A machine vision method includes obtaining a first representation of an image captured by an image sensor array, analyzing the first representation for an assessment of whether the first representation is sufficient to support execution of a machine vision task by the processor, if the first representation is not sufficient, determining, based on the first representation, a region of the image of interest for the execution of the machine vision task, reusing the image captured by the image sensor array to obtain a further representation of the image by directing the image sensor array to sample the image captured by the image sensor array in a manner guided by the determined region of the image of interest and by the assessment, and analyzing the further representation to assess whether the further representation is sufficient to support the execution of the machine vision task by implementing a procedure for the execution of the machine vision task in accordance with the further representation.

A machine vision method includes obtaining a first representation of an image captured by an image sensor array, analyzing the first representation for an assessment of whether the first representation is sufficient to support execution of a machine vision task by the processor, if the first representation is not sufficient, determining, based on the first representation, a region of the image of interest for the execution of the machine vision task, reusing the image captured by the image sensor array to obtain a further representation of the image by directing the image sensor array to sample the image captured by the image sensor array in a manner guided by the determined region of the image of interest and by the assessment, and analyzing the further representation to assess whether the further representation is sufficient to support the execution of the machine vision task by implementing a procedure for the execution of the machine vision task in accordance with the further representation.

Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

An image processing apparatus includes an acquiring unit and an assigning unit. The acquiring unit is configured to acquire first image information on a first image and second image information on a second image read by irradiating, with light at respective different wavelengths for read, a printing medium on which at least the first image and the second image to be irradiated with light at different wave lengths to read are mixed. The assigning unit is configured to assign image information on one or more color systems constituting the first image information and image information on one or more color systems constituting the second image information to any of communication paths for supplying image information to a subsequent stage image processing unit configured to perform predetermined image processing on the first image information and the second image information.

An image processing apparatus includes an acquiring unit and an assigning unit. The acquiring unit is configured to acquire first image information on a first image and second image information on a second image read by irradiating, with light at respective different wavelengths for read, a printing medium on which at least the first image and the second image to be irradiated with light at different wave lengths to read are mixed. The assigning unit is configured to assign image information on one or more color systems constituting the first image information and image information on one or more color systems constituting the second image information to any of communication paths for supplying image information to a subsequent stage image processing unit configured to perform predetermined image processing on the first image information and the second image information.

A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A method for optimizing an image classification model can include determining a first image classification model based on initial training data; in response to model optimization, determining a second image classification model based on the first image classification model and a noise data set; and obtaining a third image classification model by optimizing the second image classification model based on the initial training data, the third image classification model being configured to update the noise data set based on noise data generated within a predetermined time period and the noise data set.

A method for optimizing an image classification model can include determining a first image classification model based on initial training data; in response to model optimization, determining a second image classification model based on the first image classification model and a noise data set; and obtaining a third image classification model by optimizing the second image classification model based on the initial training data, the third image classification model being configured to update the noise data set based on noise data generated within a predetermined time period and the noise data set.