Abstract: A lamp and a method for detecting a user's sitting posture are provided. The lamp is adapted to detect the user's sitting posture. The lamp comprises a lamp bracket, an image capturing unit and a processing unit. The image capturing unit is disposed on the lamp bracket and configured to capture a target image towards a desktop. The processing unit is coupled to the image capturing unit, wherein in response to that a head feature exists in the target image, the processing unit is configured to determine a reference information according to the head feature and to determine whether the reference information meets a poor sitting posture condition.

Abstract: This invention provides a system and method for finding patterns in images that incorporates neural net classifiers. A pattern finding tool is coupled with a classifier that can be run before or after the tool to have labeled pattern results with sub-pixel accuracy. In the case of a pattern finding tool that can detect multiple templates, its performance is improved when a neural net classifier informs the pattern finding tool to work only on a subset of the originally trained templates. Similarly, in the case of a pattern finding tool that initially detects a pattern, a neural network classifier can then determine whether it has found the correct pattern. The neural network can also reconstruct/clean-up an imaged shape, and/or to eliminate pixels less relevant to the shape of interest, therefore reducing the search time, as well significantly increasing the chance of lock on the correct shapes.

Abstract: Calculating, for each frame of a plurality of frames, a corresponding quality value; calculating, for each frame of the plurality of frames, based on one or more visual attributes of a frame, a weight for the corresponding quality value of the frame; calculating an aggregate quality value for the plurality of frames based on the weight and the corresponding quality value for each frame of the plurality of frames; and providing an assessment of the plurality of frames based on the aggregate quality value for the plurality of frames.

Abstract: Physical object boundary detection techniques and systems are described. In one example, an augmented reality module generates three dimensional point cloud data. This data describes depths at respective points within a physical environment that includes the physical object. A physical object boundary detection module is then employed to filter the point cloud data by removing points that correspond to a ground plane. The module then performs a nearest neighbor search to locate a subset of the points within the filtered point cloud data that correspond to the physical object. Based on this subset, the module projects the subset of points onto the ground plane to generate a two-dimensional boundary. The two-dimensional boundary is then extruded based on a height determined from a point having a maximum distance from the ground plane from the filtered cloud point data.

Abstract: The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method includes a processing device receiving image data having one or more image frames indicative of a state of a meal preparation area. The method further includes the processing device determining, based on the image data, one of a meal preparation item or a meal preparation action associated with the state of the meal preparation area. The method further includes the processing device determining, based on the image data, a performance metric associated with the meal preparation item or the meal preparation item or the meal preparation action. The method further includes the processing device causing a notification corresponding to the performance metric to be displayed on a graphical user interface (GUI).

Abstract: Systems and methods for scalable comparisons between two pixel maps are provided. In an embodiment, an agricultural intelligence computer system generates pixel maps from non-image data by transforming a plurality of values and location values into pixel values and pixel locations. The non-image data may include data relating to a particular agricultural field, such as nutrient content in the soil, pH values, soil moisture, elevation, temperature, and/or measured crop yields. The agricultural intelligence computer system converts each pixel map into a vector of values. The agricultural intelligence computer system also generates a matrix of metric coefficients where each value in the matrix of metric coefficients is computed using a spatial distance between to pixel locations in one of the pixel maps. Using the vectors of values and the matrix of metric coefficients, the agricultural intelligence computer system generates a difference metric identifying a difference between the two pixel maps.

Abstract: An information processing apparatus (2000) detects an abnormal region (30) from a predetermined range (16) of a video frame (14). The information processing apparatus (2000) determines whether a predetermined condition is satisfied in a case where the abnormal region (30) is detected from the predetermined range (16) of a certain video frame (14) and the abnormal region (30) is not detected from the predetermined range (16) of a predetermined video frame (14) generated later than the video frame (14). In a case where the predetermined condition is not satisfied, the information processing apparatus (2000) performs a predetermined notification.

Abstract: A method includes generating a first warped image based on a pose and a depth estimated from a current image and a previous image in a sequence of images captured by a camera of the agent. The method also includes estimating a motion of dynamic object between the previous image and the target image. The method further includes generating a second warped image from the first warped image based on the estimated motion. The method still further includes controlling an action of an agent based on the second warped image.

Abstract: A system and method is disclosed for classifying natural language sentences by employing external knowledge to assist in constructing a knowledge base of sentences with a target meaning. The disclosed system and method provide a general sentence classification framework applicable for a knowledge-oriented domain (e.g., domain-specific knowledge). The system and method may be implemented in an intelligent automotive aftermarket assistance tool to assist with the identification of sentences describing specific problems and solutions for car repairs. In addition to the domain adaptability, the system and method is language-independent and could be applicable to any natural written language.

Abstract: Systems, devices, and methods for training a neural network and performing image interest point detection and description using the neural network. The neural network may include an interest point detector subnetwork and a descriptor subnetwork. An optical device may include at least one camera for capturing a first image and a second image. A first set of interest points and a first descriptor may be calculated using the neural network based on the first image, and a second set of interest points and a second descriptor may be calculated using the neural network based on the second image. A homography between the first image and the second image may be determined based on the first and second sets of interest points and the first and second descriptors. The optical device may adjust virtual image light being projected onto an eyepiece based on the homography.

Abstract: Disclosed are a video stitching method and a video stitching device. The video stitching method is applicable for stitching a first video and a second video, and includes: performing feature extraction, feature matching and screening on a first target frame of the first video and a second target frame of the second video, so as to obtain a first feature point pair set; performing forward tracking on the first target frame and the second target frame, so as to obtain a second feature point pair set; performing backward tracking on the first target frame and the second target frame, so as to obtain a third feature point pair set; and calculating a geometric transformation relationship between the first target frame and the second target frame according to a union of the first feature point pair set, the second feature point pair set and the third feature point pair set.

Abstract: Apparatus and methods for applying motion blur to overcapture content. In one embodiment, the motion blur is applied by selecting a number of frames of the captured image content for application of motion blur; selecting a plurality of pixel locations within the number of frames of the captured image content for the application of motion blur; applying motion blur to the captured image content in accordance with the selected number of frames and the selected plurality of pixel locations; and outputting the captured image content with the applied motion blur. In some implementations, motion blur is applied via implementation of a virtualized neutral density filter. Computerized devices and computer-readable apparatus for the application of motion blur are also disclosed.

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

Abstract: The present invention relates to a scene descriptor generation system, including an image sensor including a plurality of pixels, successively supplying S binary image planes, each including a single binary value per pixel; and a processing circuit configured to, for each binary image plane supplied by the sensor (100), to implement the successive steps of: a) calculating N convolutions of the binary image plane with respectively N distinct convolution kernels, to provide N convolved images; b) generating at least one meta-image from the N convolved images supplied at step a); and c) for each of the N convolved images supplied at step a), generating a binary descriptor from said convolved image and from said at least one meta-image.

Abstract: A position where a user is located can be estimated easily and quickly. When a hold detection unit 122 detects that the user holds a mobile terminal 100 by hand to use the mobile terminal 100 that is held by at least one hand of the user when used by the user and has a camera 130 on the surface facing the ceiling when used by the user, the estimation unit 240 estimates a position where a ceiling is located as the position where the user is located by using an image of the ceiling captured by the camera 130.

Abstract: A medical information processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data representing a blood vessel of a subject, extract a blood vessel shape from the medical image data, determine degree of meandering in each of regions from the extracted blood vessel shape, specify a deformed region in which the degree of meandering changes due to insertion of a device into the blood vessel on the basis of the degree of meandering, and output the deformed region in the blood vessel.

Abstract: An information processing apparatus (2000) detects an estimation position (24), which is a position where a target object is estimated to be present, from a captured image (20) generated by a camera (10). The target object is a belonging of a person. The information processing apparatus (2000) detects a plurality of parts (22) constituting a body of a person included in the captured image (20). For example, the parts (22) are joints. The information processing apparatus (2000) detects the estimation position (24) of the target object in the captured image (20) using information relating to a position of each of a plurality of the detected parts (22).

Abstract: Various systems and methods for for clustering facial images in, for example, surveillance systems.

Abstract: The present disclosure relates to methods and systems to generate a modified feature map specific to a client. A template feature map may be modified based on usage data associated with a client. The template feature map may represent a visual representation of a plurality of features provided by an operator, each feature associated with a plurality of instructions to be processed for the client. The usage data may be compared with each feature to determine whether any feature is associated and/or utilized by the client. Based on determining whether the usage data indicates that any feature is associated and/or utilized by the client, the template feature map may be modified to perform an action to the template feature map indicating that a feature is associated and/or utilized by the client. A modified template feature map may be generated that is specific for a client.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing data generated by a sensing system that rotationally senses an environment. In one aspect, a method comprises partitioning a predetermined period of time into a plurality of sub-periods, wherein the predetermined period of time is a period of time for which data generated by the sensing system constitutes a complete rotational sensing of the environment; for each sub-period: receiving current data generated by the sensing system during the sub-period and characterizing a respective partial scene of the environment; processing the current data using an object detection neural network to generate a current object detection output that is specific to the respective partial scene of the environment.