Abstract: Described is an, such as an unmanned aerial vehicle (“UAV”), that includes stereo pairs of imaging element, each imaging element including a region of interest controller. The region of interest controller for an imaging element of the stereo pair receives movement information affecting the imaging element and selects a portion of pixels of a digital image formed by the imaging element. The portion of pixels are provided to an image processor that utilizes the portion of pixels to determine depth information for objects represented by the pixels.

Abstract: Example embodiments relate to methods for detecting defects of a lithographic pattern. One example embodiment includes a method for detecting defects of a lithographic pattern on a semiconductor wafer that includes a plurality of die areas. Each of the die areas has a region of interest (ROI) that includes a plurality of features forming the lithographic pattern. The method includes acquiring an image of at least one of the ROIs. The method also includes removing features touching an edge of the image. Further, the method includes counting a number of remaining features in the image.

Abstract: A method for removing defective pixel image-artifacts includes an adaptive reconstruction kernel taking up to four main and diagonal, defect-free sub-kernel directions into account composed for each defective image pixel. The defective pixels impacted image is real-time corrected by statistical filtering or by a weighed directional convolution of kernel-associated replacement values, calculated by means of an advanced multi-parabolic reconstruction algorithm, for each contributing sub-kernel direction based on 5×5 pixels neighborhood image data readily accessible via a predetermined AMP kernels image-offsets structure.

Abstract: A method for processing fingerprint information may include obtaining an input image corresponding to at least a region of a user fingerprint, searching a memory storing at least one registered image for a reference registered image that matches the input image at least partially, determining a unique region of the input image in which the input image does not overlap with the reference registered image when the input image is aligned with the reference registered image, and, in response to a score of the unique region of the input image being greater than or equal to a predetermined reference score, classifying the input image as belonging to a same group as the reference registered image and storing the input image in the memory.

Abstract: A system and method for comparing and searching for digital data, such as images, using histograms includes receiving a source image, receiving a comparison image, generating a first histogram for the source image and generating a second histogram for the comparison image. The source image may be received from a network device, such as a computer or camera, and the comparison image may one of a plurality of stored images in a database. The histograms may correspond to an image characteristic, including a color histogram corresponding to the distribution of the intensity of a corresponding color among image pixels in the source image. Each of first and second histograms is normalized and a similarity score is calculated between the two histograms. The similarity score represents a similarity measure between the two histograms, calculated from a subset of bins, which are independently selected for each image.

Abstract: Computer-implemented systems and methods herein disclose automatic haze correction in a digital video. In one example, a video dehazing module identifies a scene including a set of video frames. The video dehazing module identifies the dark channel, brightness, and atmospheric light characteristics in the scene. For each video frame in the scene, the video dehazing module determines a unique haze correction amount parameter by taking into account the dark channel, brightness, and atmospheric light characteristics. The video dehazing module applies the unique haze correction amount parameters to each video frame and thereby generates a sequence of dehazed video frames.

Abstract: A drowsiness detection apparatus includes a camera that images a face of a user, an opening/closing-degree calculation unit that calculates an eyelid opening/closing degree based on a face image acquired by the imaging, a face-angle detection unit that detects a face angle based on the face image, a distance calculation unit that calculates the distance between an eyelid and an eyebrow based on the face image, an opening/closing-degree correction unit that corrects the eyelid opening/closing degree based on the detected face angle and the calculated distance between the eyelid and eyebrow, and an eyelid-opening/closing determination unit that determines whether the eyelids are opened or closed by comparing the eyelid opening/closing degree with a predetermined threshold value.

Abstract: This disclosure relates generally to image processing, and more particularly to generate a high resolution image from multiple low resolution images. In an embodiment, the system collects a plurality of low resolution images as input, processes the collected images, and generates a high resolution image as output. During this process, the system pre-processes the collected low resolution images, and during this process, at least one characteristic of each of the low resolution image is changed with respect to a reference image. After the pre-processing stage, the images are then enhanced based on a plurality of final enhancement factors that are dynamically determined. Further, the enhanced images are fused to generate the high resolution image.

Abstract: A multi-function biometric scanner is provided. The multi-function biometric scanner includes a housing that includes a dome-shaped or semi-dome shaped user interface, the user interface including a capacitive film for fingerprint capture that is disposed along an outer-surface of the housing and a plurality of biometric sensors that are disposed within the housing and that are configured to concurrently retrieve a plurality of biometrics from a user, each sensor being configured to measure a respective biometric of the plurality of biometrics. Fingerprints and the plurality of biometrics are compared to threat information in one or more threat databases to identify a person of interest.

Abstract: Provided are systems, methods, and computer-readable medium for operating a neural network. In various implementations, the neural network can receive an input image that includes an object to be identified. The neural network can generate a plurality of initial feature maps using a convolution layers, wherein a first initial feature maps is generated using the input image. The neural network can generate an up-sampled feature map using a de-convolution layer that takes an initial feature map as input, where the up-sampled feature map has a same resolution as the previous initial feature map. The neural network can combine the up-sampled feature map and the previous initial feature map, and use the combined feature map to more accurate identify the object.

Abstract: A computer-implemented method and systems are provided for performing machine learning for an image classification task. The method includes selecting, by a processor operatively coupled to one or more databases, a first and a second image from one or more training sets in the one or more databases. The method further includes overlaying, by the processor, the second image on the first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image. The method also includes training, by the processor, a machine learning process configured for the image classification task using the mixed image to augment data used by the machine learning process for the image classification task.

Abstract: Methods, apparatus, systems and articles of manufacture of logo recognition in images and videos are disclosed. An example method to detect a specific brand in images and video streams comprises accepting luminance images at a scale in an x direction Sx and a different scale in a y direction Sy in a neural network, and training the neural network with a set of training images for detected features associated with a specific brand.

Abstract: A system estimates quality of a digital image by accessing a corpus of digital images of one or more subjects, such as a facet of a property. The system will receive, for at least a subset of the corpus, an indicator that one or more patches of each image in the subset is out of focus. The system will train a classifier by obtaining a feature representation of each pixel in each image, along with a focus value that represents an extent to which each pixel in the image is in focus or out of focus. The system will use the classifier to analyze pixels of a new digital image and assess whether each analyzed pixel in the new digital image is in focus or out of focus. The system may use the image to assess whether an incident occurred, such as storm-related damage to the property.

Abstract: Described herein is a system and method for scanning a three-dimensional object using data from an infrared sensor. The data can be used during preprocessing, reconstructing and/or post processing of generation of a three-dimensional model. Data from an infrared sensor and data from a sensor (e.g., RGB sensor, a depth sensor, a camera, a scanner, a digital camera, a digital video camera, a web camera, depth sensor, etc.) can be utilized to generate a three-dimensional model of the three-dimensional object. For example, the data from the infrared sensor can be utilized to identify an item and to exclude the identified item from the generated three-dimensional model.

Abstract: Various embodiments can include artificial learning counter surveillance (ALCS) or self-protection surveillance systems (SPSS) and related methods. Apparatuses and methods can include non-destructive electro-optic interference or protection systems as well non-destructive directed energy systems, a control system, and an analysis system for determining an optical system of interest and generating degradation or disruption effects using various equipment items and machine learning systems. Additionally, methods are also provided for determining degree of severity of degradation or disruption based on threshold definitions related to ability to use the optical system of interest for one or more specified applications.

Abstract: A device for forming a random set of playing cards comprises a card in-feed area, a shuffling system, a card removal area, and a card reading system located within the device, the card reading system employing a complementary metal-oxide semiconductor (CMOS) sensor and a hardware component, the hardware component capable of converting signals from the CMOS sensor into vector sets and comparing the vector sets to known vectors to determine rank and suit.

Abstract: In an information processing apparatus that includes sequences of weak classifiers which are logically cascade-connected in each sequence and the sequences respectively correspond to categories of an object and in which the weak classifiers are grouped into at least a first group and a second group in the order of connection, classification processing by weak classifiers belonging to the first group of respective categories is performed by pipeline processing. Based on the processing results of the weak classifiers belonging to the first group of the respective categories, categories in which classification processing by weak classifiers belonging to the second group is to be performed are decided out of the categories. The classification processing by the weak classifiers respectively corresponding to the decided categories and belonging to the second group is performed by pipeline processing.

Abstract: Image processing methods and systems apply filtering operations to images, wherein the filtering operations use filter costs which are based on image gradients in the images. In this way, image data is filtered for image regions in dependence upon the image gradients for the image regions. This may be useful for different scenarios such as when combining images to form a High Dynamic Range (HDR) image. The filtering operations may be used as part of a connectivity unit which determines connected image regions, and/or the filtering operations may be used as part of a blending unit which blends two or more images together to form a blended image.

Abstract: A method and apparatus for inverse tone mapping of an LDR image with computer generated imagery components, such components being also called visual effects, are disclosed. The disclosed method includes accessing a first LDR image, the first LDR image being obtained from at least one computer generated imagery component rendered and composited into an LDR reference image or background plate; determining an HDR lighting indication responsive to information extracted from a part of the first LDR image and a respective part of a first intermediate HDR image obtained by applying a first pass inverse tone mapping to the first LDR image; obtaining a second intermediate HDR image by applying a second pass inverse tone mapping to the LDR reference image; obtaining the first HDR image from the computer generated imagery components rendered using HDR lighting indication and composited into the second intermediate HDR image.

Abstract: The present disclosure provides a display substrate, a method for manufacturing the same and a display device. The display substrate includes a base substrate; light-emitting devices for displaying on the base substrate; driver thin film transistors on the base substrate for driving the light-emitting devices for displaying to emit light; optical fingerprint identification devices on the base substrate; and switch thin film transistors on the base substrate for controlling the optical fingerprint identification devices. The optical fingerprint identification devices are in a display area of the display substrate. Each film layer of each driver thin film transistor and a corresponding same film layer of each switch thin film transistor are in an identical layer and are made of the same material.