Hrl Laboratories, LLC

Maquet Cardiovascular LLC

Gm Global Technolgoy Operations, Inc.

Adobe, Inc.

Amit Agarwal

Rajan Bhattacharyya

Hyukseong Kwon

Amir M Rahimi

Michael C Stewart

Kevin Lee

Alexie Pogue

Albert K Chin

Ryan Charles Abbott

Arthur M Lin

Michael J Daily

Peter Tachi Callas

Juan I Perez

Gavin D Holland

Joseph N Lamberti

Alfredo Cantu

Amar Kendale

Alfredo Cantu R

Fred Villagomez

Frederick Barrigar

Described is a system for adapting to perception errors in object detection and recognition. The system receives, with a perception module, perception data from an environment proximate a mobile platform that reflects objects in the environment. Perception probes representing perception characteristics of object detections are generated from the perception data. Using the perception probes, spatial logic-based constraints and temporal logic-based constraints are generated. Spatial perception parameters are determined by solving an optimization problem using a set of the spatial logic-based constraints. Temporal perception parameters are determined by solving an optimization problem using a set of temporal logic-based constraints. The spatial perception parameters and the temporal perception parameters are combined to estimate a final perception parameter. The perception module is adjusted based on the final perception parameter.

Perception adaptation using probabilistic signal spatio-temporal logic system

Described is a system for action recognition error detection and correction using probabilistic signal temporal logic. The system is initiated by training an action recognition system to generate true positive (TP)/false positive (FP) axioms. Thereafter, the system ca be used to classify one or more actions in a video sequence as true action classifications by using the TP/FP axioms to remove false action classifications. With the remaining true classifications, a device can be controlled given the situation and relevant true classification.

System for action recognition error detection and correction using probabilistic signal temporal logic

Described is a system for evaluating and correcting perception errors in object detection and recognition. The system receives perception data from an environment proximate a mobile platform. Perception probes are generated from the perception data which describe perception characteristics of object detections in the perception data. For each perception probe, probabilistic distributions for true positive and false positive values are determined, resulting in true positive and false negative perception probes. Statistical characteristics of true positive perception probes and false positive perception probes are then determined. Based on the statistical characteristics, true positive perception probes are clustered. An axiom is generated to determine statistical constraints for perception validity for each perception probe cluster. The axiom is evaluated to classify the perception probes as valid or erroneous. Optimal perception parameters are generated by solving an optimization problem based on the axiom. The perception module is adjusted based on the optimal perception parameters.

Automated probabilistic axiom generation and incremental updates

A method using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

Unsupervised velocity prediction and correction for urban driving entities from sequence of noisy position estimates

Described is a system for detecting and correcting perception errors in a perception system. In operation, the system generates a list of detected objects from perception data of a scene, which allows for the generation of a list of background classes from backgrounds in the perception data associated with the list of detected objects. For each detected object in the list of detected objects, a closest background class is identified from the list of background classes. Vectors can then be used to determine a semantic feature, which is used to identify axioms. An optimal perception parameter is then generated, which is used to adjust perception parameters in the perception system to minimize perception errors.

Generation and usage of semantic features for detection and correction of perception errors

Described is a system for contrast and entropy-based perception adaption to optimize perception. The system is operable for receiving an input image of a scene with a camera system and detecting one or more objects (having perception data) in the input image. The perception data of the one or more objects is converted into probes, which are then converted into axioms using probabilistic signal temporal logic. The axioms are evaluated based on probe bounds. If the axioms are within the probe bounds, then results are provided; however, if the axioms are outside of the probe bounds, the system estimates optimal contrast bounds and entropy bounds as perception parameters. The contrast and entropy in the camera system are then adjusted based on the perception parameters.

Contrast and entropy based perception adaptation using probabilistic signal temporal logic based optimization

Described is a system to evaluate and reduce perception error in object detection and recognition. The system includes a perception module that receives perception data (of an object(s)) from an environment proximate a mobile platform. Perception probes are generated that describe one or more characteristics of the objects. The perception probes are converted into probabilistic signal temporal logic (PSTL)-based constraints that provide axioms having statistical analysis of the perception probes. The axioms are evaluated to classify the perception probes as valid or erroneous. Optimal perception parameters are generated by solving an optimization problem based on the axioms, which allows the system to adjust the perception module based on the optimal perception parameters.

System and method of perception error evaluation and correction by solving optimization problems under the probabilistic signal temporal logic based constraints

A surgical blower for providing a directed stream to clear a surgical site, that can be used in multiple different use modes. In one use mode, the surgical blower is handheld by a user during operation. In another mode, a distal end portion of the blower is fixed to a foot of a stabilizer instrument and the handle of the blower is handheld. In another mode, a distal end portion of the blower is fixed to a foot of a stabilizer instrument and the handle of the blower is fixed to a stationary object such as a sternal retractor. In another mode, the distal end of the blower is free and the handle of the blower is fixed to a stationary object. An apparatus including a blower and a stabilizer is provided. Methods of using the blower as well as the apparatus are also provided.

Blower instrument, apparatus and methods of using

Devices, tools and methods for performing minimally invasive surgical procedures. Methods of performing minimally invasive ablation procedures. Methods of performing rapid exchange of tools in a device while the device remains in a reduced-access surgical space.

Apparatus and methods for performing minimally-invasive surgical procedures

A computer system receives a command to initiate setting a display mode of its corresponding display screen to one in which received input commands (e.g., mouse input, keyboard input, etc.) are directed to multiple display applications running on the computer system instead of only a single display application in a foreground (e.g., a 'topmost' window presented to a user) on the computer display screen. For example, based on input from a user, an auxiliary application herein receives an identification of at least a first display application and a second display application that each display information on different respective windows of the display screen. Thereafter, the auxiliary application duplicates received input commands and forwards the received input commands to both the first display application and the second display application, alleviating the user from having to separately apply the same commands to each of the first and second display applications.

Growing community of inventors

Monterey Park, CA, United States of America

Amit Agarwal