Abstract: A computer device segments a first sample region to obtain a candidate image region set that includes a plurality of candidate image regions, For each of the candidate image regions, the device obtains a first relationship degree corresponding to each candidate image region and obtains a second relationship degree corresponding to the candidate image region. The device obtains a relationship degree change value based on the first relationship degree and the second relationship degree. The device selects, from the plurality of candidate image regions, a first candidate image region as a target image region in accordance with a determination that the first candidate image region satisfies a condition in the relationship degree change value. The device performs model training based on the target image region to obtain a target detection model.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.

Abstract: An apparatus for generating a map includes a processor configured to detect a stop line of an intersection from a bird's-eye view image, detect from the bird's-eye view image an intersection area including an intersection and roads connected to the intersection, detect at least either one of an entry lane for entering the intersection and an exit lane for exiting the intersection in a road from which the stop line is extracted, of the roads, based on the ratio of the length of the stop line to the width of the road, and generate a lane network representing a connection relationship between lanes in the intersection so as to connect, for each of the roads, the entry lane of the road to the exit lane of another of the roads to which a vehicle is allowed to proceed from the entry lane of the road.

Abstract: A surgical instrument navigation system and method of use is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient, wherein the surgical instrument, as provided, may be a steerable surgical catheter with a biopsy device and/or a surgical catheter with a side-exiting medical instrument, among others. Additionally, systems, methods and devices are provided for forming a respiratory-gated point cloud of a patient's respiratory system and for placing a localization element in an organ of a patient.

Abstract: In order to extract a foreground area more appropriately, an image processing apparatus for extracting a foreground area from an inputted image includes an image input unit that sets a first area and a second area different from the first area in the inputted image, a first extraction unit that extracts a foreground area from the first area, and a second extraction unit that extracts a foreground area from the second area by using an extraction method different from an extraction method used by the first extraction unit.

Abstract: There is achieved short-time measurement of a measurement target such as a farm field with high accuracy. Therefore, processing is performed on a sampling image obtained by the imaging of a part of a range as a measurement target with a multi spectrum camera that performs imaging to capture images in a plurality of different wavelength bands. Then, arithmetic processing is performed on the sampling image as a processing target to generate a calculation result image serving as evaluation information for the entire measurement target.

Abstract: Apparatus detecting driving incapability state of a driver includes: a head detection portion that successively detects a head portion, which is higher than a neck of the driver, based on an image of a driver's seat captured by an imaging device mounted in a vehicle; and an out-of-frame state detection portion that detects that the driver is incapable of driving, when the head portion detected by the head detection portion is out of a predetermined scope in the image during travelling of the vehicle.

Abstract: Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

Abstract: A method for determining a type of parking space for a motor vehicle includes detecting a target, evaluating an orientation of the target relative to a road, and determining a type of parking space on the basis of the evaluated orientation. The detecting the target is implemented by a frontal camera of the vehicle.

Abstract: Examples of various method and systems are provided for information extraction from scene information. 2D image information can be generated from 2D images of the scene that are overlapping at least part of one or more object(s). The 2D image information can be combined with 3D information about the scene incorporating at least part of the object(s) to generate projective geometry information. Clustered 3D information associated with the object(s) can be generated by partitioning and grouping 3D data points present in the 3D information. The clustered 3D information can be used to provide, e.g., measurement information, dimensions, geometric information, and/or topological information about the object(s). Segmented 2D information can also be generated from the 2D image information. Validated 2D and 3D information can be produced by cross-referencing between the projective geometry information, clustered 3D information, and/or segmented 2D image information, and used to label the object(s) in the scene.

Abstract: A method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; obtaining from the data a first prediction residual of a first component from among a plurality of components of an attribute associated with the point cloud; reconstructing the first prediction residual; determining a predicted second prediction residual based on the reconstructed first prediction residual and at least one model parameter; obtaining a second prediction residual of a second component from among the plurality of components based on the predicted second prediction residual; reconstructing the second prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first prediction residual and the reconstructed second prediction residual.

Abstract: A processor-implemented neural network method includes: determining a respective probability density function (PDF) of normalizing a statistical distribution of parameter values, for each channel of each of a plurality of feature maps of a pre-trained neural network; determining, for each channel, a corresponding first quantization range for performing quantization of corresponding parameter values, based on a quantization error and a quantization noise of the respective determined PDF; determining, for each channel, a corresponding second quantization range, based on a signal-to-quantization noise ratio (SQNR) of the respective determined PDF; correcting, for each channel, the corresponding first quantization range based on the corresponding second quantization range; and generating a quantized neural network, based on the corrected first quantization range corresponding for each channel.

Abstract: A position estimation system includes: a reference image database configured to store a reference image and information indicating a position in a state in which they are associated with each other; an acquisition unit configured to acquire an image for estimation which is a position estimation target; an estimation unit which performs comparison of the reference image with the image for estimation, estimate the position related to the image for estimation from the information indicating the position associated with the reference image, and calculate a reliability of the estimated position; and an update unit configured to update the reference image using the image for estimation on the basis of the reliability.

Abstract: Systems and methods for performing visual odometry more rapidly. Pairs of representations from sensor data (such as images from one or more cameras) are selected, and features common to both representations of the pair are identified. Portions of bundle adjustment matrices that correspond to the pair are updated using the common features. These updates are maintained in register memory until all portions of the matrices that correspond to the pair are updated. By selecting only common features of one particular pair of representations, updated matrix values may be kept in registers. Accordingly, matrix updates for each common feature may be collectively saved with a single write of the registers to other memory. In this manner, fewer write operations are performed from register memory to other memory, thus reducing the time required to update bundle adjustment matrices and thus speeding the bundle adjustment process.

Abstract: A method for estimating tightly enclosing bounding boxes by a computing system includes: controlling a scanning system including one or more depth cameras to capture visual information of the scene including one or more objects; detecting the one or more objects of the scene based on the visual information; singulating each the one or more objects from the frame of the scene to generate one or more 3D models corresponding to the one or more objects, the one or more 3D models including a partial 3D model of a corresponding one of the one or more objects; extrapolating a more complete 3D model of the corresponding one of the one or more objects based on the partial 3D model; and estimating a tightly enclosing bounding box of the corresponding one of the one or more objects based on the more complete 3D model.

Abstract: Disclosed herein are system, method, and computer program product embodiments for optimizing the determination of a phenotypic impact of a molecular variant identified in molecular tests, samples, or reports of subjects by way of regularly incorporating, updating, monitoring, validating, selecting, and auditing the best-performing evidence models for the interpretation of molecular variants across a plurality of evidence classes.

Abstract: Techniques are described for automated operations related to analyzing visual data from images captured in rooms of a building and optionally additional captured data about the rooms to assess room layout and other usability information for the building's rooms and optionally for the overall building, and to subsequently using the assessed usability information in one or more further automated manners, such as to improve navigation of the building. The automated operations may include identifying one or more objects in each of the rooms to assess, evaluating one or more target attributes of each object, assessing usability of each object using its target attributes' evaluations and each room using its objects' assessment and other room information with respect to an indicated purpose, and combining the assessments of multiple rooms in a building and other building information to assess usability of the building with respect to its indicated purpose.

Abstract: There is provided a 3D image generation method and apparatus. A 3D image generation method according to an embodiment includes outputting a signal to an object from at least one point, receiving the signal reflected by the object at one or more points, and generating a 3D image of the object based on a temporal feature and a spectral feature of the signal reflected by the object.

Abstract: Methods, systems and computer program products for recognition of partially digested medications are provided. Aspects include receiving an image depicting regurgitated stomach contents of an individual and obtaining medical data regarding the individual. Aspects also include analyzing the image, by a recognition model, to identify one or more pills depicted in the image and a percentage of the one or more pills that has not been digested. Aspects further include performing an action based on the medical data, the identification of the one or more pill and the percentage of the one or more pills that has not been digested.

Abstract: A diagnostic tool for deep learning similarity models and image classifiers provides valuable insight into neural network decision-making. A disclosed solution generates a saliency map by: receiving a baseline image and a test image; determining, with a convolutional neural network (CNN), a first similarity between the baseline image and the test image; based on at least determining the first similarity, determining, for the test image, a first activation map for at least one CNN layer; based on at least determining the first similarity, determining, for the test image, a first gradient map for the at least one CNN layer; and generating a first saliency map as an element-wise function of the first activation map and the first gradient map. Some examples further determine a region of interest (ROI) in the first saliency map, cropping the test image to an area corresponding to the ROI, and determine a refined similarity score.