Abstract: An imaging device includes one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the imaging device to perform functions including: capturing a first image and thereafter a second image; making a determination of whether or not a difference between the first image and the second image is greater than a threshold value; generating a third image by processing the second image using an image processing algorithm that corresponds to the determination; and displaying the third image.

Abstract: The present disclosure relates to the generation of partial surface models from volumetric datasets for subsequent registration of such partial surface models to surface topology datasets. Specifically, given an object that is imaged using surface topology imaging and another volumetric modality, the volumetric dataset is processed in combination with an approach viewpoint to generate one or more partial surfaces of the object that will be visible to the surface topology imaging system. This procedure can eliminate internal structures from the surfaces generated from volumetric datasets, thus increases the similarity of the dataset between the two different modalities, enabling improved and quicker registration.

Abstract: A system and method for identifying media segments using audio augmented image cross-comparison is disclosed, in which a media segment identifying system analyses both audio and video content, producing a unique identifier to compare with previously identified media segments in a media segment database. The characteristic landmark-linked-image-comparisons are constructed by first identifying an audio landmark. The audio landmark is an audio peak that exceeds a predetermined threshold. Two digital images are then obtained, one associated directly with the audio landmark, and one obtained a predetermined landmark time removed from the first image. The two images are then used to provide a characteristic landmark-linked-image-comparison. The pair of images are reduced in pixel size and converted to gray scale. Corresponding pixels are compared to form a numeric comparison. One image is mirrored before comparison to reduce the possibility of null comparisons.

Abstract: An image processing apparatus includes a character recognition processing unit configured to execute character recognition processing on the image data, an acquisition unit configured to acquire one or more character string blocks included in the image data, from the image data, a selection unit configured to select a character string block to be used for setting of a file name, from among the one or more character string blocks acquired by the acquisition unit, and a setting unit configured to set the file name of image data by using a character recognition result of the character recognition processing unit for the character string block selected by the selection unit.

Abstract: An object detection training method can include receiving a training sample set in a current iteration of an object detection training process over an object detection neural network. The training sample set can include first samples of a first class and second samples of a second class. A first center loss value of each of the first and second samples can be determined. The first center loss value can be a distance between a feature vector of the respective sample and a center feature vector of the first or second class which the respective sample belongs to. A second center loss value of the training sample set can be determined according to the first center loss values of the first and second samples. A first target loss value of the current iteration can be determined according to the second center loss value of the training sample set.

Abstract: A method for training a neural network includes receiving a plurality of images and, for each individual image of the plurality of images, generating a training triplet including a subset of the individual image, a subset of a transformed image, and a homography based on the subset of the individual image and the subset of the transformed image. The method also includes, for each individual image, generating, by the neural network, an estimated homography based on the subset of the individual image and the subset of the transformed image, comparing the estimated homography to the homography, and modifying the neural network based on the comparison.

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: Mobile phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some aspects relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others concern user interface improvements. Other aspects relate to imaging architectures, in which a mobile phone's image sensor is one in a chain of stages that successively act on packetized instructions/data, to capture and later process imagery. Still other aspects relate to distribution of processing tasks between the mobile device and remote resources (“the cloud”). Elemental image processing (e.g., simple filtering and edge detection) can be performed on the mobile phone, while other operations can be referred out to remote service providers.

Abstract: A fingerprint sensor includes a die, a plurality of conductive structures, an encapsulant, a plurality of conductive patterns, a first dielectric layer, a second dielectric layer, and a redistribution structure. The die has an active surface and a rear surface opposite to the active surface. The conductive structures surround the die. The encapsulant encapsulates the die and the conductive structures. The conductive patterns are over the die and are electrically connected to the die and the conductive structures. Top surfaces of the conductive patterns are flat. The first dielectric layer is over the die and the encapsulant. A top surface of the first dielectric layer is coplanar with top surfaces of the conductive patterns. The second dielectric layer covers the first dielectric layer and the conductive patterns. The redistribution structure is over the rear surface of the die.

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 de-cluttered 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: The invention relates to an apparatus for optically monitoring the dosing of a liquid to be pipetted for an automatic analysis unit. The apparatus comprises a dosing device, comprising a pipetting needle for pipetting the liquid, a lighting device for illuminating a drop of the liquid adhering to the pipetting needle, a camera with a set of optics to capture an image of the drop of the liquid, and an evaluation device for characterizing the drop of liquid by means of an automatic analysis of the image of the drop of liquid.

Abstract: A force sensed surface scanning system (20) employs a scanning robot (41) and a surface scanning controller (50). The scanning robot (41) includes a surface scanning end-effector (43) for generating force sensing data informative of a contact force applied by the surface scanning end-effector (43) to an anatomical organ. In operation, the surface scanning controller (50) controls a surface scanning of the anatomical organ by the surface scanning end-effector (43) including the surface scanning end-effector (43) generating the force sensing data, and further constructs an intraoperative volume model of the anatomical organ responsive to the force sensing data generated by the surface scanning end-effector (43) indicating a defined surface deformation offset of the anatomical organ.

Abstract: Implementations described herein generally relate to a method for detecting anomalies in time-series traces received from sensors of manufacturing tools. A server feeds a set of training time-series traces to a neural network configured to derive a model of the training time-series traces that minimizes reconstruction error of the training time-series traces. The server extracts a set of input time-series traces from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate. The server feeds the set of input time-series traces to the trained neural network to produce a set of output time series traces reconstructed based on the model. The server calculates a mean square error between a first input time series trace of the set of input time series traces and a corresponding first output time series trace of the set of output time-series traces.

Abstract: A method for generating a multi-modal video dataset with pixel-wise hand segmentation is disclosed. To address the challenges of conventional dataset creation, the method advantageously utilizes multi-modal image data that includes thermal images of the hands, which enables efficient pixel-wise hand segmentation of the image data. By using the thermal images, the method is not affected by fingertip and joint occlusions and does not require hand pose ground truth. Accordingly, the method can produce more accurate pixel-wise hand segmentation in an automated manner, with less human effort. The method can thus be utilized to generate a large multi-modal hand activity video dataset having hand segmentation labels, which is useful for training machine learning models, such as deep neural networks.

Abstract: A system for image classification is disclosed that includes a central system configured to provide high reliability image data processing and recognition and a plurality of endpoint systems, each configured to provide image data processing and recognition with a lower reliability than the central system and to generate probability data. A decision switch disposed at each of the plurality of endpoint systems is configured to receive the probability data and to determine whether to deny access, grant access or generate a referral message to the central system, wherein the referral message includes at least a set of image data generated at the endpoint system.

Abstract: Systems and methods are provided for distributed video storage and search over edge computing devices having a short-term memory and a long-term memory. The method may comprise caching a first portion of data on a first device. The method may further comprise determining, at a second device, whether the first device has the first portion of data. The determining may be based on whether the first piece of data satisfies a specified criterion. The method may further comprise sending the data, or a portion of the data, and/or a representation of the data from the first device to a third device.

Abstract: In implementations of resource-aware training for neural network, one or more computing devices of a system implement an architecture optimization module for monitoring parameter utilization while training a neural network. Dead neurons of the neural network are identified as having activation scales less than a threshold. Neurons with activation scales greater than or equal to the threshold are identified as survived neurons. The dead neurons are converted to reborn neurons by adding the dead neurons to layers of the neural network having the survived neurons. The reborn neurons are prevented from connecting to the survived neurons for training the reborn neurons.

Abstract: This invention concerns an automatic segmentation method of features, such as anatomical and pathological structures or instruments, which are visible in a 3D medical image of a subject, composed of voxels. Said method being characterised in that it consists in providing a global software means or arrangement combining N different convolutional neural networks or CNNs, with N?2, and having a structured geometry or architecture adapted and comparable to that of the image volume, and in analysing voxels forming said volume of the 3D image according to N different reconstruction axes or planes, each CNN being allocated to the analysis of the voxels belonging to one axis or plane.

Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Abstract: Embodiments of the present invention provide a method for identifying a body position of a detection object in medical imaging, a medical imaging system, and a computer-readable storage medium. The method comprises: receiving an image group by a trained deep learning network, the image group comprising a plurality of pre-scan images in a plurality of directions obtained by pre-scanning a detection object; and outputting body position information of the detection object by the deep learning network.