Abstract: Systems and methods are provided for deterministically estimating whether the location of a computing device that is fixed or mobile is inside a building or not using location data. Particularly, the system may compare 3D geoposition uncertainty region of a location to a 3D volumetric structure shape of the location, and determining a confidence value based on the comparison associated with a likelihood that the location associated with the computing device is indoors or outdoors. Various states of the computing device are supported by the substance of the disclosure, including indoor and outdoor states.

Abstract: A method for measuring a feature near the edge of an object comprising receiving image data characterizing the object, identifying an edge of the object, identifying a point on the perimeter of the feature at a position opposite the edge of the object, determining a reference plane, determining a three-dimensional reference line on the reference plane associated with the edge of the object, determining a three-dimensional measurement point on the reference plane based on the point on the perimeter of the feature, and determining the perpendicular distance from the measurement point to the reference line.

Abstract: A method includes providing a sample holder having a plasmonic layer and applying the sample to the sample holder. The sample is illuminated and an image formed. The method enables identifying a structure in the sample from the image based at least partly on its colour. The colour can encode a structural property of the sample, preferably without staining. The method can be used to differentiate a state of at least one cell in a sample. Application to identification of cancer and non-cancer abnormalities are disclosed.

Abstract: The present disclosure relates to a method for object identification based on a scalar score corresponding to a set of local microstructural features of an object. The plurality of digital images of one or more surfaces corresponding to the object is captured. The set of local microstructural features is extracted from the plurality of digital images. The plurality of objects with a set of local microstructural features is stored in a database. Local microstructural features of a query object are compared with the set of local microstructural features of the plurality of objects stored in the database using a voting function. A score is generated for individual object based on the comparison. A potential match set of the plurality of objects is selected based on the score and a status of the query object is identified based on the potential match set.

Abstract: A method for evaluating data to be used to train an object recognition model is to be implemented by a computing device. The computing device stores a plurality of training datasets respectively related to a plurality of images, and each training dataset includes a plurality of entries of training data. The method includes steps of: obtaining, for each image, at least one target area and at least one target property that are related to the image based on the entries of training data; creating, for each image, a training material that includes the image, and the at least one target area and the at least one target property both related to the image; and obtaining at least three object recognition models based on the training materials that are created respectively for the images using at least one machine learning algorithm.

Abstract: Herein is machine learning for anomalous graph detection based on graph embedding, shuffling, comparison, and unsupervised training techniques that can characterize an unfamiliar graph. In an embodiment, a computer obtains many known vectors that respectively represent known graphs. A new vector is generated that represents a new graph that contains multiple vertices. The new vector may contain an arithmetic aggregation of vertex vectors that respectively represent multiple vertices and/or a vector that represents a virtual vertex that is connected to the multiple vertices by respective virtual edges. In the many known vectors, some similar vectors that are similar to the new vector are identified. The new graph is automatically characterized based on a subset of the known graphs that the similar vectors represent.

Abstract: A method of training a semiconductor process image generator includes training the semiconductor process image generator with a plurality of mask images including a first group and a second group, training the semiconductor process image generator with the second group and a first transformed group obtained by applying a transformation to the first group, and training the semiconductor process image generator with the first group and a second transformed group obtained by applying a transformation to the second group.

Abstract: The invention relates to a method for real-time multi-camera tracking of a target object comprising, for at least one new tracklet captured by at least one camera, calculating at least a first distance between the new tracklet and at least one reference tracklet belonging to the target object. The method also includes, when the smallest calculated first distance is between a first predetermined threshold value, called re-identification threshold, and a second predetermined threshold value, called human threshold, presenting the new tracklet to an operator, for the operator to assign or not assign the new tracklet to the target object, manually. The human threshold is a function of the number of reference tracklets in the reference list. The invention also relates to a computer program, to an object re-identification device and to an object re-identification system implementing such a method.

Abstract: An emotion recognition method includes the following steps: acquiring video data of a human-object interaction behavior process; performing data labeling on the positions of a person and an object and the interaction behaviors and emotions expressed by the person; constructing a feature extraction model based on deep learning, extracting features of interaction between the person and the object in a time-space dimension, and detecting the position and category of the human-object interaction behavior; mapping the detected interaction behavior category into a vector form through a word vector model; and finally, constructing a fusion model based on deep learning, fusing the interaction behavior vector and the time-space interaction behavior features, and identifying the emotion expressed by the interaction person.

Abstract: Methods and systems include receiving, at de-ringing circuitry of a display pipeline of an electronic device, scaled image content based on image data. The de-ringing circuitry also receives a fallback scaler output. The de-ringing circuitry determines whether the image data has a change frequency greater than a threshold. In response to the change frequency being above the threshold, the de-ringing circuitry determines a weight. Based at least in part on the weight, the de-ringing circuitry blends the scaled image content and the fallback scaler output based at least in part on the weight.

Abstract: A system includes a processing unit communicatively coupled to a detector array of an optical wafer characterization system. The processing unit is configured to perform one or more steps of a method or process including the steps of acquiring one or more target images of a target location on a wafer from the detector array, applying a de-noising filter to at least the one or more target images, determining one or more difference images from one or more reference images and the one or more target images, and up-sampling the one or more difference images to generate one or more up-sampled images. One or more wafer defects are detectable in the one or more difference images or the up-sampled images.

Abstract: There is provided a multi-scale object detection device. The device includes an image frame acquisition unit for acquiring a plurality of consecutive image frames, a critical region extractor for extracting at least one second critical region from a current image frame based on at least one first critical region extracted from a previous image frame among the consecutive image frames, a multi-scale object detector whose operation involves a first object detection process for the current image frame and a second object detection process for the at least one second critical region, and an object detection integration unit for integrating the results of the first and second object detection processes.

Abstract: Processes, algorithms and techniques are disclosed for detecting the presence of a source of at least one of heat, mass, momentum, energy, electrical field, and species in a system, such as, for identifying the presence of a heat source and its 3D location within a body part, and also to identify absence of a heat source within the body part. Multi-view thermal images are obtained of the body part and thermal and spatial information in different views are obtained. Iterative algorithms are developed to determine the presence of a heat source in the body part by employing the Physics-Informed Neural Network analyzer. Methods of generating a spatial-point-cloud and thermal-spatial-point-cloud are disclosed.

Abstract: A technique is described herein. The technique includes receiving endpoint location data in a geometry shader; processing the endpoint location data to calculate Bezier curve control points; and based on the Bezier curve control points, determining estimated electrode positions for the catheter.

Abstract: Methods and systems for detecting defects on a specimen are provided. One system performs double detection in which at least one of the reference images compared to a test image is a computed reference image generated from multiple images corresponding to the test image. The other reference image may or may not be computed from more than one of the multiple images. Such a computed reference image may also be a median-based computed reference generated from multiple-median images generated from different subsets of images in a job of images generated by an inspection subsystem for a specimen. Such a system may also group images for a die row on a specimen into different jobs based on color so that different jobs have different color value ranges. Such grouping may also be performed so that each of the jobs includes a number of images greater than a predetermined, minimum job size.

Abstract: An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.

Abstract: A filling rate measurement method includes: obtaining a space three-dimensional model generated by measuring a first storage having an opening and a first storage space in which a measurement target is to be stored, the measuring being performed using a range sensor facing the first storage; obtaining a storage three-dimensional model that is a three-dimensional model of the first storage in which the measurement target is not stored; extracting a target portion corresponding to the measurement target from the space three-dimensional model using the space three-dimensional model and the storage three-dimensional model; calculating a first three-dimensional coordinate system; estimating a target three-dimensional model using the target portion and the first three-dimensional coordinate system, the target three-dimensional model being a three-dimensional model of the measurement target in the first storage space; and calculating a first filling rate of the measurement target with respect to the first storage sp

Abstract: Provided are cell image analysis devices and sample analysis methods. A sample smear of a test sample is imaged by an imaging device in an assigned analysis mode to obtain first cell images of the test sample, which are identified and analyzed by a control device. If it is identified that there is preset abnormality in the sample smear, an analysis mode different from the assigned analysis mode and corresponding to the present abnormality is determined as an additional analysis mode, and the imaging device is controlled to image the sample smear in the additional analysis mode. The additional analysis mode matches with the preset abnormality, so that the imaging device is allowed to obtain cell images in the additional analysis mode, to identify and analyze the cell images matching the preset abnormality, thereby increasing processing efficiency and accuracy of processing result.

Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.

Abstract: Systems and methods for associating three-dimensional bounding boxes with tracked objects are disclosed based on information gathered from objects in a two-dimensional image, for applications such as autonomous navigation. The systems and methods track an object in three-dimensional space and receive a two-dimensional image from a vehicle sensor. The system generates a three-dimensional bounding box for an object in the two-dimensional image, determines a two-dimensional image characteristic of the object, and associates the three-dimensional bounding box with the tracked object based on the three-dimensional bounding box of the object and the two-dimensional image characteristic of the object.