A multimodality imaging system and method for mineralogy segmentation is disclosed. Image datasets of the sample are generated for one or more modalities, including x-ray and focused ion beam scanning electron microscope (FIB-SEM) modalities. Mineral maps are then created using Energy Dispersive X-ray spectroscopy (EDX) from at least part of the sample covered by the image datasets. The EDX mineral maps are applied as a mask to the image datasets to identify and label regions of minerals within the sample. Feature vectors are then extracted from the labeled regions via feature generators such as Gabor filters. Finally, machine learning training and classification algorithms such as Random Forest are applied to the extracted feature vectors to construct a segmented image representation of the sample that classifies the minerals within the sample.

Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

The present invention provides a Gabor cube feature selection-based classification method for hyperspectral remote sensing images, comprising the following steps: generating three-dimensional Gabor filters according to set frequency and direction parameter values; convoluting hyperspectral remote sensing images with the three-dimensional Gabor filters to obtain three-dimensional Gabor features; selecting three-dimensional Gabor features, classification contribution degrees to various classes of which meet preset requirements, from the three-dimensional Gabor features; and classifying the hyperspectral remote sensing images by a multi-task joint sparse representation-based classification means by using the selected three-dimensional Gabor features. The present invention is based on the three-dimensional Gabor features, and the used three-dimensional Gabor features contain rich local change information of a signal and are competent in feature characterizing. Using a Fisher discriminant criterion not only makes full use of high-level semantics hidden among the features, but also eliminates redundant information and reduces the classification time complexity.

The invention provides a method for recognition of information in digital image data, said method comprising a learning phase on a data set of example digital images having known information, and characteristics of categories are computed automatically from each example digital image and compared to its known category, said method comprises training a convolutional neural network comprising network parameters using said data set, in which via deep learning each layer of said convolutional neural network is represented by a linear decomposition of all filters as learned in each layer into basis functions.

The present disclosure is related in general to image processing and a method and system for generating a multi-level classifier for image processing. An image processing system may analyse an input image of a predetermined image type to extract unique key feature descriptors associated with the input image. Further, the unique key feature descriptors are resized into a predefined standard template format which is utilized to develop an image type classifier. Furthermore, the unique key feature descriptors are resized into each of one or more template classifiers of the predetermined image type. Further, signal quality value of each of the template classifiers is determined by validating each of the unique key feature descriptors resized based on each of the template classifiers and an image prediction classifier is developed based on the signal quality value.

Systems and methods for medical image diagnoses in accordance with embodiments of the invention are illustrated. One embodiment includes a method for evaluating multimedia content. The method includes steps for receiving multimedia content and identifying a set of one or more image frames for each of several target views from the received multimedia content. For each target view, the method includes steps for evaluating the corresponding set of image frames to generate an intermediate result. The method includes steps for determining a composite result based on the intermediate results for each of the several target views.

A multi-source image correspondence method and system based on heterogeneous model fitting is provided, the method includes the following steps: constructing a multi-orientation phase consistency model, fusing phase consistency, image amplitude, and orientation detection feature points, constructing logarithmic polar coordinate descriptors with variable-size bins using sub-region grids and orientation histograms, effectively estimating model parameters through heterogeneous model fitting, accumulating matching pairs from different heterogeneous models that meet a preset joint position offset transformation error, outputting a final matching pair, and completing multi-source image correspondence. The present disclosure alleviate the influence of nonlinear radiation distortion by constructing the multi-orientation phase consistency model, constructing logarithmic polar coordinate descriptors with variable-size bins by sub-region grids and orientation histograms, removing an abnormal matching relationship in multi-source images with the heterogeneous model fitting method, thereby improving the accuracy and robustness of feature detection and improving multi-source image correspondence performance.

A biometrics authentication device is configured to include a non-directional feature generation process unit configured to generate a non-directional feature on the basis of a directional features; a directional feature generation process unit configured to select, from among the directional features, a reference directional feature corresponding to a reference direction; a non-directional feature matching process unit configured to obtain a first degree of similarity between the non-directional feature and a registered non-directional feature; a directional feature matching process unit configured to obtain a second degree of similarity between the reference directional feature and a registered reference directional feature; and a determination unit configured to make a weight of the second degree of similarity smaller than a weight of the first degree of similarity and to determine whether or not a subject is a person to be authenticated, by using the first degree of similarity and the second degree of similarity.

This disclosure relates generally to visual inspection systems, and more particularly to a method and system for facilitating real time detection of linear infrastructural objects in aerial imagery. In one embodiment, a background suppression technique is applied to one or more hardware processors to a HSV image. Further, a mean shift filtering technique is applied to the hardware processors to find a peak of a confidence map and then a gradient image generation is performed for a plurality of edges of the image. A seed point pair along a middle cut portion of a linear feature of the HSV image to identify one or more boundaries of the seed point pair is extracted and then a contour growing approach to detect the boundaries of the linear feature is initiated. Lastly, one or more false positives are removed by using a rigidity feature, the rigidity feature being equivalent to the total sum of gradient orientations.

A deep learning based compression (DLBC) system generates a progressive representation of the encoded input image such that a client device that requires the encoded input image at a particular target bitrate can readily be transmitted the appropriately encoded data. More specifically, the DLBC system computes a representation that includes channels and bitplanes that are ordered based on importance. For a given target rate, the DLBC system truncates the representation according to a trained zero mask to generate the progressive representation. Transmitting a first portion of the progressive representation enables a client device with the lowest target bitrate to appropriately playback the content. Each subsequent portion of the progressive representation allows the client device to playback the content with improved quality.