Techniques are disclosed for using and training a descriptor network. An image may be received and provided to the descriptor network. The descriptor network may generate an image descriptor based on the image. The image descriptor may include a set of elements distributed between a major vector comprising a first subset of the set of elements and a minor vector comprising a second subset of the set of elements. The second subset of the set of elements may include more elements than the first subset of the set of elements. A hierarchical normalization may be imposed onto the image descriptor by normalizing the major vector to a major normalization amount and normalizing the minor vector to a minor normalization amount. The minor normalization amount may be less than the major normalization amount.

A processor-implemented method with data recognition includes: extracting input feature data from input data; calculating a matching score between the extracted input feature data and enrolled feature data of an enrolled user, based on the extracted input feature data, common component data of a plurality of enrolled feature data corresponding to the enrolled user, and distribution component data of the plurality of enrolled feature data corresponding to the enrolled user; and recognizing the input data based on the matching score.

Poses or gestures of actors within a scene may be detected and tracked using multiple imaging devices aligned with fields of view that overlap at least in part. Images captured by the imaging devices may be synchronized and provided to a classifier to recognize body parts within the images, and score maps indicative of locations of peak probabilities that the images include the respective body parts may be generated. Locations of peak values within the score maps may be correlated with one another to confirm that a given body part is depicted in two or more fields of view, and vectors indicative of distances to or ranges of motion of body parts, with respect to the given body part, may be generated. Motion of the body parts may be tracked in subsequent images, and a virtual model of the body parts may be generated and updated based on the motion.

A learning apparatus for image generation includes a preprocessing module configured to receive text for image generation and generate a sentence feature vector and a word feature vector from the received text, a first generative adversarial network (GAN) configured to receive the sentence feature vector from the preprocessing module and generate an initial image based on the received sentence feature vector, and a second generative adversarial network configured to receive the word feature vector generated by the preprocessing module and the initial image generated by the first generative adversarial network and generate a final image based on the word feature vector and the initial image.

One or more binary turning function signatures for each of the layout patterns are determined. The one or more binary turning function signatures comprise binary turning function signatures for polygons in each of the layout patterns, and may further comprise binary turning function signatures for secondary polygons A binary turning function signature of a polygon is derived based on deriving a minimum binary number or a maximum binary number among variants of a binary turning function sequence number for the polygon. The variants are generated by circular bit shifting and bit sequence reversing. Similar layout patterns in the layout patterns are determined based on the one or more binary turning function signatures.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

A system and methodologies for neuromorphic (NM) vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

In implementations of vector-based glyph style transfer, a style transfer system transfers a modification of a modified glyph to an additional glyph. The system identifies the modification by comparing the modified glyph to a corresponding unmodified glyph. In one or more implementations, this includes identifying similar segments of the additional glyph based on a style transfer policy, which defines conditions for transferring the modification to the additional glyph. The system transfers the modification to the additional glyph by mapping the modification to the similar segments.

Among other things, one or more travel signals are identified by analyzing one or more images and data from sensors, classifying candidate travel signals into zero, one or more true and relevant travel signals, and estimating a signal state of the classified travel signals.

The present invention relates to a method for encoding the borders of pixel regions of an image, wherein the borders contain a sequence of vertices subdividing the image into regions of pixels (superpixels), by generating a sequence of symbols from an alphabet including the step of: defining for each superpixel a first vertex for coding the borders of the superpixel according to a criterion common to all superpixels; defining for each superpixel the same coding order of the border vertices, either clockwise or counter-clockwise; defining the order for coding the superpixels on the base of a common rule depending on the relative positions of the first vertices; defining a set of vertices as a known border, wherein the following steps are performed for selecting a symbol of the alphabet, for encoding the borders of the superpixels: a) determining the first vertex of the next superpixel border individuated by the common criterion; b) determining the next vertex to be encoded on the basis of the coding direction; c) selecting a first symbol (0) for encoding the next vertex if the next vertex of a border pertains to the known border, d) selecting a symbol (1; 2) different from the first symbol (0) if the next vertex is not in the known border; e) repeating steps b), c), d) and e) until all vertices of the superpixel border have been encoded; f) adding each vertex of the superpixel border that was not in the known border to the set; g) determining the next superpixel whose border is to be encoded according to the common rule, if any; i) repeating steps a)-g) until the borders of all the superpixels of the image have being added to the known border.