Abstract: A video monitoring system (20) includes a camera head (22, 62), including an infrared illumination source (76) and an image sensor (74). A mount (64, 102, 112) is configured to hold the camera head in a fixed location and orientation above a crib (24), so that the image sensor captures images of the crib and an intervention region (52) adjacent to the crib from a fixed perspective.

Abstract: Systems and methods for monitoring babies with cameras using a centralized computation and storage center that allows using visual output signals for computer vision and machine learning analysis and high-level reasoning of baby movements. The system comprises a camera located at a predefined working point above a baby's crib, and one or more communication networks between components of the system including a web-based network for in-depth computer vision and machine learning analysis of the visual output signals by an analysis server.

Abstract: Systems and methods for monitoring babies with cameras using a centralized computation and storage center that allows using visual output signals for computer vision and machine learning analysis and high-level reasoning of baby movements. The system comprises a camera located at a predefined working point above a baby's crib, and one or more communication networks between components of the system including a web-based network for in-depth computer vision and machine learning analysis of the visual output signals by an analysis server.

Abstract: Systems and methods for monitoring babies with cameras using a centralized computation and storage center that allows using visual output signals for computer vision and machine learning analysis and high-level reasoning of baby movements. The system comprises a camera located at a predefined working point above a baby's crib, and one or more communication networks between components of the system including a web-based network for in-depth computer vision and machine learning analysis of the visual output signals by an analysis server.

Abstract: Systems and methods for monitoring babies with cameras using a centralized computation and storage center that allows using visual output signals for computer vision and machine learning analysis and high-level reasoning of baby movements. The system comprises a camera located at a predefined working point above a baby's crib, and one or more communication networks between components of the system including a web-based network for in-depth computer vision and machine learning analysis of the visual output signals by an analysis server.

Abstract: A method for defect analysis includes identifying single-class classifiers for a plurality of defect classes, the plurality of defect classes characterized by respective ranges of inspection parameter values. Each single-class classifier is configured for a respective class to identify defects belonging to the respective class based on the inspection parameter values, while identifying the defects not in the respective class as unknown defects. A multi-class classifier is identified that is configured to assign each defect to one of the plurality of the defect classes based on the inspection parameter values. Inspection data is received, and both the single-class and multi-class classifiers are applied to the inspection data to assign the defect to one of the defect classes.

Abstract: A method for defect analysis includes identifying single-class classifiers for a plurality of defect classes, the plurality of defect classes characterized by respective ranges of inspection parameter values. Each single-class classifier is configured for a respective class to identify defects belonging to the respective class based on the inspection parameter values, while identifying the defects not in the respective class as unknown defects. A multi-class classifier is identified that is configured to assign each defect to one of the plurality of the defect classes based on the inspection parameter values. Inspection data is received, and both the single-class and multi-class classifiers are applied to the inspection data to assign the defect to one of the defect classes.