GENERATION OF SYNTHETIC 3-DIMENSIONAL OBJECT IMAGES FOR RECOGNITION SYSTEMS

    公开(公告)号:US20180357834A1

    公开(公告)日:2018-12-13

    申请号:US16053135

    申请日:2018-08-02

    Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

    METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO DETECT CODE DEFECTS

    公开(公告)号:US20220012163A1

    公开(公告)日:2022-01-13

    申请号:US17483431

    申请日:2021-09-23

    Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to detect code defects. An example apparatus includes repository interface circuitry to retrieve code repositories corresponding to a programming language of interest, tree generating circuitry to generate parse trees corresponding to code blocks contained in the code repositories, directed acyclic graph (DAG) circuitry to generate DAGs corresponding to respective ones of the parse trees, the DAGs including control flow information and data flow information, abstraction generating circuitry to abstract the DAGs, invariant identification circuitry to extract invariants from the abstracted DAGs, and DAG comparison circuitry to cluster respective ones of the extracted invariants to identify respective ones of the abstracted DAGs with common invariants.

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