Abstract: A method and a computing device for object detection and tracking from a video input are described. The method and the computing device may be used to, for example, track objects of interest, such as lane markings, in traffic. A plurality of frames corresponding to a video may be analyzed in a spatiotemporal domain by a neural network. The neural network may be trained using data synthesized in the spatiotemporal domain.

Abstract: Systems and methods are disclosed for error correction in convolutional neural networks. In one implementation, a first image is received. A first activation map is generated with respect to the first image within a first layer of the convolutional neural network. A correlation is computed between data reflected in the first activation map and data reflected in a second activation map associated with a second image. Based on the computed correlation, a linear combination of the first activation map and the second activation map is used to process the first image within a second layer of the convolutional neural network. An output is provided based on the processing of the first image within the second layer of the convolutional neural network.

Abstract: A system for rendering a merged virtual 3D augmented replica of a 3D product image and a 3D model image of a body part. A 3D modeling engine transforms an acquired 2D image of a body part into a 3D augmented replica thereof. A GUI enables the merging, displaying and manipulating of the 3D product image and the 3D augmented replica of a body part.

Abstract: A system for rendering a merged virtual 3D augmented replica of a 3D product image and a 3D model image of a body part. A 3D modeling engine transforms an acquired 2D image of a body part into a 3D augmented replica thereof. A GUI enables the merging, displaying and manipulating of the 3D product image and the 3D augmented replica of a body part.