Hrl Laboratories, LLC

Raytheon Company

Payam Saisan

Yuri Owechko

Swarup S Medasani

Shubha Kadambe

Described is a system for dynamic background estimation which utilizes Particle Swarm Optimization (PSO). The present invention comprises a system, method, and computer program product for accurate estimation of a background mask corresponding to a dynamically changing scene. The system is configured to construct a background template model of a scene, and then capture an image of a current view of the scene with a camera. Thereafter, the system generates an image-based template matching cost function as an optimization problem, where the objective is to identify and fit a corresponding subregion of the background template model to the current camera view. The cost function is optimized using a PSO search algorithm. Finally, the system is configured to generate the corresponding subregion of the background template model for display. The inherent efficiency of PSO makes this system conducive for use in applications requiring real-time background estimation.

Dynamic background estimation for video analysis using evolutionary optimization

In one aspect, a method to reduce false alarms in identifying whether a candidate image is from an object class includes projecting the candidate image onto an object class subspace and projecting the candidate image onto a non-object class subspace. The method also includes determining whether the candidate image is from the object class using a Bayesian decision function based on the projections on the object class subspace and the non-object class subspace. In another aspect, a method to reduce false alarms in identifying whether a candidate mine image is from a mine class includes projecting the candidate mine image onto a mine subspace and projecting the candidate mine image onto a non-mine subspace. The method also includes determining whether the candidate mine image represents a mine using a Bayesian decision function based on the projections on the mine class subspace and the non-mine class subspace.

Reducing false alarms in identifying whether a candidate image is from an object class

Described is a system for identifying a concealed object of interest. The system is initialized by receiving an image in grayscale. The image is processed image to identify feature rich regions, which are identified as a binary mask describing edge pixels and non-edge pixels. A set of edge pixels are then randomly sampled. An object detector function is then used on the set of edge pixels to determine if an object of interest is present in the set of edge pixels.

Shape-based object detection and localization system

The invention is a novel method and apparatus for optimal placement of actuators responsible for shaping of elastically deformable structures into desired target shapes using sparse number of actuators such that the discrepancy between deformed surfaces and target shapes is minimized. The invention utilizes a computational algorithm for optimal placement of actuators using particle swarm optimization, a biologically inspired evolutionary optimization paradigm that searches for the minima (or maxima) of objective functions with possibly large number of parameters.

Method and apparatus for optimal placement of actuators for shaping deformable materials into desired target shapes

In one aspect, a method to identify a candidate object includes receiving an image of the candidate object and projecting the received image onto an image subspace. The image subspace is formed from images of known objects of a class. The method also includes determining whether the candidate object is in the object class based on the received image and the image subspace using a likelihood ratio. The likelihood ratio includes a first probability density indicating a probability an object is in the object class and a second probability density indicating a probability an object is not in the class. The first probability density and the second probability are each a function of a distance of the received image to the image subspace.

Identifying whether a candidate object is from an object class

Described is a system for anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing. The system is configured to generate a subspace model for a normal class using training images. The normal class represents normal objects in a common class. The system receives a novel image having an object in the common class. A set of geometric landmarks are identified in the object in the novel image for use in registering the image. The novel image is registered by warping the image so that the geometric landmarks coincide in the novel image and the training images, resulting in a warped novel image having an object. Thereafter, the system determines if the object in the warped novel image is anomalous by measuring the distance of the warped novel image from the subspace model. Finally, if anomalous, an operator is notified accordingly.

System for anomaly detection using sub-space analysis

The present invention provides a method and apparatus for sensing and determining the shapes of deformable materials, shaped by a plurality of actuators, using sparse sensor network topologies. The method comprises placing a plurality of curvature sensors on the surface of a deformable material to measure the local curvatures of the deformable material. The plurality of curvature sensors generate a collection of sensor data correlated to the local curvatures. Actuator parameters are generated from sensor data by assuming linear approximation between sensors data and actuator parameters. The shape of the deformable material is determined from the generated actuator parameters.

Precision shape sensing of elastically deformable materials

An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.

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Santa Monica, CA, United States of America

Payam Saisan