Abstract: A method of transfer learning an object recognition neural network comprises acquiring a set of image frames; determining, by a first object recognition algorithm implementing an object recognition neural network, a plurality of object recognitions in the set of image frames; determining verified object recognitions by evaluating the plurality of object recognitions by a second, different from the first, object recognition algorithm, wherein an object recognition with a positive outcome in said evaluating forms a verified object recognition; forming a training set of annotated images comprising image frames associated with the verified object recognitions; performing transfer learning of the object recognition neural network based on the training set of annotated images.

Abstract: A system and method include identification of a plurality of sub-volumes of an image volume, each of the plurality of sub-volumes of the image volume associated with a respective one of a plurality of vertebra, registration of each of the plurality of sub-volumes of the image volume to one of a plurality of reference sub-volumes associated with a same respective vertebra, input of the registered sub-volumes and the image volume to a trained neural network, reception of an output image volume from the trained neural network, the output image volume labeled to associate voxels with respective vertebra, and display of the output image volume.

Abstract: Embodiments of the disclosure provide systems and methods for detecting a medical condition of a subject. The system includes a communication interface configured to receive a sequence of images acquired from the subject by an image acquisition device and an end-to-end multi-task learning model. The end-to-end multi-task learning model includes an encoder, a Convolutional Recurrent Neural Network (ConvRNN), and at least one of a decoder and a classifier. The system further includes at least one processor configured to extract feature maps from the images using the encoder, capture contextual information between adjacent images in the sequence using the ConvRNN, and detect medical condition of the subject using the classifier based on the extracted feature maps of the image slices and the contextual information or segment each image slice using the decoder to obtain a region of interest indicative of the medical condition based on the extracted feature maps.

Abstract: An improvement to automatic classifying of threat level of objects in CT scan images of container content, methods include automatic identification of non-classifiable threat level object images, and displaying on a display of an operator a de-cluttered image, to improve operator efficiency. The decluttered image includes, as subject images, the non-classifiable threat level object images. Improvement to resolution of non-classifiable threat objects includes computer-directed prompts for the operator to enter information regarding the subject image and, based on same, identifying the object type. Improvement to automatic classifying of threat levels includes incremental updating the classifying, using the determined object type and the threat level of the object type.

Abstract: There is provided a vehicle system including a sensing unit, a processing unit, a control unit and a display unit. The sensing unit is configured to capture an image frame containing an eyeball image from a predetermined distance. The processing unit is configured to calculate a pupil position of the eyeball image in the image frame and generate a drive signal corresponding to the pupil position. The control unit is configured to trigger a vehicle device associated with the pupil position according to the drive signal. The display unit is configured to show information of the vehicle device.

Abstract: A morphing part to be morphed and a morphing effect are acquired. Human body contour point information is acquired by detecting a human contour point of a human sideway image. A morphing region is determined according to the human contour point information and the morphing part. The morphing region is an image region to be morphed in the human sideway image. A human orientation is determined according to the human contour point information. A morphing direction of the morphing region is determined according to the human orientation and the morphing effect. Morphing is performed along the morphing direction in the morphing region.

Abstract: A system and method for recognizing objects in an image is described. The system can receive an image from a sensor and detect one or more objects in the image. The system can further detect one or more components of each detected object. Subsequently, the system can create a segmentation map based on the components detected for each detected object and determine whether the segmentation map matches a plurality of 3-D models (or projections thereof). Additionally, the system can display a notification through a user interface indicating whether the segmentation map matches at least one of the plurality of 3-D models.

Abstract: An object tracking system includes a sensor and a tracking system. The sensor is configured to capture a first frame of a global plane for at least a portion of a space. The tracking system is configured to receive a first coordinate in the global plane where a first marker is located in the space and to receive a second coordinate in the global plane where a second marker is located in the space. The tracking system is further configured to identify the first marker and the second marker within the first frame, to determine a first pixel location in the first frame for the first marker, to determine a second pixel location in the first frame for the second marker, and to generate a homography based on the first coordinate, the second coordinate, the first pixel location, and the second pixel location.

Abstract: A real-time pose measurement method of a planar coding target for a vision system. The planar coding target includes a plurality of coding elements, a coding block, a coding template, a minimum identification unit pattern and a coding pattern. Each coding element has a unique coding value, and serial numbers of the coding elements are different from each other. The coding block includes four coding elements that are distributed in the same rectangle ABCD and do not overlap with each other. A center of the coding block is an intersection point O of two diagonals of the rectangle ABCD. A coding value of the coding block is associated with coding values of the four coding elements contained therein.

Abstract: A method including selecting image data of a mechanical mud separation machines (“MMSM”) to detect objects in an object flow and other operational conditions at the MMSM. The image data may be processed by a Deep Neural Network to identify objects in the object flow, operational parameters of the MMSM, and environmental conditions. Additional image data may be selected for additional processing based on the results of the analysis.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: A system, method, and apparatus for displaying a fused reconstructed image with a multidimensional image are disclosed. An example imaging system receives a selection corresponding to a portion of a displayed multidimensional visualization of a surgical site. At the selected portion of the multidimensional visualization, the imaging system displays a portion of a three-dimensional image which corresponds to the selected multidimensional visualization such that the displayed portion of the at least one of the three-dimensional image or model is fused with the displayed multidimensional visualization.

Abstract: A method for measuring humidity at long range using simplified equipment includes creating a formula according to a relationship between multiple sets of known optical flow feature vectors and a known humidity. First and second images are obtained, wherein the first image and the second image are captured as being in the same range of capture. A plurality of feature points in the first image is obtained and an optical flow feature vector for each of the feature points according to apparent changes in position of each feature point according to the second image are calculated. The degree of current humidity according to the optical flow feature vectors and the formula is thus obtained.

Abstract: A face image processing device, includes: an image coordinate system coordinate value derivation unit detecting an x-coordinate value and a y-coordinate value in an image coordinate system at a feature point of an organ of a face of a person in an image, and estimating a z-coordinate value, so as to derive three-dimensional coordinate values in the image coordinate system; a camera coordinate system coordinate value derivation unit deriving three-dimensional coordinate values in a camera coordinate system from the three-dimensional coordinate values in the image coordinate system derived by the image coordinate system coordinate value derivation unit; and a parameter derivation unit applying the three-dimensional coordinate values in the camera coordinate system derived by the camera coordinate system coordinate value derivation unit to a predetermined three-dimensional face shape model to derive a model parameter of the three-dimensional face shape model in the camera coordinate system.

Abstract: Methods and apparatus to measure brand exposure in media streams are disclosed. Example apparatus disclosed herein are to determine a first histogram based on at least one of luminance components or chrominance components of a first frame of video, and determine a second histogram based on at least one of luminance components or chrominance components of a second frame of the video. Disclosed example apparatus are also to detect a transition in the video based on the first histogram and the second histogram, and responsive to the detection of the transition in the video. Disclosed example apparatus are further to process a region of interest within at least one of the first frame or the second frame to detect a logo in the region of interest.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a deep neural network to process object user indicators and an initial object segmentation from a digital image to efficiently and flexibly generate accurate object segmentations. In particular, the disclosed systems can determine an initial object segmentation for the digital image (e.g., utilizing an object segmentation model or interactive selection processes). In addition, the disclosed systems can identify an object user indicator for correcting the initial object segmentation and generate a distance map reflecting distances between pixels of the digital image and the object user indicator. The disclosed systems can generate an image-interaction-segmentation triplet by combining the digital image, the initial object segmentation, and the distance map.

Abstract: Systems and methods for diagnosing and facilitating repair of system components and/or improving watering schedules are disclosed. In one embodiment, a method of diagnosing a sprinkler system component is disclosed that includes initiating a sprinkler system zone run for a particular zone of a sprinkler system, capturing image data for the sprinkler system run, uploading the image data to a network node, processing the image data at the network node to produce data on the sprinkler system or a sprinkler system component, and generating an action item for the sprinkler system or sprinkler system component based on the data produced at the network node.

Abstract: Methods and systems are provided for image processing. A plurality of correlation parameters representing degrees of correlation between two or more images of a plurality of images may be produced. An optimized correlation dependency graph may be produced according to the plurality of correlation parameters. The plurality of images may then be delta encoded according to the optimized correlation dependency graph. For example, the optimized correlation dependency graph may be used for performing a correlation encoding operation. The plurality of correlation parameters may be produced, for example, in accordance with one or more correlation metrics associated with the correlation encoding operation.

Abstract: A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.

Abstract: A monitoring method and system for providing visual enhancements during a medical procedure is described. The method includes capturing current visual information of a site during the medical procedure in real time, storing at least a portion of the captured visual information as stored visual information, identifying a feature of interest in at least one of the current visual information and the stored visual information; generating feedback data associated with the feature of interest, and displaying a virtual representation of the feedback data overlaid on the current visual information.