Abstract: Embodiments herein use constraints from a target process (e.g., testing a semiconductor wafer or designing a new integrated circuit) and historical real-world samples (e.g., probe test sample, fault detection, or measured signals) to generate synthesized samples using AI synthesis embedded in a digital twin. These test samples can be combined with real-world test samples and then evaluated to determine next actions. In another example, a digital twin can use an new IC design and a design from an older, but related, IC to synthesize new IC design samples.

Abstract: In some aspects, a method for object re-identification may include obtaining a first set of images from a first camera, and a second set of images from at least one second camera; determining a first set of features based on the first set of images, the first set of features lying in a first feature space; and determining a second set of features based on the second set of images, the second set of features lying in a second feature space. The method may additionally include determining a first feature projection matrix and a second feature projection matrix that respectively map the first set of features and the second set of features to a shared feature space; and determining a common dictionary based on the shared feature space.

Abstract: Methods and systems for crowd level estimation are provided. The system for crowd level estimation includes an input module and a crowd level estimation module. The input module receives an input image of a crowd and the crowd level estimation module estimates a crowd level of the crowd in the input image in response to a crowd density level.

Abstract: Methods and systems for crowd level estimation are provided. The system for crowd level estimation includes an input module and a crowd level estimation module. The input module receives an input image of a crowd and the crowd level estimation module estimates a crowd level of the crowd in the input image in response to a crowd density level.

Abstract: Methods and systems for crowd level estimation are provided. The system includes a plurality of performance modeling modules (206), an input module (202) and a crowd estimation technique integration module. The plurality of performance modeling modules (206) performance model each of a plurality of crowd estimation techniques based on an accuracy thereof at different crowd levels and/or at different locations. The input module (202) receives an image of a crowd. The crowd estimation technique integration module (208) selects one or more of the plurality of crowd estimation techniques in response to the performance modeling of the one or more of the plurality of crowd estimation techniques and an estimated crowd level and/or an estimated location. The crowd estimation technique integration module (208) then estimates a crowd count of the crowd in the received image in accordance with the selected one or more of the plurality of crowd estimation techniques.

Abstract: Methods and systems for crowd level estimation are provided. The system for crowd level estimation includes an input module and a crowd level estimation module. The input module receives an input image of a crowd and the crowd level estimation module estimates a crowd level of the crowd in the input image in response to a crowd density level.

Abstract: In some aspects, a method for object re-identification may include obtaining a first set of images from a first camera, and a second set of images from at least one second camera; determining a first set of features based on the first set of images, the first set of features lying in a first feature space; and determining a second set of features based on the second set of images, the second set of features lying in a second feature space. The method may additionally include determining a first feature projection matrix and a second feature projection matrix that respectively map the first set of features and the second set of features to a shared feature space; and determining a common dictionary based on the shared feature space.

Abstract: The present disclosure relates to methods and systems for identifying an individual in a crowd. The method comprises the steps of capturing a plurality of crowd-viewing images using multiple image-capturing devices. For each image, a reference point within a facial contour of a person in the image is selected. The reference point is selected based on a facial feature on the facial contour of the person. The method further comprises the step of processing the image to obtain one or more angular measurements associated with a posture displayed by the person in the image. Based on the obtained angular measurements, location data of a predetermined body point of the person relative to the reference point is extracted.

Abstract: An automatic method improves calibration of the camera by detecting key body points on people in images (100); extracting, from the key body points, orthogonal lines that extend from a head to feet of the people (110); selecting, from the orthogonal lines, head-toe lines of the people who are standing upright in the images (120); and calibrating the camera from the head-toe lines of the people who are standing upright in the images (130).

Abstract: This document describes a system and method for identifying persons-of-interest based on images and/or information captured by image capturing devices and information provided by third party sources/databases. In particular, this document describes a system that comprises at least one set of image capturing devices that are provided at a particular location-of-interest, a remote database that is configured to receive the captured images and its associated information from the set of image capturing devices, a server and a third party database whereby the remote database, the server and the third party database are all communicatively connected together. The server is then configured to utilize information from the remote database and the third party database to identify persons-of-interest from the images of individuals that were captured by the set of image capturing devices.

Abstract: The present disclosure relates to methods and systems for identifying an individual in a crowd. The method comprises the steps of capturing a plurality of crowd-viewing images using multiple image-capturing devices. For each image, a reference point within a facial contour of a person in the image is selected. The reference point is selected based on a facial feature on the facial contour of the person. The method further comprises the step of processing the image to obtain one or more angular measurements associated with a posture displayed by the person in the image. Based on the obtained angular measurements, location data of a predetermined body point of the person relative to the reference point is extracted.

Abstract: Methods and systems for crowd level estimation are provided. The system for crowd level estimation includes an input module and a crowd level estimation module. The input module receives an input image of a crowd and the crowd level estimation module estimates a crowd level of the crowd in the input image in response to a crowd density level.

Abstract: Methods and systems for performance modeling of crowd estimation techniques are provided. The system for modeling of crowd estimation techniques includes one or more crowd estimation modules for estimating a crowd count of a crowd in an image and a performance modeling module for each of the one or more crowd estimation modules.

Abstract: Methods and systems for crowd level estimation are provided. The system includes a plurality of performance modeling modules (206), an input module (202) and a crowd estimation technique integration module. The plurality of performance modeling modules (206) performance model each of a plurality of crowd estimation techniques based on an accuracy thereof at different crowd levels and/or at different locations. The input module (202) receives an image of a crowd. The crowd estimation technique integration module (208) selects one or more of the plurality of crowd estimation techniques in response to the performance modeling of the one or more of the plurality of crowd estimation techniques and an estimated crowd level and/or an estimated location. The crowd estimation technique integration module (208) then estimates a crowd count of the crowd in the received image in accordance with the selected one or more of the plurality of crowd estimation techniques.