A method for matching a sensed signal to a concept structure, the method may include: receiving a sensed signal; generating a signature of the input image; comparing the signature of the input image to signatures of a concept structure; determining whether the signature of the input image matches any of the signatures of the concept structure based on signature matching criteria, wherein each signature of the concept structure is associated within a signature matching criterion that is determined based on an object detection parameter of the signature; and concluding that the input image comprises an object associated with the concept structure based on an outcome of the determining.

Lossy or lossless compression and transmission, comprising the steps of: (i) receiving an input image; (ii) encoding it using an encoder trained neural network, to produce a y latent representation; (iii) encoding the y latent representation using a hyperencoder trained neural network, to produce a z hyperlatent representation; (iv) quantizing the z hyperlatent representation using a predetermined entropy parameter to produce a quantized z hyperlatent representation; (v) entropy encoding the quantized z hyperlatent representation into a first bitstream, using predetermined entropy parameters; (vi) processing the quantized z hyperlatent representation using a hyperdecoder trained neural network to obtain a location entropy parameter μ.sub.y, an entropy scale parameter σ.sub.y, and a context matrix A.sub.y of the y latent representation; (vii) processing the y latent representation, the location entropy parameter μ.sub.y and the context matrix A.sub.y, to obtain quantized latent residuals; (viii) entropy encoding the quantized latent residuals into a second bitstream, using the entropy scale parameter σ.sub.y; and (ix) transmitting the bitstreams.

A method for grasping texture-less metal parts based on BOLD image matching comprises: obtaining a real image and CAD template images by photographing, extracting a foreground part of the input part image, calculating a covariance matrix of a foreground image, establishing the direction of a temporary coordinate system, and setting directions of line segments to point to a first or second quadrant of the temporary coordinate system; constructing a descriptor of each line segment according to an angle relation between the line segment and k nearest line segments, and matching the descriptors of different line segments in the real image and the CAD template images to obtain line segment pairs; and recognizing a processed pose through a PNL algorithm to obtain a pose of a real texture-less metal part, and then inputting the pose of the real texture-less metal part to a mechanical arm to grasp the part. The present invention can correctly match line segments, obtain an accurate pose of the part by calculation, successfully grasp the part, and satisfy actual application requirements.

A learning device allocates which feature value of a sub-object is extracted by a module from a group of sub-objects constituting an object of an image to each of modules that extract feature values of the object in the image in a deep neural network that is a learning target. After that, the learning device performs first learning to perform learning of the respective modules so that the respective modules are capable of precisely picking up regions of sub-objects allocated to the modules using information indicating the regions of the sub-objects in an image for each of images and second learning to perform learning of the respective modules so that analysis precision of an image analysis is further improved using a result of the image analysis based on the feature values of the sub-objects picked up by the respective modules.

An information processing device (30) includes: an estimation unit (325) that estimates an operable part among a plurality of parts of a first object obtained by capturing a real item into a virtual space; and a display control unit (326) that controls a display device (20) to display a motion of the part operated by a second object indicating a virtual item on the basis of operation information of the second object with respect to the first object and a first piece of information indicating a result of the estimation unit (325).

The disclosure relates to system and method for image quality detection. The method may include: obtaining an image acquired by a camera; obtaining an image quality detection model, the image quality detection model being provided by training a machine learning model using a plurality of training samples; determining a detection result of the image using the image quality detection model; and in response to a determination that the detection result includes a quality anomaly of the image, generating a strategy in response to the quality anomaly.

An imaging device includes a subject detection unit that detects a subject with respect to an input image. The subject detection unit detects at least two parts within the image and determines a priority part among the detected parts. The subject detection unit performs a region extension process of extending the determined priority part to thereby extend a region from the priority part in a direction of another detected part. In detection of a plurality of parts, a part that cannot be detected is also handled as an extended region of a detection part. Thereby, if a user designates a region erroneously, the imaging device can determine that the user has designated the region when the designated region is included in the extended region.

In various examples, an ego-machine may analyze sensor data to identify and track features in the sensor data using. Geometry of the tracked features may be used to analyze motion flow to determine whether the motion flow violates one or more geometrical constraints. As such, tracked features may be identified as dynamic features when the motion flow corresponding to the tracked features violates the one or more static constraints for static features. Tracked features that are determined to be dynamic features may be clustered together according to their location and feature track. Once features have been clustered together, the system may calculate a detection bounding shape for the clustered features. The bounding shape information may then be used by the ego-machine for path planning, control decisions, obstacle avoidance, and/or other operations.

In various examples, an ego-machine may analyze sensor data to identify and track features in the sensor data using. Geometry of the tracked features may be used to analyze motion flow to determine whether the motion flow violates one or more geometrical constraints. As such, tracked features may be identified as dynamic features when the motion flow corresponding to the tracked features violates the one or more static constraints for static features. Tracked features that are determined to be dynamic features may be clustered together according to their location and feature track. Once features have been clustered together, the system may calculate a detection bounding shape for the clustered features. The bounding shape information may then be used by the ego-machine for path planning, control decisions, obstacle avoidance, and/or other operations.

One embodiment provides a method, including: receiving, at an information handling device, drawing input; identifying, using a processor, at least one object in the drawing input; determining, based on the identifying, whether a factual anomaly exists in the drawing input with respect to the at least one object; and notifying, responsive to determining that a factual anomaly exists, a user of the factual anomaly.