Abstract: A computer-implemented method of training an auxiliary machine learning model to predict a set of new parameters of a primary machine learning model, wherein the primary model is configured to transform from an observed subset of a set of real-world features to a predicted version of the set of real-world features.

Abstract: According to examples, an apparatus may identify a first viewpoint that an entity expressed in a first media file, identify a second viewpoint expressed in a second media file that is attributed to the entity, determine that the second viewpoint is dissimilar to the first viewpoint and in response to the determination that the second viewpoint is dissimilar to the first viewpoint, may output a message.

Abstract: In some examples, a camera tracking failure in a simultaneous localization and mapping (SLAM) process with respect to a first SLAM map may be identified. Responsive to identification of the camera tracking failure, a second SLAM map for the SLAM process may be initialized. A video frame tracked through the second SLAM map may be accessed. Matched features between the video frame and individual keyframes of the first SLAM map may be identified to determine a keyframe subset. The keyframe subset may be analyzed to determine a candidate camera position from among the keyframe subset. The candidate camera position may be tracked with respect to the first SLAM map for subsequent video frames. The first SLAM map may be stitched to the second SLAM map responsive to tracking the candidate camera position with respect to the first SLAM map for the subsequent.

Abstract: A plurality of binary support vector machines (SVM) are trained to vote between two of a plurality of classes. Each of the binary SVMs vote between a different pair of the plurality of classes. A set of validated samples is input to each of the binary SVMs. The set of validated samples includes samples of each of the classes. Likelihood estimates are determined based on votes of the binary SVMs. A prior probability distribution (prior) is assigned for each of the classes. A posterior probability is calculated for each of the classes based on the determined likelihood estimates and the assigned priors.

Abstract: According to examples, an index of entries may be retrieved, in which each of the entries may correspond to phrases that are analogous to other phrases in content on a network. A plurality of phrases that are similar to the entries in the index may be identified in a content accessed on the apparatus and a determination may be made, based on a user interaction with the accessed content via a user interface, that the user has read a first phrase in the plurality of phrases. The index may be updated to indicate that the user has read the first phrase and, based on the updated index, a second phrase in a subsequently accessed content that is similar to the first phrase may be determined. In addition, the second phrase in the subsequently accessed content may be displayed differently from other displayed phrases in the subsequently accessed content.

Abstract: In some examples, a system includes a color cluster learning engine and a color recognition engine. The color cluster learning engine may be configured to obtain a set of training images, process the training images to obtain clusters of pixel colors for the training images, identify learned color clusters from the clusters of pixel colors obtained from the training images, and label the learned color clusters with color indicators. The color recognition engine may be configured to receive an input image for color identification, process the input image to obtain a particular cluster of pixel colors that covers the highest number of pixels in the input image, match the particular cluster to a particular learned color cluster labeled with a particular color indicator, and identify a color of the input image as specified by the particular color indicator.

Abstract: A pair of video frames may be used for simultaneous localization and mapping (SLAM) initialization. The pair of frames may be determined according to a translation threshold. Whether the translation threshold is met may be determined by obtaining the pair of video frames and estimating the translation between the frames.

Abstract: Examples disclosed herein relate to detecting change in a 3-dimensional (3D) view. The examples enable determining a first set of sparse local features from a first set of frames of an initial 3D scene; determining whether the initial 3D scene is different from a current 3D scene based on the first set of sparse local features; and providing information about any determined differences between the initial 3D scene and the current 3D scene.

Abstract: According to examples, an apparatus may identify a first viewpoint that an entity expressed in a first media file, identify a second viewpoint expressed in a second media file that is attributed to the entity, determine that the second viewpoint is dissimilar to the first viewpoint and in response to the determination that the second viewpoint is dissimilar to the first viewpoint, may output a message.

Abstract: According to examples, an index of entries may be retrieved, in which each of the entries may correspond to phrases that are analogous to other phrases in content on a network. A plurality of phrases that are similar to the entries in the index may be identified in a content accessed on the apparatus and a determination may be made, based on a user interaction with the accessed content via a user interface, that the user has read a first phrase in the plurality of phrases. The index may be updated to indicate that the user has read the first phrase and, based on the updated index, a second phrase in a subsequently accessed content that is similar to the first phrase may be determined. In addition, the second phrase in the subsequently accessed content may be displayed differently from other displayed phrases in the subsequently accessed content.

Abstract: In some examples, a camera tracking failure in a simultaneous localization and mapping (SLAM) process with respect to a first SLAM map may be identified. Responsive to identification of the camera tracking failure, a second SLAM map for the SLAM process may be initialized. A video frame tracked through the second SLAM map may be accessed. Matched features between the video frame and individual keyframes of the first SLAM map may be identified to determine a keyframe subset. The keyframe subset may be analyzed to determine a candidate camera position from among the keyframe subset. The candidate camera position may be tracked with respect to the first SLAM map for subsequent video frames. The first SLAM map may be stitched to the second SLAM map responsive to tracking the candidate camera position with respect to the first SLAM map for the subsequent.

Abstract: In some examples, a system includes a color cluster learning engine and a color recognition engine. The color cluster learning engine may be configured to obtain a set of training images, process the training images to obtain clusters of pixel colors for the training images, identify learned color clusters from the clusters of pixel colors obtained from the training images, and label the learned color clusters with color indicators. The color recognition engine may be configured to receive an input image for color identification, process the input image to obtain a particular cluster of pixel colors that covers the highest number of pixels in the input image, match the particular cluster to a particular learned color cluster labeled with a particular color indicator, and identify a color of the input image as specified by the particular color indicator.

Abstract: Examples disclosed herein relate to detecting change in a 3-dimensional (3D) view. The examples enable determining a first set of sparse local features from a first set of frames of an initial 3D scene; determining whether the initial 3D scene is different from a current 3D scene based on the first set of sparse local features; and providing information about any determined differences between the initial 3D scene and the current 3D scene.

Abstract: A plurality of binary support vector machines (SVM) are trained to vote between two of a plurality of classes. Each of the binary SVMs vote between a different pair of the plurality of classes. A set of validated samples is input to each of the binary SVMs. The set of validated samples includes samples of each of the classes. Likelihood estimates are determined based on votes of the binary SVMs. A prior probability distribution (prior) is assigned for each of the classes. A posterior probability is calculated for each of the classes based on the determined likelihood estimates and the assigned priors.

Abstract: A pair of video frames may be used for simultaneous localization and mapping (SLAM) initialization. The pair of frames may be determined according to a translation threshold. Whether the translation threshold is met may be determined by obtaining the pair of video frames and estimating the translation between the frames.