding scales for each of the models. The models may be employed for modeling parameters corresponding to a visually detected data. The processor may be further configured to evaluate each of the models over a plurality of iterations based on a function evaluation of each of the models, select one of the models based on the function evaluation of the selected one of the models, and utilize the selected one of the models for fitting the data.

Abstract: An apparatus for providing pattern detection may include a processor. The processor may be configured to iteratively test different models and corresponding scales for each of the models. The models may be employed for modeling parameters corresponding to a visually detected data. The processor may be further configured to evaluate each of the models over a plurality of iterations based on a function evaluation of each of the models, select one of the models based on the function evaluation of the selected one of the models, and utilize the selected one of the models for fitting the data.

Abstract: An image processing is applied in common to image data for a first image and to image data for a second image. The image data for the first image and the image data for the second image are moreover compared as a basis for matching a visual appearance of images for a stereoscopic presentation. The visual appearance can be matched in the current pair of images by adjusting at least one of the images accordingly and/or in future pairs of images by adjusting parameters of at least one of the image capturing components providing the image data.

Abstract: An apparatus for providing pattern detection may include a processor. The processor may be configured to iteratively test different models and corresponding scales for each of the models. The models may be employed for modeling parameters corresponding to a visually detected data. The processor may be further configured to evaluate each of the models over a plurality of iterations based on a function evaluation of each of the models, select one of the models based on the function evaluation of the selected one of the models, and utilize the selected one of the models for fitting the data.

Abstract: An image processing is applied in common to image data for a first image and to image data for a second image. The image data for the first image and the image data for the second image are moreover compared as a basis for matching a visual appearance of images for a stereoscopic presentation. The visual appearance can be matched in the current pair of images by adjusting at least one of the images accordingly and/or in future pairs of images by adjusting parameters of at least one of the image capturing components providing the image data.

Abstract: The invention relates to a motion-input device for a computing device, comprising a housing; a three-axis acceleration sensor arranged in said housing for outputting inertia signals related to the orientation and the movement of the motion-input device with a three-axis compass arranged in said housing, for outputting magnetic field signals related to the magnetic field orientation of the motion-input device, wherein said motion-input device is provided with a transfer component for transferring said magnetic field signals and said inertia signals to said computing device.

Abstract: Systems and methods for organization and browsing a set of images allow a user to find and identify desired images. A collection of images (e.g., stored in an image database) may be organized using a tree or pyramid hierarchy such that each level of the hierarchy represents a finer level of similarity between images in an image cluster. In one arrangement, images are stored as leaf nodes and each ascendant node of the leaf nodes represents a cluster to which the images belong. Each ascendant node may include an animation image representing the set of images belonging to the cluster. A user may browse an image database by identifying and accessing clusters of images that are progressively more refined. A hybrid browsing method and system may also be used wherein images may be displayed simultaneously with the animation images to which they correspond.

Abstract: An apparatus-based method is disclosed for calibration of a 3D field sensor. The method includes accessing a plurality of samples, where the samples are from the 3D field sensor. Each sample represents a magnitude and an orientation of a three-dimensional field sensed by the 3D field sensor. Using the plurality of samples, a plurality of parameters is determined of an ellipsoid. The determination of the plurality of parameters is performed so that the ellipsoid fits the plurality of samples. A transformation is determined that transforms the ellipsoid into a sphere. The transformation is applied to a sample to create a transformed sample. Apparatus and signal bearing media are also disclosed.