Cognex Corporation

Amazon Technologies, Inc.

Vivek Anand

Lowell D Jacobson

Lei Wang

David Y Li

Krutarth Mukesh Gathani

Sajin George

Hector Jonathan Espinoza

Systems and techniques for tamper detection of electronic devices are described. The tamper detection is performed using a tamper circuit that includes an authenticator connected to an auxiliary power source of the electronic device via a switch. The switch is configured to open and break the connection between the authenticator and the auxiliary power source when a housing of the electronic device is opened or tampered with. The authenticator may be evaluated, and a value compared against a server-based value to determine a difference in values and thereby detect tampering.

Tamper detection systems for electronic devices

This invention provides a system and method for finding patterns in images that incorporates neural net classifiers. A pattern finding tool is coupled with a classifier that can be run before or after the tool to have labeled pattern results with sub-pixel accuracy. In the case of a pattern finding tool that can detect multiple templates, its performance is improved when a neural net classifier informs the pattern finding tool to work only on a subset of the originally trained templates. Similarly, in the case of a pattern finding tool that initially detects a pattern, a neural network classifier can then determine whether it has found the correct pattern. The neural network can also reconstruct/clean-up an imaged shape, and/or to eliminate pixels less relevant to the shape of interest, therefore reducing the search time, as well significantly increasing the chance of lock on the correct shapes.

System and method for finding and classifying patterns in an image with a vision system

This invention provides a system and method for finding line features in an image that allows multiple lines to be efficiently and accurately identified and characterized. When lines are identified, the user can train the system to associate predetermined (e.g. text) labels with respect to such lines. These labels can be used to define neural net classifiers. The neural net operates at runtime to identify and score lines in a runtime image that are found using a line-finding process. The found lines can be displayed to the user with labels and an associated probability score map based upon the neural net results. Lines that are not labeled are generally deemed to have a low score, and are either not flagged by the interface, or identified as not relevant.

System and method for finding and classifying lines in an image with a vision system

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Chelmsford, MA, United States of America

Vivek Anand