A computer-implemented method is provided for analyzing videos of a living system captured with microscopic imaging. The method can include obtaining a base dataset including one or more videos captured with microscopic imaging with at least one of the one or more videos including a cellular event, and cropping out, from the base dataset, sub-videos including one or more objects of interest that may be involved in the cellular event. An artificial neural network (ANN) model can be trained using the plurality of selected sub-videos as training data, to perform unsupervised video alignment, a query sub-video can be aligned using the trained ANN model, and a determination can be made whether or not the query sub-video includes the cellular event.

A detection device detecting a scene related to a sponsor credit included in a commercial message from a target video is provided. The detection device comprises a detection unit that associates, from a preliminary video, a still image related to the sponsor credit with an audio signal related to the sponsor credit included other than in a frame or an audio signal configuring the commercial message so as to detect the scene related to the sponsor credit from the target video.

In one aspect, an example method includes (i) receiving a first group of video content items; (ii) identifying from among the first group of video content items, a second group of video content items having a threshold extent of similarity with each other; (iii) determining a quality score for each video content item of the second group; (iv) identifying from among the second group of video content items, a third group of video content items each having a quality score that exceeds a quality score threshold; and (v) based on the identifying of the third group, transmitting at least a portion of at least one video content item of the identified third group to a digital video-effect (DVE) system, wherein the system is configured for using the at least the portion of the at least one video content item of the identified third group to generate a video content item.

An apparatus and method relating to use of a physical writing surface (132) during a videoconference or presentation. Snapshots of a whiteboard (132) are identified by applying a difference measure to the video data (e.g., as a way of comparing frames at different times). Audio captured by a microphone may be processed to generate textual data, wherein a portion of the textual data is associated with each snapshot. The writing surface may be identified (enrolled) using gestures. Image processing techniques may be used to transform views of a writing surface.

Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Methods and systems for filtering video content items are described herein. The system identifies a plurality of video content items that are linked to respective image content items. The system determines, for each of the plurality of video content items, whether a video content item corresponds to a respective image content item. In response to the determining, the system causes to be provided information identifying the plurality of video content items excluding video content items that do not correspond to respective image content items.

The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

Machine-based video classifying to identify misleading videos by training a model using a video corpus, obtaining a subject video from a content server, generating respective feature vectors of a title, a thumbnail, a description, and a content of the subject video, determining a first semantic similarities between ones of the feature vectors, determining a second semantic similarity between the title of subject video and titles of videos in the misleading video corpus in a same domain as the subject video, determining a third semantic similarity between comments of the subject video and comments of videos in the misleading video corpus in the same domain as the subject video, classifying the subject video using the model and based on the first semantic similarities, the second semantic similarity, and the third semantic similarity, and outputting the classification of the subject video to a user.

A content segmentation system includes a computing platform having processing hardware and a system memory storing a software code and a trained machine learning model. The processing hardware is configured to execute the software code to receive content, the content including multiple sections each having multiple content blocks in sequence, to select one of the sections for segmentation, and to identify, for each of the content blocks of the selected section, at least one respective representative unit of content. The software code is further executed to generate, using the at least one respective representative unit of content, a respective embedding vector for each of the content blocks of the selected section to provide a multiple embedding vectors, and to predict, using the trained machine learning model and the embedding vectors, subsections of the selected section, at least some of the subsections including more than one of the content blocks.

A content device and method is disclosed to include a processing device to process streaming video content. A fingerprinter receives captured frames of the streaming video content and, for each frame of a plurality of the captured frames, generates a one-dimensional histogram function of pixel values and transforms the histogram function with a Fast Fourier Transform (FFT), to generate a plurality of complex values for the frame. The fingerprinter further, for each of the plurality of complex values, assigns a binary one (“1”) when a real part of the complex value is greater than zero (“0”) and assigns a binary zero (“0”) when the real part is less than or equal to zero, to generate a plurality of bits. The fingerprinter further concatenates a specific number of the bits to generate a fingerprint for the frame.