Abstract: A text partitioning method, a text classifying method, an apparatus, a device and a storage medium, wherein the method includes: parsing a content image, to obtain a target text in a text format; according to a line break in the target text, partitioning the target text into a plurality of text sections; and according to a first data-volume threshold, partitioning sequentially the plurality of text sections into a plurality of text-to-be-predicted sets, wherein a data volume of a last one text section in each of the text-to-be-predicted sets is greater than a second data-volume threshold.

Abstract: A video processing method and apparatus, a computing device and a computer readable storage medium are disclosed. The video processing method includes obtaining a video frame sequence comprised in a video to be processed, performing scene classification identification on the video frame sequence so as to obtain a scene classification identification result of the video frame sequence, and performing a smoothing process to the video frame sequence based on the scene classification identification result.

Abstract: The contents of totes and the amount a tote container is filled are monitored. A central database stores product dimensions and tote dimensions as well as a percentage of which a tote is to be filled. Image scanners that obtain three-dimensional images are used to determine if the actual dimensions of the tote, the product, and the fill amount are consistent with the expected or optimal values of these characteristics. When there is a discrepancy between measured and expected values, then one or more actions can be taken.

Abstract: An image processing method includes acquiring a set of image samples for training an attribute recognition model, wherein the set of image samples includes a first subset of image samples with category labels and a second subset of image samples without category labels; training a sample prediction model using the first subset of image samples, and predicting categories of the image samples in the second subset of image samples using the trained sample prediction model; determining a category distribution of the set of image samples based on the category labels of the first subset of image samples and the predicted categories of the second subset of image samples; and acquiring a new image sample if the determined category distribution does not conform to the expected category distribution, and adding the acquired new image sample to the set of image samples.

Abstract: Detecting cells depicted in an image using RNA segmentation can include obtaining a FISH image of a tissue that depicts multiple cells, obtaining a nuclear stained image of the tissue, and generating a mask that includes multiple areas that each have a position with respect to the tissue by enhancing structures depicted in the FISH image. Edges depicted in the enhanced FISH image are detected to use for the mask, and positions are determined for a first plurality of regions that fit potential nuclei depicted in the nuclear stained image. A second plurality of regions are selected from the first plurality by determining, using the mask, which regions from the first plurality overlap with the position of an area from multiple areas in the mask. Unique nuclei in the tissue are labelled using the second plurality of regions that each indicate a potential nuclei in the tissue.

Abstract: A system comprises a LiDAR sensor and a processor coupled to the LiDAR. The processor is configured to derive a regression line, and generate the outline of the static object by connecting the classified valid points except for the classified removal points. Wherein the processor is further configured to calculate an angle defined by one segment and another segment adjacent to the segment, select an i-lth point, which is a start point of a segment including an ith point, as a start point, when an angle between segments at both sides with respect to the ith point is greater than the reference angle, and select a jth point as an end point, when an angle between segments at both sides with respect to the jth point is less than or equal to the reference angle and when there is the start point before the jth point.

Abstract: Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a plurality of bone scale coefficients each corresponding to respective bones of a skeleton model; receiving a plurality of joint angle coefficients that collectively define a pose for the skeleton model; generating the skeleton model based on the received bone scale coefficients and the received joint angle coefficients; generating a base surface based on the plurality of bone scale coefficients; generating an identity surface by deformation of the base surface; and generating the 3D body model by mapping the identity surface onto the posed skeleton model.

Abstract: Aspects of the present disclosure relate to object-based image capture. Embodiments include identifying a reference point corresponding to an object in an image of a series of images. Embodiments include comparing a position of the reference point in the image to positions of one or more corresponding reference points in one or more previous images in the series of images. Embodiments include determining a total number of images in the series of images. Embodiments include selecting, based on the comparing and the total number of images in the series of images, between: capturing the image; or declining to capture the image.

Abstract: The disclosure relates to an object identification method and device as well as a non-transitory computer-readable storage medium. The object identification method includes: acquiring a first object image, and generating a first identification result group according to the first object image, wherein the first identification result group includes one or more first identification results arranged in order of confidence from high to low; acquiring a second object image, and generating a second identification result group based on the second object image, wherein the second identification result group includes one or more second identification results arranged in order of confidence from high to low; and determining whether the first object image and the second object image correspond to the same object to be identified according to the first identification result group and the second identification result group.

Abstract: A method for recognizing a key time point in a video includes: obtaining at least one video segment by processing each image frame in the video by an image classification model; determining a target video segment in the at least one video segment based on a shot type; obtaining respective locations of a first object and a second object in an image frame of the target video segment by an image detection model; and based on a distance between the location of the first object and the location of the second object in the image frame satisfying a preset condition, determining a time point of the image frame as the key time point of the video.

Abstract: An information processing apparatus includes circuitry configured to: acquire, as a first image, a read image of an image formed on a medium; generate a second image based on document data; detect reference points from the second image; compare the first image with the second image to calculate an amount of positional deviation of the medium for each of the reference points; detect, based on the amount of positional deviation, a malfunction of a conveying device that conveys the medium; cause an image forming apparatus to print a correction chart for correction of conveyance of the medium, in response to detection of the malfunction of the conveying device; calculate, from a read image of the correction chart, an amount of fluctuation in conveyance of the medium performed by the conveying device; and calculate a correction value for the calculated amount of fluctuation in conveyance of the medium.

Abstract: Provided is a mixed reality-based experience simulator including an imaging part including first and second cameras installed on an experience object to capture images of different viewpoints from the experience object, a display part worn by a participant and configured to display a mixed reality image of the experience object to the participant, and an image processing unit configured to provide the mixed reality image to the display part and configured to generate the mixed reality image such that images captured by the first and second cameras are included in the mixed reality image. The mixed reality-based experience simulator according to the present invention generates a mixed reality image on the basis of images captured by a plurality of cameras installed on an experience object and provides the generated mixed reality image to a participant so that a more realistic experience is provided.

Abstract: A learning data generation device of an embodiment includes a detection unit that detects a changed region in a space between a first map and a second map based on first map data which indicate the first map and second map data which indicate the second map which indicates a substantially same space as the first map and represents the space at a different time point from the first map and a changed region image acquisition unit that acquires images which correspond to the changed region detected by the detection unit from a first image in which the space represented by the first map is captured and a second image in which the space represented by the second map is captured.