A computer-implemented method of generating metadata from an image may comprise sending the image to an object detection service, which generates detections metadata from the image. The image may also be sent to a visual features extractor, which extracts visual features metadata from the image. The generated detections metadata may then be sent to an uncertainty score calculator, which computes an uncertainty score from the detections metadata. The uncertainty score may be related to a level of uncertainty within the detections metadata. The image, the visual features metadata, the detections metadata and the uncertainty score may then be stored in a database accessible over a computer network.

A system for modular image mining and search, comprising a plurality of image capture devices, a search engine, an indexing engine, a database, and user interface software. A plurality of image capture devices capture images and store them to a database. The indexing engine indexes images to create image data for each image with associated metadata and stores the information to the database. The interface software is used to request search queries via a search engine. The search engine accesses the image data to find images or parts of images that satisfy the search query.

A circular dome for photographing vehicles includes curved frames that support a skin. Contoured walls offset from the curved frames to define a recess adapted to receive cameras and lights for photographing vehicles around a perimeter of the dome. A door matching the contour of the curved frames completes the dome and is size to receive a vehicle, to collectively implement subtractive lighting along the perimeter of the dome. A system and method are provided for automatically photographing vehicles in an enclosable circular domed structure where an automated process captures a series of vehicle images and uploads the captured images to a web template for display and recordation.

An image processing device acquires an image to be processed that includes an image of clothing, the image being composed of a hue, saturation and brightness; calculates a difference between the image to be processed and a plurality of shift images obtained by shifting the image to be processed in units of one pixel by at least one pixel or more in a prescribed direction, and generates a plurality of difference images that correspond to the number of shifted pixels; generates a histogram of differential pixel values for each of the hue, saturation and brightness of the difference images; determines the fabric of the clothing on the basis of the features of the histogram.

An ultrasound diagnosis apparatus includes: a controller configured to recognize an object included in an ultrasound image and search at least one piece of texture information corresponding to the recognized object; a display configured to display the ultrasound image and the searched at least one piece of texture information; a user interface configured to receive an input for selecting one piece of texture information from among the searched at least one piece of texture information; and an image processor configured to perform texture mapping of the selected piece of texture information onto at least one region in the ultrasound image of the object.

A method, computer readable medium, and system are disclosed for performing a texture level-of-detail approximation. The method includes the steps of identifying a scene to be rendered, projecting a ray passing through a pixel of a screen space, resulting in a first hit point at a geometry element within the scene, determining a footprint angle of the pixel, determining a curvature measure for the geometry element at the first hit point within the scene, computing a texture level of detail (LOD) approximation for a component of the scene, utilizing the footprint angle of the pixel and the curvature measure for the geometry element, and performing, utilizing a hardware processor, one or more rendering operations for the scene, utilizing the texture LOD approximation.

A computer-implemented method of generating metadata from an image may comprise sending the image to an object detection service, which generates detections metadata from the image. The image may also be sent to a visual features extractor, which extracts visual features metadata from the image. The generated detections metadata may then be sent to an uncertainty score calculator, which computes an uncertainty score from the detections metadata. The uncertainty score may be related to a level of uncertainty within the detections metadata. The image, the visual features metadata, the detections metadata and the uncertainty score may then be stored in a database accessible over a computer network.

A method and non-transitory computer readable medium for content based image retrieval. The method includes selecting a query image, segmenting the selected query image by applying a segmentation technique, extracting features from the segmented query image by determining at least two feature descriptors, including color feature descriptors and texture feature descriptors, and determining a similarity of the query image to a plurality of images included in a database using the determined at least two feature descriptors of the segmented query image, features being extracted from each of the plurality of images included in the database by determining the at least two feature descriptors, the color feature descriptors and the texture feature descriptors including a simultaneous combination of different color spaces, and global and local statistical measurements being carried out on the simultaneous combination of the different color spaces.

Embodiments of the present disclosure provide systems, methods, and computer storage media for mitigating delays typically experienced when training codebooks during the encoding process. Instead of training a codebook based on a single digital asset, multiple digital assets determined to have asset characteristics in common can be grouped together to form a group of digital assets, from which a single codebook can be trained. The group of digital assets together form a codebook training set, such that each digital asset therein can be analyzed, in parallel, to expeditiously train a single codebook. A codebook trained in this manner can be employed to encode other digital assets sharing the asset characteristics as those in the codebook training set.

Embodiments of the present disclosure provide systems, methods, and computer storage media for optimizing computing resources generally associated with cloud-based media services. Instead of decoding digital assets on-premises to stream to a remote client device, an encoded asset can be streamed to the remote client device. A codebook employable for decoding the encoded asset can be embedded into the stream transmitted to the remote client device, so that the remote client device can extract the embedded codebook, and employ the extracted codebook to decode the encoded asset locally. In this way, not only are processing resources associated with on-premises decoding eliminated, but on-premises storage of codebooks can be significantly reduced, while expensive bandwidth is freed up by virtue of transmitting a smaller quantity of data from the cloud to the remote client device.