Abstract: A system and method of performing high precision anatomical measurements of non-visible aspects of features of living organisms that include a visible contoured shape on the anatomical region of the living organism is disclosed. The system and method include an imager configured to take a series of images of an anatomical region of a living organism, such as a human patient in a clinic, doctor's office, or hospital. The system and method create a three-dimensional digital anatomical model of the exterior or visible part of the anatomical region that includes a target feature, such as a breast, nose, foot, or tumor. The target feature within the three-dimensional digital model is then isolated and manipulated to find measurements of non-visible aspects of the feature, such as mass, distances between visible points which pass through invisible tissues, e.g., depth and base width, volume, area, and surface angle measurements of the feature.

Abstract: A method for detecting wheels of a vehicle on a road comprises directing a vehicle classification sensor onto a section of the road and recording a 3D representation of the vehicle, directing a camera onto the section and recording a source 2D image, determining a bounding box circumscribing the 3D representation, a side and four corner points of said bounding box side, identifying corner points in a source 2D image plane, defining four corner points of a rectangle in a destination 2D image plane, calculating a projective transformation between the source corner image points and the destination corner image points, transforming source 2D image pixels to destination 2D image pixels of a first destination 2D image, and detecting the wheels using the destination 2D image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for eye gesture recognition. In one aspect, a method includes obtaining an electrical signal that represents a measurement, by a photodetector, of an optical signal reflected from an eye and determining a depth map of the eye based on phase differences between the electrical signal generated by the photodetector and a reference signal. Further, the method includes determining gaze information that represents a gaze of the eye based on the depth map and providing output data representing the gaze information.

Abstract: One example provides a computing system comprising a depth sensor comprising a plurality of pixels, and a storage machine holding instructions executable by a logic machine to, for each pixel, make K phase measurements to form a set of noisy phase measurements, determine a location at which a projection line that passes through the set of noisy phase measurements in a K-dimensional phase space passes through a lower dimensional plane, the projection line being parallel to a noise free phase evolution line, compare the location to a plurality of independent terms of a predetermined matrix of points in the lower dimensional plane, locate a corresponding set of noiseless phase orders by using a selected set of independent terms to reference a look-up table, determine a distance value for the pixel based upon the corresponding set of noiseless phase orders, and output the distance value for the pixel.

Abstract: This invention provides a system and method for using an area scan sensor of a vision system, in conjunction with an encoder or other knowledge of motion, to capture an accurate measurement of an object larger than a single field of view (FOV) of the sensor. It identifies features/edges of the object, which are tracked from image to image, thereby providing a lightweight way to process the overall extents of the object for dimensioning purposes. Logic automatically determines if the object is longer than the FOV, and thereby causes a sequence of image acquisition snapshots to occur while the moving/conveyed object remains within the FOV until the object is no longer present in the FOV. At that point, acquisition ceases and the individual images are combined as segments in an overall image. These images can be processed to derive overall dimensions of the object based on input application details.

Abstract: Discussed generally are techniques for managing operation of programs in a sequential order. A method can include receiving a query for an image, the query indicating characteristics of the image, selecting a chain of algorithms configured to identify the image based on the characteristics, operating an algorithm of the selected chain of algorithms that operate in increased fidelity order on an input to produce a first result, operating a ground truth algorithm on the input to generate a second result, comparing the first and second results to determine a probability of correctness (Pc) and confidence interval (CI) for the algorithm, and altering the chain of algorithms based on the determined Pc and CI.

Abstract: The present disclosure discloses an unsupervised domain adaptation method, a device, a system and a storage medium of semantic segmentation based on uniform clustering; first, a prototype-based source domain uniform clustering loss and an empirical prototype-based target domain uniform clustering loss are established, to reduce intra-class differences of pixels responding to the same category; meanwhile, the pixels with similar structures but different classes are driven away from each other, wherein they tend to be evenly distributed, increasing the inter-class distance and overcoming the problem that the category boundaries are unclear during the domain adaptation process; next, the prototype-based source domain uniform clustering loss and the empirical prototype-based target domain uniform clustering loss are integrated into an adversarial training framework, which reduces the domain difference between the source domain and the target domain, thus improving the accuracy of semantic segmentation.

Abstract: A method for generating a super resolution image may comprise up-scaling an input low resolution image; determining a directivity for each patch included in the up-scaled image; selecting an orientation-specified neural network or an orientation-non-specified neural network according to the directivity of the patch; applying the selected neural network to the patch; and obtaining a super resolution image by combining one or more patches output from the orientation-specified neural network and the orientation-non-specified neural network.

Abstract: In some implementations, a device may receive an image that depicts an environment associated with a vehicle. The device may partition the image into a plurality of subsections. The device may analyze the plurality of subsections to determine respective subsection information, wherein subsection information, for an individual subsection, indicates: a probability score that the subsection includes a line segment associated with an object class, a position of a representative point of the line segment, and a direction of the line segment. The device may identify, based on the respective subsection information of the plurality of subsections, a line associated with the object class that is associated with a set of subsections of the plurality of subsections. The device may perform one or more actions based on identifying the line associated with the object class.

Abstract: In some implementations, a device may receive image data identifying images of a space with racks and objects stored on the racks. The device may receive location data identifying location coordinates associated with the images. The device may process the image data and the location data to generate a merged point cloud identifying the racks and the objects in the space. The device may process the image data to generate mask data identifying at least a first mask for the racks and a second mask for the objects. The device may process the location data, the merged point cloud, and the mask data to generate a semantic point cloud identifying the racks and the objects in the space. The device may process the semantic point cloud, with a computer vision model, to calculate utilization metrics for the space.

Abstract: A system, apparatus, method and computer program product are provided for determining a mobile device integrity status. Images of a mobile device captured by the mobile device and using a reflective surface are processed with various trained models, such as neural networks, to verify authenticity, detect damage, and to detect occlusions. A mask may be generated to enable identification of concave occlusions or blocked corners of an object, such as a mobile device, in an image. Images of the front and/or rear of a mobile device may be processed to determine the mobile device integrity status such as verified, not verified, or inconclusive. A user may be prompted to remove covers, remove occlusions, and/or move the mobile device closer to the reflective surface. A real-time response relating to the mobile device integrity status may be provided. The trained models may be trained to improve the accuracy of the mobile device integrity status.

Abstract: The present invention relates to a method of determining stock in an inventory. The method comprises obtaining one or more images comprising one or more objects. Further, estimating a three dimensional (3D) location of a Stock Keeping Unit (SKU) marker associated with each of one or more visible objects. Furthermore, determining a stacking pattern of the one or more objects for each level on the pallet using one of the 3D location of SKU marker and a learning model. Thereafter, detecting at least one of presence or absence of one or more undetected objects at each level based on the stacking pattern and the 3D location of the SKU marker. Finally, determining the stock in the inventory based on the presence or the absence of the one or more undetected objects and the one or more visible objects.

Abstract: The invention relates to reconstructing a synthetic dense super-resolution image from at least one low-information-content image, for example from a sequence of diffraction-limited images acquired by single molecule localization microscopy. After having obtained such a sequence of diffraction-limited images, a sparse localization image is reconstructed from the obtained sequence of diffraction-limited images according to single molecule localization microscopy image processing.

Abstract: An information processing apparatus includes a captured image acquisition unit that acquires an image captured by imaging a device including plural markers. A marker image coordinate specification unit specifies a representative coordinate of each marker image from the captured image, and a position and posture derivation unit derives position information and posture information of the device using the representative coordinates of the marker images. The marker image coordinate specification unit specifies a first boundary box surrounding a region within which pixels having a first luminance or more continuously appear, specifies a second boundary box surrounding a region within which pixels having a second luminance or more continuously appear in the first boundary box, the second luminance being higher than the first luminance, and derives a representative coordinate of each marker image based on pixels in the first or second boundary box in response to the number of specified second boundary boxes.

Abstract: A system accesses content structure that includes a first attribute table including a first list of attributes of a first object, and a first mapping including first attribute values. The first list of attributes of the first object also includes a quality attribute indicating a first quality. After a request to modify quality is received, the system searches a plurality of content structures for a suitable second content structure that comprises a second attribute table including a second list of attributes of a second object. The suitable content structure has another attribute that matches a corresponding attribute of the first list of attributes of the first object and a quality attribute indicating a second quality. The system modifies the first attribute table to include the second list of attributes of the second object. In this way content is generated that is of higher or lower quality than the original content.

Abstract: An intelligent police car loudspeaker includes a loudspeaker mechanism composed of a dust cover, a paper cone, a yoke ring, a centering piece, a magnet, a voice coil, and a soft iron core. The voice coil is disposed around the soft iron core. The magnet is disposed around the voice coil. The paper cone is disposed above the magnet. The centering piece is disposed above the magnet and located inside the paper cone. The yoke ring is disposed above the centering piece and located inside the paper cone. The dust cover is configured to cover an opening of the paper cone. With the present invention, the level of intelligence of the loudspeaker can be improved.

Abstract: Provided is a method for reconstructing content image data. The method includes selecting a first point and a second point in a first image of first content, selecting a third point and a fourth point in a second image of second content (the second image is an image corresponding to the first image and the third point and the fourth point are points in an image corresponding to the first point and the second point, respectively), generating a first reference vector using the first point and the second point, generating a second reference vector using the third point and the fourth point, calculating a rotation, scale, and transformation (RST) value from the first image to the second image using the first reference vector and the second reference vector; and reconstructing the second content using the calculated RST value.

Abstract: A system, a method, and a computer program for surveillance and area encroachment detection, including receiving satellite image data containing an image of a geospatial area, extracting features from the satellite image data of an object in the image, classifying the object in the image based on the extracted features, comparing the extracted features of the object to previously extracted features for the geospatial area, determining delta features between the extracted features of the object and the previously extracted features for the geospatial area, determining existence of an alarm event in the geospatial area based on the delta features, and transmitting an alarm event message to a communicating device.

Abstract: Various approaches are disclosed to temporally and spatially filter noisy image data—generated using one or more ray-tracing effects—in a graphically rendered image. Rather than fully sampling data values using spatial filters, the data values may be sparsely sampled using filter taps within the spatial filters. To account for the sparse sampling, locations of filter taps may be jittered spatially and/or temporally. For filtering efficiency, a size of a spatial filter may be reduced when historical data values are used to temporally filter pixels. Further, data values filtered using a temporal filter may be clamped to avoid ghosting. For further filtering efficiency, a spatial filter may be applied as a separable filter in which the filtering for a filter direction may be performed over multiple iterations using reducing filter widths, decreasing the chance of visual artifacts when the spatial filter does not follow a true Gaussian distribution.

Abstract: An object detection device equipped with: a first object detection unit that uses a predetermined prediction model to acquire, from a known first image for which the coordinates of a detection subject are known, a confidence value with respect to each detection region extracted from a plurality of positions in the first image; a parameter determination unit that, on the basis of the acquired confidence value, determines parameters that include a detection threshold value which is a threshold value with respect to the confidence value; and a second object detection unit that uses the prediction model to acquire a confidence value with respect to each detection region extracted from the narrowed-down detection region candidates, and detects the detection subject on the basis of the acquired confidence value.