Abstract: A method is disclosed for classifying and/or counting objects (for example, cells) in an image that contains a mixture of several types of objects. Prior statistical information about the object mixtures (class proportion data) is used to improve classification results. The present technique may use a generative model for images containing mixtures of object types to derive a method for classifying and/or counting cells utilizing both class proportion data and classified object templates. The generative model describes an image as the sum of many images with a single cell, where the class of each cell is selected from some statistical distribution. Embodiments of the present techniques have been successfully used to classify white blood cells in images of lysed blood from both normal and abnormal blood donors.

Abstract: The present invention provides a system and method for structured low-rank matrix factorization of data. The system and method involve solving an optimization problem that is not convex, but theoretical results should that a rank-deficient local minimum gives a global minimum. The system and method also involve an optimization strategy that is highly parallelizable and can be performed using a highly reduced set of variables. The present invention can be used for many large scale problems, with examples in biomedical video segmentation and hyperspectral compressed recovery.

Abstract: The present invention provides a system and method for structured low-rank matrix factorization of data. The system and method involve solving an optimization problem that is not convex, but theoretical results should that a rank-deficient local minimum gives a global minimum. The system and method also involve an optimization strategy that is highly parallelizable and can be performed using a highly reduced set of variables. The present invention can be used for many large scale problems, with examples in biomedical video segmentation and hyperspectral compressed recovery.