Abstract: An observation system includes a processor. The processor manages a plurality of images acquired in time series with respect to a biological sample which is being cultured, records a first comment and a first region of interest (RoI) in a storage device in association with the images based on user's instructions, and records a second comment and a second ROI in the storage device in association with the first comment based on user's instructions.

Abstract: One example provides a computer-implemented method for reading data stored as birefringence values in a storage medium. The method comprises acquiring an image of a voxel of the storage medium, applying a first low-pass filter with a first cutoff frequency to the image of the voxel to obtain a first background image, applying a second low-pass filter with a second cutoff frequency to the image of the voxel to obtain a second background image, the second cutoff frequency being different than the first cutoff frequency, determining an enhanced background image from the first background image and the second background image, determining birefringence values for the enhanced background image, determining birefringence values for the image of the voxel, and correcting the birefringence values for the image of the voxel based upon the birefringence values for the enhanced background image.

Abstract: Systems, methods, and apparatuses for reducing or eliminating reverberation artifacts in images are disclosed. Systems including one or more ultrasound probes are disclosed. Apparatuses for providing registration information for an ultrasound probe or probes at different positions are disclosed. A method of combining volume images acquired at different positions to reduce or eliminate reverberation artifacts is disclosed.

Abstract: A temporal propagation network (TPN) system learns the affinity matrix for video image processing tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The TPN system includes a guidance neural network model and a temporal propagation module and is trained for a particular computer vision task to propagate visual properties from a key-frame represented by dense data (color), to another frame that is represented by coarse data (grey-scale). The guidance neural network model generates an affinity matrix referred to as a global transformation matrix from task-specific data for the key-frame and the other frame. The temporal propagation module applies the global transformation matrix to the key-frame property data to produce propagated property data (color) for the other frame. For example, the TPN system may be used to colorize several frames of greyscale video using a single manually colorized key-frame.

Abstract: A temporal propagation network (TPN) system learns the affinity matrix for video image processing tasks. An affinity matrix is a generic matrix that defines the similarity of two points in space. The TPN system includes a guidance neural network model and a temporal propagation module and is trained for a particular computer vision task to propagate visual properties from a key-frame represented by dense data (color), to another frame that is represented by coarse data (grey-scale). The guidance neural network model generates an affinity matrix referred to as a global transformation matrix from task-specific data for the key-frame and the other frame. The temporal propagation module applies the global transformation matrix to the key-frame property data to produce propagated property data (color) for the other frame. For example, the TPN system may be used to colorize several frames of greyscale video using a single manually colorized key-frame.

Abstract: Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).

Abstract: The present disclosure provides a laser sensor-based map generation method. In an embodiment, the method includes: obtaining image data, the image data being acquired by a visual sensor; determining first point cloud data belonging to glass-like region in laser data based on the image data; adjusting a weight of the laser data according to the first point cloud data; fusing the first point cloud data and second point cloud data belonging to non-glass-like region in the laser data based on the adjusted weight of the laser data, to generate a map.

Abstract: A system and method of identifying potential areas for mineral extraction is disclosed. The proposed systems and methods describe an autonomous mineral discovery platform that leverages robotics, X-Ray Florescence (XRF) technology, image analytics, smart devices, and IoT enabled devices to perform comprehensive field surveying and exploratory sampling. For example, by implementation of remote navigation and control, as well as field data capture and real-time data transmission capabilities, this platform can be configured to automatically identify rock types and their surface features and perform elemental composition analysis of surface while on-site and remote from the operator site.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for intelligent content rating determination. Example methods include determining presence of a first feature in a first frame of a video using an object recognition algorithm, determining presence of a second feature in an audio file associated with the video using an audio processing algorithm, and determining presence of a third feature in a text file associated with the video using a natural language processing algorithm. Certain embodiments may include generating a predicted content rating for the video using a machine learning model, where the predicted content rating is based at least in part on the first feature, the second feature, and the third feature, and using feedback data for the predicted content rating to retrain the machine learning model.

Abstract: According to one or more embodiments of the present invention a computer-implemented method for fabricating a chip includes generating, using an aerial image generation system, a set of aerial images for a chip layout, the set of aerial images including an aerial image corresponding to each region from the chip layout. The method further includes automatically determining, using an artificial neural network, a feature vector for each aerial image from the set of aerial images. The method further includes clustering the aerial images using their corresponding feature vectors. The method further includes selecting, as test samples, a predetermined number of aerial images from each cluster. The method further includes performing a pattern coverage inspection of the chip layout using the aerial images that are selected as test samples.

Abstract: A method for determining objects of interest using a vehicular vision system includes providing at least one camera and providing a control having an image processor that processes image data captured by the camera. An object present in the field of view of the camera is detected via processing at the control of a first frame of captured image data. Hypotheses filtering and hypotheses merging may be utilized when processing additional frames of captured image data to discern the detected object being an object of interest from the detected object being not an object of interest. Hypotheses filtering includes tracking the detected object over multiple additional frames of captured image data, and hypotheses merging includes comparing outputs of hypotheses filtering for the multiple additional frames of captured image data. The detected object may be determined to be an object of interest responsive to results of hypotheses merging.

Abstract: A document scanner with the capability of landing a document by determining its location on the scanning bed, using intelligent methodologies for framing the document to determine the edges/boundaries of the document, processing the image using various techniques and methodologies to speed up the processing, so that the document feed track does not have to be slowed, and a unique binarization of the image to efficiently create different renderings of the scanned image.

Abstract: Region detection device includes acquirer that acquires a fingertip image of a fingertip including a nail, converter that transforms the fingertip image into a polar coordinate image in which one point inside the nail in the fingertip image is used as a pole, and detector that detects a region of the nail using the polar coordinate image.

Abstract: The disclosure herein generally relates to image processing, and, more particularly, to a method and system for impurity detection using multi-modal image processing. This system uses a combination of polarization data, and at least one of a depth data and an RGB image data to perform the impurity material detection. The system uses a graph fusion based approach while processing the captured images to detect presence of the impurity material, and accordingly alert the user.

Abstract: Techniques are disclosed for filling or otherwise replacing a target region of a primary image with a corresponding region of an auxiliary image. The filling or replacing can be done with an overlay (no subtractive process need be run on the primary image). Because the primary and auxiliary images may not be aligned, both geometric and photometric transformations are applied to the primary and/or auxiliary images. For instance, a geometric transformation of the auxiliary image is performed, to better align features of the auxiliary image with corresponding features of the primary image. Also, a photometric transformation of the auxiliary image is performed, to better match color of one or more pixels of the auxiliary image with color of corresponding one or more pixels of the primary image. The corresponding region of the transformed auxiliary image is then copied and overlaid on the target region of the primary image.

Abstract: Embodiments generally relate to a machine-implemented method of automatically adjusting the range of a depth data recording executed by at least one processing device. The method comprises determining, by the at least one processing device, at least one position of a subject to be recorded; determining, by the at least one processing device, at least one spatial range based on the position of the subject; receiving depth information; and constructing, by the at least one processing device, a depth data recording based on the received depth information limited by the at least one spatial range.

Abstract: An information detection method includes: determining key point information in a target identification from a target image based on a preset deep learning algorithm; obtaining an image of the target identification from the target image according to the key point information; and determining information of a preset field from the image of the target identification according to the image of the target identification and a preset identification template matching the target identification.

Abstract: Methods and apparatus to capture images are disclosed. An example device disclosed herein includes an image sensor to observe a first image of a face and a logic circuit. The logic circuit is to determine whether the face in the first image is in a first orientation with respect to the image sensor. When the face in the first image is in the first orientation, the logic circuit is trigger the image sensor to capture a second image of the face, and perform a facial recognition process on the second image, but not on the first image.

Abstract: Systems and methods for full motion video search are provided. In one aspect, a method includes receiving one or more search terms. The search terms include one or more of a characterization of the amount of man-made features in a video image and a characterization of the amount of natural features in the video image. The method further includes searching a full motion video database based on the one or more search terms.

Abstract: A monitoring apparatus obtains image data captured by an image capturing apparatus, generates a background image based on the image data, performs detection processing of a moving object using the image data and the background image, and sets a wait time from a start of generation of the background image to a start of the detection processing based on a condition concerning the detection processing.