Abstract: Disclosed therein are a method, an apparatus, and a storage medium for image encoding/decoding. Templating matching is used for prediction for a target block. Prediction information to be applied to encoding of the target block is determined, and prediction for the target block that uses the determined prediction information is performed. The prediction information includes a list and multiple candidates in the list. When the multiple candidates in the list are configured, template matching between blocks may be used. A candidate selected from among multiple candidates configured using template matching is used for prediction for the target block.

Abstract: The present invention relates to image processing, and more particularly, to a video coding/decoding method using a clipped motion vector and an apparatus thereof. An embodiment of the present invention relates to a method of decoding an image. The method includes clipping a motion vector of a reference picture in a predetermined dynamic range to generate a clipped motion vector, storing the clipped motion vector in a buffer, deriving a motion vector of a coding treeblock using the motion vector stored in the buffer, and performing inter prediction decoding process using the motion vector of the coding treeblock. According to the exemplary embodiment of the present invention, a size of a memory required for storing motion vectors may be reduced.

Abstract: A redundant control system is applied to a brake-by-wire (BBW) system. The redundant control system applied to the BBW system includes electromechanical brakes (EMBs) provided at wheels of a vehicle and configured to perform brake control of the vehicle, controllers connected to the EMBs, respectively, and a local gateway on a first communication line configured to receive information on the vehicle and a command of a driver and to transmit the information on the vehicle and the command of the driver to the controllers, where the controllers are configured to receive the information on the vehicle and the command of the driver through a second communication line.

Abstract: Provided is a device for upscaling resolution based on a slice image, in which a low-resolution image is divided into a plurality of slice images so that a high-resolution image is generated. The device includes a convolution operation unit configured to convert a low-resolution input slice image into a high-resolution output slice image using a convolutional neural network. The convolutional neural network includes a cascading block configured to perform a convolution operation using a convolution filter having a predetermined size and a residual operation on an input feature map generated from the low-resolution input slice image to generate an output feature map, and an upscaling block configured to upscale the output feature map to generate the high-resolution output slice image.

Abstract: A control method of a fastening tool for fastening a component part includes photographing a fastening portion of the component part through a camera portion mounted on the fastening tool. The control method includes pre-processing that rotates an input image, which has been photographed by the camera portion, to match with representative model image. The control method includes estimating the fastening portion through an inference process through a convolutional neural network (CNN) algorithm-based image classification work on a video input image of a same fastening portion finished with the pre-processing work. The control method also includes setting a torque value that is matched with a recognized fastening portion when a probability value of the fastening portion in the inference process exceeds a predetermined reference ratio.

Abstract: A fastening tool system for fastening a component part by using a supplied power includes: a camera portion mounted on a fastening tool to photograph a fastening portion of the component part; an image input portion for inputting an image photographed from the camera portion when a distance with the fastening portion measured through a distance measurement sensor is within a predetermined distance; a communication portion for transmitting data by wireless communication; and a fastening portion determining portion that recognizes the fastening portion through machine learning algorithm-based image classification work for an input image of the camera portion and sets a torque value matched with the recognized fastening portion.

Abstract: Provided is a device for upscaling resolution based on a slice image, in which a low-resolution image is divided into a plurality of slice images so that a high-resolution image is generated. The device includes a convolution operation unit configured to convert a low-resolution input slice image into a high-resolution output slice image using a convolutional neural network. The convolutional neural network includes a cascading block configured to perform a convolution operation using a convolution filter having a predetermined size and a residual operation on an input feature map generated from the low-resolution input slice image to generate an output feature map, and an upscaling block configured to upscale the output feature map to generate the high-resolution output slice image.

Abstract: A fastening tool system for fastening a component part by using a supplied power includes: a camera portion mounted on a fastening tool to photograph a fastening portion of the component part; an image input portion for inputting an image photographed from the camera portion when a distance with the fastening portion measured through a distance measurement sensor is within a predetermined distance; a communication portion for transmitting data by wireless communication; and a fastening portion determining portion that recognizes the fastening portion through machine learning algorithm-based image classification work for an input image of the camera portion and sets a torque value matched with the recognized fastening portion.

Abstract: An image processing apparatus for transforming a wide-angle image into a narrow-angle image includes a deblurrer performing deblurring on the wide-angle image to generate a deblurred image; a super-resolution (SR) unit performing SR processing on the deblurred image to generate an upscaled transformed image; and a view transformer performing view transformation to obtain the narrow-angle image based on the wide-angle image and the upscaled transformed image.

Abstract: An image processing apparatus for transforming a wide-angle image into a narrow-angle image includes a deblurrer performing deblurring on the wide-angle image to generate a deblurred image; a super-resolution (SR) unit performing SR processing on the deblurred image to generate an upscaled transformed image; and a view transformer performing view transformation to obtain the narrow-angle image based on the wide-angle image and the upscaled transformed image.