Abstract: System and method for detecting objects in geospatial images, 3D point clouds and Digital Surface Models (DSMs). Deep Convolution Neural Networks (DCNNs) are trained using positive and negative training examples. Using a rotation pattern match of only positive examples reduces the number of negative examples required. In DCNNs softmax probability is variant of rotation angles. When rotation angle is coincident with object orientation, softmax probability has maximum value. During training, positive examples are rotated so that their orientation angles are zero. During detection, test images are rotated through different angles. At each angle, softmax probability is computed. A final object detection is based on maximum softmax probability as well as a pattern match between softmax probability patterns of all positive examples and the softmax probability pattern of a target object at different rotation angles.

Abstract: System and method for detecting objects in geospatial images, 3D point clouds and Digital Surface Models (DSMs). Deep Convolution Neural Networks (DCNNs) are trained using positive and negative training examples. Using a rotation pattern match of only positive examples reduces the number of negative examples required. In DCNNs softmax probability is variant of rotation angles. When rotation angle is coincident with object orientation, softmax probability has maximum value. During training, positive examples are rotated so that their orientation angles are zero. During detection, test images are rotated through different angles. At each angle, softmax probability is computed. A final object detection is based on maximum softmax probability as well as a pattern match between softmax probability patterns of all positive examples and the softmax probability pattern of a target object at different rotation angles.

Abstract: A method for detecting a message loop, a routing agent apparatus, and a networking system. The method for detecting a message loop includes: receiving a request message sent by a network element apparatus; acquiring routing information for sending the request message to a server apparatus; detecting whether a next-hop link for sending the request message in the routing information and a link for receiving the request message belong to a same link set; and sending a response to the network element apparatus that a message loop is detected, when it is detected that the link for sending the request message in the routing information and the link for receiving the request message belong to a same link set. The technical solutions disclosed herein can improve the efficiency of detecting the message loop and thus have strong practicability.

Abstract: A system and method for finding real terrain matches in a stereo image pair is presented. A method for finding differences of underlying terrain between a first stereo image and a second stereo image includes performing epipolar rectification on a stereo image pair to produce rectified image data. The method performs a hybrid stereo image matching on the rectified image data to produce image matching data. A digital surface model (DSM) is generated based on the image matching data. Next, the method identifies areas in the DSM where the stereo image matching should fail based on the image matching data and the DSM to generate predicted failures. The method can then determine real terrain changes based on the predicted failures and the image matching data.

Abstract: A system and method for finding real terrain matches in a stereo image pair is presented. A method for finding differences of underlying terrain between a first stereo image and a second stereo image includes performing epipolar rectification on a stereo image pair to produce rectified image data. The method performs a hybrid stereo image matching on the rectified image data to produce image matching data. A digital surface model (DSM) is generated based on the image matching data. Next, the method identifies areas in the DSM where the stereo image matching should fail based on the image matching data and the DSM to generate predicted failures. The method can then determine real terrain changes based on the predicted failures and the image matching data.