Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A system and method for segmenting a current frame of a video includes receiving a segmentation mask determined from a previous frame of the video, receiving a background model value and a background deviation value for a given pixel of a previous frame of the video, updating the received background deviation value, updating the background model value independently of the updating of the background deviation value and determining a value of a pixel of a new segmentation mask corresponding to the given pixel based on a foreground model value of the current frame, a segmenting background model value of the current frame and a segmentation threshold, the determining of the new segmentation mask defining whether the given pixel is a foreground pixel or a background pixel of the current frame. The segmentation mask is used for detecting objects, classifying objects and detecting events in the scene captured by the camera.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A camera system comprises an image capturing device, and connected to it are an object classification module and a calibration module. The object classification module is operable to determine whether or not an object in an image is a member of an object class, and the calibration module is operable to estimate representative sizes of the object. The object classification module may determine a confidence parameter that is used by the calibration module, or conversely, the calibration module may produce a size that is used by the classification module.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A system and method for segmenting a current frame of a video includes receiving a segmentation mask determined from a previous frame of the video, receiving a background model value and a background deviation value for a given pixel of a previous frame of the video, updating the received background deviation value, updating the background model value independently of the updating of the background deviation value and determining a value of a pixel of a new segmentation mask corresponding to the given pixel based on a foreground model value of the current frame, a segmenting background model value of the current frame and a segmentation threshold, the updating defining whether the given pixel is a foreground pixel or a background pixel of the current frame. A chaotic noise mask may also be determined and weighting factors may be updated based on the noise ratio of the chaotic noise mask.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The image capturing device has a field of view and produces image data representing an image of the field of view. The object classification module is operable to determine whether an object in an image is a member of an object class. The object classification module includes N decision steps configured in a cascade configuration, wherein at least one of the N decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether an object is a member of the object class.

Abstract: A computer-implemented method performs an operation on a set of at least one BFP operands to generate a BFP result. The method is designed to reduce the risks of overflow and loss of accuracy attributable to the operation. The method performs an analysis to determine respective shift values for each of the operands and the result. The method calculates result mantissas by shifting the stored bit patterns representing the corresponding operand mantissa values by their respective associated shift values determined in the analysis step, performing the operation on shifted operand mantissas to generate preliminary result mantissa, and shifting the preliminary result mantissas by a number of bits determined in the analysis step.

Abstract: A computer-implemented method performs an operation on a set of at least one BFP operands to generate a BFP result. The method is designed to reduce the risks of overflow and loss of accuracy attributable to the operation. The method performs an analysis to determine respective shift values for each of the operands and the result. The method calculates result mantissas by shifting the stored bit patterns representing the corresponding operand mantissa values by their respective associated shift values determined in the analysis step, performing the operation on shifted operand mantissas to generate preliminary result mantissa, and shifting the preliminary result mantissas by a number of bits determined in the analysis step.

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The image capturing device has a field of view and produces image data representing an image of the field of view. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes N decision steps configured in a cascade configuration, wherein at least one of the N decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class.

Abstract: A machine vision inspection system and method that increase the resolution of object image-data without physically manipulating the physical optics of cameras used during inspection. The exemplary embodiment of the invention provides a system and method that perform what may be characterized as a digital zoom. Using this digital zoom, the resolution of object image-data provided by a camera may be artificially increased. Moreover, in accordance with the exemplary embodiment of the invention, a determination may be made regarding whether portions of the object image-data are under-resolved, and based on that determination, a local digital zoom may be performed on that under-resolved portion of object image-data to increase the resolution of that portion of object image-data.

Abstract: During statistical training and automated inspection of objects by a machine vision system, a General Affine Transform is advantageously employed to improve system performance. During statistical training, the affine poses of a plurality of training images are determined with respect to an alignment model image. Following filtering to remove high frequency content, the training images and their corresponding affine poses are applied to an affine transformation. The resulting transformed images are accumulated to compute template and threshold images to be used for run-time inspection. During run-time inspection, the affine pose of the run-time image relative to the alignment model image is determined. Following filtering of the run-time image, the run-time image is affine transformed by its affine pose. The resulting transform image is compared with the template and threshold images computed during statistical training to determine object status.

Abstract: Novel methods for generating a projection of an image according to the invention concurrently rotate, scale, translate, skew, shear, or otherwise transform the image via a sequence of two one-dimensional transformations. A first intermediate image is generated via affine transformation of the source along a first axis. The intermediate image is then subjected to affine transformation along a second axis, e.g., perpendicular to the first. A projection of the resultant image is generated along a selected one of the first or second axes by summing pixel intensities in corresponding rows (or columns) or by summing counts of pixels whose intensities are above or below a threshold.

Abstract: During statistical training and automated inspection of objects by a machine vision system, a General Affine Transform is advantageously employed to improve system performance. During statistical training, the affine poses of a plurality of training images are determined with respect to an alignment model image. Following filtering to remove high frequency content, the training images and their corresponding affine poses are applied to an affine transformation. The resulting transformed images are accumulated to compute template and threshold images to be used for run-time inspection. During run-time inspection, the affine pose of the run-time image relative to the alignment model image is determined. Following filtering of the run-time image, the run-time image is affine transformed by its affine pose. The resulting transform image is compared with the template and threshold images computed during statistical training to determine object status.