Abstract: Systems and methods for detecting image anomalies include extracting one or more detected images from a submission file received from at least one computing device and generating an image identification (ID) for each of the one or more images. One or more image quality indices are determined for the submission file based on at least one of predetermined image features, an image type of the one or more images, and submission file attributes, and one or more image anomalies associated with the one or more images of the submission file are detected based on at least one of the image ID and the one or more image quality indices.

Abstract: A computer-implemented method of prioritizing agricultural field management includes training a machine learning model using historical machine data, receiving machine data, classifying the machine data, generating an agricultural prescription instruction set; and performing the agricultural prescription instruction set. A computing system includes a processor and a memory storing instructions that, when executed by the processor, cause the computing system to train a machine learning model using historical machine data, receive machine data, classify the machine data, generate an agricultural prescription instruction set; and perform the agricultural prescription instruction set.

Abstract: Methods and software utilizing artificial neural networks (ANNs) to estimate density and/or flow (speed) of objects in one or more scenes each captured in one or more images. In some embodiments, the ANNs and their training configured to provide reliable estimates despite one or more challenges that include but are not limited to, low-resolution images, low framerate image acquisition, high rates of object occlusions, large camera perspective, widely varying lighting conditions, and widely varying weather conditions. In some embodiments, fully convolutional networks (FCNs) are used in the ANNs. In some embodiments, a long short-term memory network (LSTM) is used with an FCN. In such embodiments, the LSTM can be connected to the FCN in a residual learning manner or in a direct connected manner. Also disclosed are methods of generating training images for training an ANN-based estimating algorithm that make training of the estimating algorithm less costly.

Abstract: Various approaches to image enhancement are disclosed. In one approach, a boundary map and an image are received. The boundary map is used to determine that brightness values for a set of pixels included in the image should be regularized. An output image is generated by storing, at both a first and second pixel position in the output image, a set of regularized values. In another approach, an image to be enhanced and an edge map are received. Edge-masked derivative matrices are constructed and used to iteratively solve a series of linear equations, wherein solutions to the linear equations minimize an increasingly accurate quadratic approximation of a penalty function that measures a total amount of variation in a function, and a derivation of the function from the image. A vector result of the iterative solution is transformed into a raster image.

Abstract: A computer-implemented method for identifying features in an image. The method comprises fitting a plurality of second models to the image, the plurality of second models together modelling a region of interest, wherein each part of the region of interest is modelled by at least two of the plurality of second models; and identifying the features in the image based upon the fit of the plurality of second models.

Abstract: A computer-implemented method of generating, from time-series data, a time-series of data sets for predictive analysis, comprises dividing the time-series data into evenly-sized overlapping segments of data, generating an image representing data for each segment, using the time-series data to determine a trend associated with each image, and storing each of the generated images and its associated trend as a data set. In some embodiments of the method the image from each stored data set is transformed into numerical vectors through a feature extraction process using a pre-trained convolutional neural network. The numerical vectors are stored in association with the data set, and the data sets and associated numerical vectors are used to predict the trend for a new time-series image which has been generated from any time-series data.

Abstract: Techniques are described for analyzing images acquired via mobile devices in various ways, including to estimate measurements for one or more attributes of one or more objects in the images, as well as determine changes over time in objects and their measurements based on images acquired at different times. For example, the described techniques may be used to measure the volume of a stockpile of material or other large object, based on images acquired via a mobile device that moves around some or all of the object. The calculation of object volume and/or other determined object information may include generating and manipulating one or more computer models of the object from selected images, and determining changes may include comparing different models for different times. In addition, further automated activities may include displaying, presenting or otherwise providing information about some or all of the determined information.

Abstract: A computer-implemented method for processing images of a document is disclosed. The method includes: displaying, on a display of an electronic device, a live view image of at least part of a document captured by a camera of the electronic device, the at least part of the document having imprinted thereon a plurality of data fields and corresponding data entry items, wherein displaying the live view image includes: receiving image data for a first image of the at least part of the document captured using the camera; extracting one or more of the data entry items from the document based on processing the first image; and displaying the extracted data entry items on the live view image of the at least part of the document, the extracted data entry items being displayed in annotation overlays on the live view image in association with representations of their respective corresponding data fields in the live view image.

Abstract: Certain aspects of an apparatus and method for automatic ER/PR scoring of tissue samples may include for determining a cancer diagnosis score comprising identifying a positive stained nucleus in a slide image of the tissue sample, identifying a negative stained nucleus in the slide image, computing a proportion score based on number of the positive stained nucleus identified and number of the negative stained nucleus identified and determining the cancer diagnosis score based on the proportion.

Abstract: An apparatus for processing a medical image includes an image processor including a plurality of processors, the plurality of processors configured to reconstruct a cross-sectional image of an object by performing a first operation having a first priority and a second operation having a second priority that is lower than the first priority, and a controller configured to monitor whether a malfunction occurs among the plurality of processors, and configured to assign, to at least one of the plurality of processors, at least one of the first operation and the second operation to be performed, based on a result of monitoring of the plurality of processors.

Abstract: An optical correlator includes a first spatial light modulator arranged to receive light from a light source and configured to selectively attenuate the light; a first focusing layer arranged to receive the selectively-attenuated light from the first spatial light modulator and configured to focus the selectively-attenuated light; a first spacer layer substantially transparent to the light from the light source, the first focusing layer being disposed on the first spacer layer; a second spatial light modulator arranged in a Fourier optical relationship with respect to the first spatial light modulator and configured to selectively attenuate the focused light from the first focusing layer to provide twice-attenuated light, the second spatial light modulator being disposed on the first spacer layer opposite the first focusing layer; a second spacer layer substantially transparent to the light from the light source, the second spatial light modulator being disposed on the second spacer layer and positioned betwe

Abstract: A system for advanced vector editing, comprising a vector analysis server that analyzes vector information, a routing calculation server that calculates routes from the vector information, and a rendering engine that produces visualizations from the routing information, and several methods for advanced vector editing in two- and three-dimensional visualizations.

Abstract: Disclosed are a system and method for performing spoof detection. The method includes: receiving, by processor from a biometric sensor, an input image of a biometric; obtaining, by the processor, keypoint locations of keypoints in the input image, wherein keypoints in the input image comprise local regions of interest in the input image; computing, by the processor, one or more anti-spoof metrics of the input image based on the keypoint locations; and, determining, by the processor, whether the input image is a replica of the biometric based on the one or more anti-spoof metrics.

Abstract: Color conversion apparatus comprises mapping circuitry configured to map pixels of an input image having color components in an input color space to pixels of an mapped image having color components in an output color space, the input color space and the output color space being different color spaces such that at least a subset of colors representable in the input color space are not representable in the output color space; processing circuitry configured to process the mapped image by detecting instances, in the color components of pixels of the mapped image, of representations of negative amounts of a color component and in response to such a detection in respect of a pixel under test, to increase all of the color components of the pixel under test by an amount of at least the magnitude of the greatest magnitude negative amount of the color components of that pixel under test; and output circuitry configured to generate pixels of an output image, having color components in the output color space, from pixe

Abstract: A first image is received. An initial label is assigned to at least some pixels in the first image, including by assigning a first label to a first pixel. A determination is made, based at least in part on labels of spectral neighbors of the first pixel, that the first pixel's label should be replaced with a different label. The first pixel's label is updated with the different label.

Abstract: There is described herein a method for recovering missing information in diffusion magnetic resonance imaging (dMRI) data. The data are modeled according to the theory of moving frames and regions where frame information is missing are reconstructed by performing diffusions into the regions. Local orthogonal frames computed along the boundary of the regions are rotated into the regions. Connection parameters are estimated at each new data point obtained by a preceding rotation, for application to a subsequent rotation.

Abstract: The subject disclosure presents systems and computer-implemented methods for automatic immune cell detection that is of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.

Abstract: An analysis method is provided that ensures objective and quantitative analysis for analyzing time-series images.

Abstract: Systems, devices, and methods are presented for segmenting an image of a video stream with a client device by accessing a set of images within a video stream, identifying an object of interest within one or more images of the set of images, and detecting a region of interest within the one or more images. The systems, devices, and method identify a first set of median pixels in a first portion of the object of interest and a second set of median pixels in a second portion of the object of interest. The systems, devices, and methods determine a polyline approximating the first and second sets of median pixels and generate a model for the polyline.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for facial recognition using fractal features are disclosed. In one aspect, a method includes the actions of accessing data encoding a facial image, the facial image including a face. The actions further include generating a hierarchical graphical model of the face in the facial image, the hierarchical graphical model including more than nodes from at least one level, each level approximating the face within a contour. The actions further include applying a bank of filters to the face at a particular node, each filter spanning more than one scale and at least one direction. The actions further include analyzing filter responses from the bank of filters applied at each direction to obtain a similarity measure that consolidates filter responses from filters applied at more than one scale. The actions further include generating a vector representation.