Technologies are shown for near-duplicate detection where a message is received and a fingerprint generated for some or all of its content. A distance metric is determined between the received message fingerprint and fingerprints for a cluster of other messages. If the message fingerprint matches a fingerprint in a cluster, then the received message is added to the matching cluster. A risk value associated with the matching cluster can be determined. If the risk value is greater than a risk threshold, the received message fingerprint can be added to a risk list or an alert, notification or block indication can be generated. A fingerprint can be determined for an inquiry message and, if the inquiry message fingerprint matches a fingerprint in the risk list, then an alert can be generated. The distance metric between fingerprints correlates to a similarity between the message content corresponding to the fingerprints.

The exemplary embodiments disclose a system and method, a computer program product, and a computer system for generating an agriculture map of a region of land. The exemplary embodiments may include collecting agricultural data of one or more sub-regions of the region of land, wherein the agricultural data includes classified data and unclassified data, extracting one or more features from the collected classified agricultural data, training one or more models based on the extracted one or more features, and generating an agricultural map of the region of land based on applying the one or more models to the collected unclassified agricultural data.

Aspects of the present disclosure involve systems and methods for an automated machine learning partitioning of a digital image file into multiple documents. The machine learning system may obtain or receive a digital image file that includes multiple documents merged into the single image file. To determine the different documents included in the image file, the machine learning model may analyze the content of the pages of the image file to determine particular content that may indicate the start and/or end of documents within the image file and partition the image file into multiple documents based on the determined start and/or end of the documents. In one instance, the machine learning partitioning system may generate an analysis window that comprises two pages of the corpus of pages and compare features or content of the two pages or determine if either of the two pages includes one or more features.

Provided are an image recognition apparatus, an image recognition method, and a program for enabling recognition of many kinds of objects with high precision. An overall recognition unit executes, for at least one given object, a process of recognizing the position of the object in an image. A partial image extraction unit extracts, from the image, a partial image which is a part of the image associated with the recognized position. A partial recognition unit executes a process of recognizing what is one or more objects represented by the partial image, the one or more objects including an object other than the given object the position of which is recognized.

A computer-implemented method of performing a convolution between an input data array and a kernel to generate an output data array includes decomposing the kernel into a plurality of sub-kernels each having a respective position relative to the kernel and respective in-plane dimensions less than or equal to a target kernel dimension, and for each of the plurality sub-kernels: determining a respective portion of the input data array on the basis of the respective in-plane dimensions of the sub-kernel and the respective position of the sub-kernel relative to the kernel; retrieving the respective portion of the input data array; and performing a convolution between the retrieved respective portion of the input data array and the sub-kernel to generate a respective intermediate data array. The method further includes summing the generated intermediate data arrays to generate at least a portion of the output data array.

A system for detecting boundaries of lanes on a road is presented. The system includes an imaging system configured to produce a set of pixels associated with lane markings on a road. The system also includes one or more processors configured to detect boundaries of lanes on the road, including: receive, from the imaging system, the set of pixels associated with lane markings; partition the set of pixels into a plurality of groups, each of the plurality of groups associated with one or more control points; and generate a first spline that traverses the control points of the plurality of groups, the first spline describing a boundary of a lane on the road.

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving, by a processing device, image data including one or more image frames indicative of a current state of a drive-thru area. The processing device determines a vehicle disposed within the drive-thru area based on the image data. The processing device receives order data with a pending meal order. The processing device determines a first association between the vehicle and the pending meal order based on the image data. The processing devices determine a meal delivery procedure associated with the based on the association between the vehicle and the pending meal order. The processing device performs may perform the meal delivery procedure. The processing device may provide the meal delivery procedure for display on a graphical user interface (GUI).

A method for the clustering and identification of animals in acquired images based on physical traits is provided, where a trait feature is a scalar or vector quantity that is a property of a trait and trait distance is a measure of discrepancy between the same trait features of two animals, and also introduced are several different ways of implementing clustering using trait features.

Various examples of the disclosure pertain to determining a label set for an anatomical structure such as a complex blood vessel, e.g., the coronary artery. The determining of the label set takes into account multiple inputs, such as the rule set of anatomical relationship between sub structures of the anatomical structure and a list of candidate labels and associated probabilities obtained for each one of the anatomical substructures.

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method includes a processing device receiving image data having one or more image frames indicative of a state of a meal preparation area. The method further includes the processing device determining, based on the image data, one of a meal preparation item or a meal preparation action associated with the state of the meal preparation area. The method further includes the processing device determining, based on the image data, a performance metric associated with the meal preparation item or the meal preparation item or the meal preparation action. The method further includes the processing device causing a notification corresponding to the performance metric to be displayed on a graphical user interface (GUI).