Abstract: A system includes a controller configured to receive one or more fixed text prompts and one or more images, wherein the fixed text prompts are associated with the one or more images. The controller is further configured to, in response to utilizing the fixed text prompt and the one or more images at a foundation model associated with a machine-learning network, output an intermediate representation from generating a series of objects and a task, decode the intermediate representation utilizing a decoder associated with the foundation model to generate a matrix associated with the task associated with the fixed text prompt and the image, and in response to identifying a highest probability associated with the matrix utilizing label selection, output a final label associated with a visual based prediction task.

Abstract: Certain embodiments provide a method of generating a color-corrected ophthalmic image. The method comprises obtaining an ophthalmic image of an eye with a nuclear sclerotic cataract or a tinted intraocular lens (“IOL”). The method further comprises determining color-shift information associated with the ophthalmic image based on at least one of user input and by processing the obtained image, the color-shift information indicative of the extent to which color in the obtained image is to be corrected to at least partially remedy the effect of the nuclear sclerotic cataract or the tinted IOL on the color of the obtained image. The method further comprises color-correcting the ophthalmic image based on the color-shift information. The method further comprises generating the color-corrected ophthalmic image based on the color-correcting.

Abstract: A method for determining an intracranial hemodynamic parameter according to embodiments of the present disclosure is provided, which includes determining a three-dimensional a model of a blood vessel based on CT angiographic data, and determining at least one of a boundary condition of each inlet or a boundary condition of each outlet in the three-dimensional vessel model based on CT perfusion imaging data and CT angiographic data.

Abstract: Methods and systems are provided for establishing an activity zone within video captured by a camera. A user device may establish an activity zone or an exclusion zone within video representing a physical view captured by the camera. An automatic monitoring system may analyze the video of the camera with respect to the activity zone or exclusion zone and trigger notifications that are associated with a zone so as to limit notifications transmitted to the user device to those notifications that are relevant to the user. With activity or exclusion zones video from a camera may be automatically monitored to reduce a volume of notifications transmitted to the user device.

Abstract: A method for identifying medical devices, referred to as medical devices of interest, the method including: collecting images of the outer portions of the medical devices of interest, each image being labeled with an identifier of the medical device of interest, the images collected forming a training image database; training an identification model from the training image database in order to obtain an identification model trained to assign an identifier to a medical device of interest to be identified according to an image of the outer portion of the medical device; c. receiving an image of the outer portion of a medical device of interest to be identified; assigning, by the trained classification model, of an identifier to the medical device of interest captured on the received image; and characterizing the medical device of interest according to the assigned identifier.

Abstract: An object of the present invention is to generate a highly accurate trail by correcting detection box information of an object in an object tracking apparatus that generates a trail of an object within a measurement range.

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.