An apparatus for matching point cloud data according to an embodiment includes a generation unit configured to generate an input model and an input model group including a plurality of the input models, the input model being obtained by reducing a data amount of the point cloud data, an extraction unit configured to extract some of the input models from the input model group according to shape information of the input models, a calculation unit configured to compare an extracted extraction model with a reference model based on reference point cloud data and calculate a cost, a determination unit configured to determine whether or not the cost has converged, and a change unit configured to change a position and a posture so that the cost is reduced when the cost has not converged.

An apparatus for matching point cloud data according to an embodiment includes a generation unit configured to generate an input model and an input model group including a plurality of the input models, the input model being obtained by reducing a data amount of the point cloud data, an extraction unit configured to extract some of the input models from the input model group according to shape information of the input models, a calculation unit configured to compare an extracted extraction model with a reference model based on reference point cloud data and calculate a cost, a determination unit configured to determine whether or not the cost has converged, and a change unit configured to change a position and a posture so that the cost is reduced when the cost has not converged.

To improve the accuracy and efficiency of object detection through computer digital image analysis, the detection of some objects can inform the sub-portion of the digital image to which subsequent computer digital image analysis is directed to detect other objects. In such a manner object detection can be made more efficient by limiting the image area of a digital image that is analyzed. Such efficiencies can represent both computational efficiencies and communicational efficiencies arising due to the smaller quantity of digital image data that is analyzed. Additionally, the detection of some objects can render the detection of other objects more accurate by adjusting confidence thresholds based on the detection of those related objects. Relationships between objects can be utilized to inform both the image area on which subsequent object detection is performed and the confidence level of such subsequent object detection.

A multi-view interactive digital media representation (MVIDMR) of an object can be generated from live images of an object captured from a camera. Selectable tags can be placed at locations on the object in the MVIDMR. When the selectable tags are selected, media content can be output which shows details of the object at location where the selectable tag is placed. A machine learning algorithm can be used to automatically recognize landmarks on the object in the frames of the MVIDMR and a structure from motion calculation can be used to determine 3-D positions associated with the landmarks. A 3-D skeleton associated with the object can be assembled from the 3-D positions and projected into the frames associated with the MVIDMR. The 3-D skeleton can be used to determine the selectable tag locations in the frames of the MVIDMR of the object.

The present disclosure generally relates to obtaining details regarding an image based on search intent and, further, to determining royalty distributions and credits for projects. In an example, a computer-implemented method includes: searching, via a computing device, at least one piece of information associated with a song that is currently playing on the computing device; and displaying, via the computing device, the at least one piece of information on a display of the computing device.

The present disclosure generally relates to obtaining details regarding an image based on search intent and, further, to determining royalty distributions and credits for projects. In an example, a computer-implemented method includes: searching, via a computing device, at least one piece of information associated with a song that is currently playing on the computing device; and displaying, via the computing device, the at least one piece of information on a display of the computing device.

According to one embodiment, a data processing device includes an acquisition part, and a processor. The acquisition part is configured to acquire first data including time-series image data. The processor is configured to derive first feature information based on a multidimensional array of n dimensions based on the first data acquired by the acquisition part. n is an integer not less than 3. A first axis of the multidimensional array is related to time.

Images of an insect are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect. A computer calculates an outer product of the first feature map and the second feature map to form an integrated feature map. Extracting fully connected layers from respective sets of integrated feature maps and applying the fully connected layers to a classification network for identifying the genus and the species of the insect.

A system for improving hardware usage in a control server using artificial intelligence image processing, including: an image input unit receiving multiple images, temporarily storing input original images, and transmitting image data into one hardware component; an image data pre-processing unit converting multiple images into images with smaller resolution to process the multiple images according to the image resolution of a single image and concatenating the converted images into a single image; an artificial intelligence task performance unit retrieving a data learning model for an object to be recognized and performing an object recognition artificial intelligence task using the data for which pre-processing of image data has been applied; and a resultant image output unit checking boundary coordinates along which images are concatenated according to the number of input images and calculating the center coordinates of each recognized object in the vicinity of the image boundaries.

Aspects of the present disclosure relate to machine learning techniques for identifying collections of items, such as furniture items, that are visually complementary. These techniques can rely on computer vision and item imagery. For example, a first portion of a machine learning system can be trained to extract aesthetic item qualities or attributes from pixel values of images of the items. A second portion of the machine learning system can learn correlations between these extracted aesthetic qualities and the level of visual coordination between items. Thus, the disclosed techniques use computer vision machine learning to programmatically determine whether items visually coordinate with one another based on pixel values of images of those items.