Abstract: A secondary battery according to an embodiment of the present disclosure includes an electrode assembly to which an electrode lead is attached; a case accommodating the electrode assembly so that a portion of the electrode lead is exposed to the outside; a sealing portion formed in the case to seal the electrode assembly; and a lead film covering a portion of an outer surface of the electrode lead and interposed between the electrode lead and the sealing portion, wherein the electrode lead includes a first region not corresponding to the lead film and exposed to the outside of the case, a second region corresponding to the lead film, and a third region not corresponding to the lead film and not exposed to the outside of the case, wherein a separation groove may be located in the third region.

Abstract: A deep learning-based image stitching method and apparatus are provided. The deep learning-based image stitching method includes receiving a plurality of images including a first image and a second image stitched to the first image, extracting a feature map of the plurality of images, generating a plurality of flow maps indicating movement information of pixels of the plurality of images, based on the feature map, generating a plurality of warping images in which the pixels of the plurality of images are moved, by applying the plurality of flow maps to the plurality of images, respectively, generating a plurality of weight maps for blending the plurality of warping images, based on the feature map, and generating a stitch image in which the first image and the second image are stitched, by blending the plurality of warping images based on the plurality of weight maps.

Abstract: Disclosed herein are a method and apparatus for generating a panoramic image. The method includes configuring a data set for training a deep learning network based on K images, extracting an encoding feature map, a skip connection feature map, and a decoding feature map based on K/2 images, among the images, estimating multiple homographies based on the encoding feature map, estimating a flow adjustment map, a preprocessing blending map, a weight map, and a post-processing blending map based on the decoding feature map, deriving a flow map based on the multiple homographies and the flow adjustment map, generating a preprocessed image based on the preprocessing blending map, generating multiple warped images based on the preprocessed image and the flow map, generating a matching image based on the warped images and the weight map, and correcting the matching image based on the matching image and the post-processing blending map.

Abstract: A deep learning-based image stitching method and apparatus are provided. The deep learning-based image stitching method includes receiving a plurality of images including a first image and a second image stitched to the first image, extracting a feature map of the plurality of images, generating a plurality of flow maps indicating movement information of pixels of the plurality of images, based on the feature map, generating a plurality of warping images in which the pixels of the plurality of images are moved, by applying the plurality of flow maps to the plurality of images, respectively, generating a plurality of weight maps for blending the plurality of warping images, based on the feature map, and generating a stitch image in which the first image and the second image are stitched, by blending the plurality of warping images based on the plurality of weight maps.