A system and method for processing a 3D point cloud to generate a segmented point cloud in real time are disclosed, the method includes: receiving a sparse 3D point cloud captured by a detection and ranging sensor mounted to a vehicle, the 3D point cloud comprising a plurality of data points, each data point in the 3D point cloud having a set of coordinates in a coordinate system of the detection and ranging sensor; generating, from the 3D point cloud, a range map comprising a plurality of elements, each of the plurality of data points of the 3D point cloud occupying a respective element of the plurality of elements; labelling the data point in each respective element of the range map as one of a pole-like data point or a vertical-plane-like data point; and generating the segmented point cloud including one or more of the labeled data points.

The present subject matter refers a method for developing machine-learning (ML) based tool. The method comprises initializing an input dataset for undergoing ML based processing. The input dataset is pre-processed by a first model to harmonize features across the dataset. Thereafter, the dataset is annotated by a second model to define a labelled data set. A plurality of features are extracted with respect to the data set through a feature extractor. A selection of at-least a machine-learning classifier is received through an ML training module to operate upon the extracted features and classify the dataset with respect to one or more labels. A meta controller communicated with one or more of the first model, the second model, the feature extractor and the selected classifier for assessing a performance of at least one of first model and the feature extractor, a comparison of operation among the one or more selected classifier, and diagnosis of an unexpected operation with respect to one or more of the first model, the feature extractor and the selected classifier.

A method for retrieving an image is provided. The method includes: extracting a global feature and a local feature of an image to be retrieved, and a global feature and a local feature of an image to be recalled by employing a preset neural network model; determining a candidate image set by matching the global feature of the image to be retrieved with the global feature of the image to be recalled and matching the local feature of the image to be retrieved with the local feature of the image to be recalled; and determining a retrieval result from the candidate image set by performing local feature verification on the image to be retrieved and a candidate image in the candidate image set. An apparatus for retrieving an image, an electronic device, and a medium are further provided.

Embodiments are directed to techniques for interacting with a hierarchical video segmentation using a metadata panel with a composite list of video metadata. The composite list is segmented into selectable metadata segments at locations corresponding to boundaries of video segments defined by a hierarchical segmentation. In some embodiments, the finest level of a hierarchical segmentation identifies the smallest interaction unit of a video—semantically defined video segments of unequal duration called clip atoms, and higher levels cluster the clip atoms into coarser sets of video segments. One or more metadata segments can be selected in various ways, such as by clicking or tapping on a metadata segment or by performing a metadata search. When a metadata segment is selected, a corresponding video segment is emphasized on the video timeline, a playback cursor is moved to the first video frame of the video segment, and the first video frame is presented.

Aspects of the subject disclosure may include, for example, storing, in a database, a decorating style preference of a user; receiving, from user equipment of the user, one or more images (and/or one or more 2D environment models and/or one or more 3D environment models) depicting an environment in which remodeling is desired; generating, via a machine learning process, a first model to present by the user equipment, the generating the first model being based upon the decorating style preference and the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), the first model comprising a first remodeling proposal for the environment; sending, to the user equipment, the first model, the sending of the first model facilitating display by the user equipment of a first depiction of the environment as proposed by the first remodeling proposal; receiving, from the user equipment, feedback information regarding the first remodeling proposal; generating, via the machine learning process, a second model to present by the user equipment, the generating the second model being based upon the decorating style preference, the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), and the feedback information, the second model comprising a second remodeling proposal for the environment; and sending, to the user equipment, the second model, the sending of the second model facilitating display by the user equipment of a second depiction of the environment as proposed by the second remodeling proposal. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, storing, in a database, a decorating style preference of a user; receiving, from user equipment of the user, one or more images (and/or one or more 2D environment models and/or one or more 3D environment models) depicting an environment in which remodeling is desired; generating, via a machine learning process, a first model to present by the user equipment, the generating the first model being based upon the decorating style preference and the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), the first model comprising a first remodeling proposal for the environment; sending, to the user equipment, the first model, the sending of the first model facilitating display by the user equipment of a first depiction of the environment as proposed by the first remodeling proposal; receiving, from the user equipment, feedback information regarding the first remodeling proposal; generating, via the machine learning process, a second model to present by the user equipment, the generating the second model being based upon the decorating style preference, the one or more images (and/or the one or more 2D environment models and/or the one or more 3D environment models), and the feedback information, the second model comprising a second remodeling proposal for the environment; and sending, to the user equipment, the second model, the sending of the second model facilitating display by the user equipment of a second depiction of the environment as proposed by the second remodeling proposal. Other embodiments are disclosed.

The present disclosure provides systems and methods for providing augmented reality experiences. Consistent with disclosed embodiments, one or more machine-learning models can be trained to selectively process image data. A pre-processor can be configured to receive image data provided by a user device and trained to automatically determine whether to select and apply a preprocessing technique to the image data. A classifier can be trained to identify whether the image data received from the pre-processor includes a match to one of a plurality of triggers. A selection engine can be trained to select, based on a matched trigger and in response to the identification of the match, a processing engine. The processing engine can be configured to generate an output using the image data, and store the output or provide the output to the user device or a client system.

Systems, methods, and computer-readable storage media for categorizing items based on attributes of the item and a shape of the item, where the shape of the item is determined from an image of the item. An exemplary system configured as disclosed herein can receive a request to categorize an item, the item having a plurality of attributes, and receive an image of the item. The system can identify, via a processor configured to perform image processing, a shape of the item based on the image, and transform the plurality of attributes and the shape of the item, into a plurality of quantifiable values. The system can then categorize the item based on the quantifiable values.

Embodiments may provide techniques that provide identification of images that can provide reduced resource utilization due to reduced sampling of video frames for visual recognition. For example, in an embodiment, a method of visual recognition processing may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising: coarsely segmenting video frames of video stream into a plurality of clusters based on scenes of the video stream, sampling a plurality of video frames from each cluster; determining a quality of each cluster, re-clustering the video frames of video stream to improve the quality of at least some of the clusters.

A computer identifies entity-containing content. The computer analyzes the entity-containing content for entities. The computer identifies a plurality of hierarchy levels for the entities. The computer receives selections of highlights for the entities, wherein the highlights for the entities within each hierarchy level share one or more characteristics. The computer applies entity contrasting. The computer outputs the entity-containing content with applied entity contrasting to a user.