Abstract: Provided are a vibration object monitoring method and apparatus, a computer device, and a storage medium. The method includes: in response to detecting that a vibration object exists in a monitoring video picture for a target monitoring region, a vibration object region in the monitoring video picture is determined, where the vibration object region is a region where the vibration object is located in the monitoring video picture; displacement information of a key point of the vibration object in the vibration object region is recorded; vibration information of the vibration object in the monitoring video picture is determined based on the displacement information; and a vibration object monitoring result for the target monitoring region is generated according to the vibration information. The abnormal vibration monitoring can be performed on the vibration object in the target monitoring region in time according to this method.

Abstract: Provided is a binarization method for CT sectional image of fiber package containing artifacts, which includes: performing brightness adjustment on the source image obtained after converting of the HSV image model by using a composite tangent function; creating a planar morphological structural element having a morphology similar to that of a target object to obtain a background image without the target object; obtaining a second intermediate image by a subtraction operation of the first intermediate image and the background image; improving an image contrast of the second intermediate image again to obtain a third intermediate image; and binarizing the third intermediate image by using a local adaptive threshold binarization algorithm and removing a background noise to obtain a final binarized image. The binarization method can improve the uneven brightness of the image under complex illumination, alleviate the artifacts, and strip similar objects from the background with similar gray scales.

Abstract: Disclosed is a cell classification method, to be executed by an analyzer, for classifying cells contained in a specimen, including: preparing a first measurement sample by treating a specimen under a first preparation condition; obtaining a first signal from the prepared first measurement sample; classifying, by using the first signal, cells contained in the first measurement sample; preparing a second measurement sample by treating the specimen under a second preparation condition different from the first preparation condition; obtaining a second signal from the prepared second measurement sample; classifying, by using the second signal, cells contained in the second measurement sample; and comparing a result of the cell classification performed by using the first signal and a result of the cell classification performed by using the second signal, with each other, and outputting an analysis result including a number of cells on the basis of a result of the comparison.

Abstract: A method of performing air pollution estimation is provided. The method is to be implemented using a processor of a computer device and includes: generating a spectral image based on an original color image of an environment under test using a spectral transformation matrix; supplying the spectral image as an input into an estimating model for air pollution estimation; and obtaining an estimation result from the estimating model indicating a degree of air pollution of the environment under test.

Abstract: Systems and methods for determining bacterial load in targets are disclosed. An autofluorescence detection and collection device includes a light source configured to illuminate a target with excitation light causing at least one biomarker in the illuminated target to fluoresce. Bacterial autofluorescence data regarding the target is collected and analyzed to determine bacterial load the target. The autofluorescence data may be analyzed using pixel intensity.

Abstract: A method and system for reconstructing magnetic resonance (MR) images, the method including the steps of: receiving an under-sampled MR image, the under-sampled MR image being transformed from under-sampled k-spaced data; classifying intensity of each pixel in the under-sampled MR image to one of a plurality of predetermined quantized values of intensity by using a neural network; and generating a reconstructed MR image based on the classified quantized value of the intensity for each pixel in the under-sampled MR image.

Abstract: An adaptor for configuring a mobile communication device for tissue imaging is disclosed. The adaptor includes a housing configured to be removably coupled to a mobile communication device, at least one excitation light source for fluorescent imaging, a white light source for white light imaging, and a power source for powering the adaptor. The excitation light source is configured to emit light in one of ultraviolet, visible, near-infrared, and infrared ranges.

Abstract: A method for processing a 360-degree image includes determining a processing order of each of a plurality of segments constituting a 360-degree image, based on positions of the segments and parameters of a camera by which the 360-degree image is captured, obtaining distortion correction information according to distortion in the 360-degree image predicted based on parameters of an optical device via which at least a partial region of the 360-degree image is projected, and processing data of the at least a partial region of the 360-degree image according to the determined processing order and the obtained distortion correction information.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to detecting text in images. In some embodiments, a system for detecting text in images comprises a database configured to store images and a control circuit configured to retrieve an image, generate, based on the image, a collection of augmented images, detect characters in each of the augmented images, generate bounding boxes for the characters in each of augmented images, recognize the characters in each of the augmented images, select, based on the recognition of the characters in each of the augmented images, candidate characters, wherein the candidate characters are selected based on consistency of the recognition of the characters in each of the augmented images, detect, for the image, a color associated with the characters, and store, in the database, the image, the candidate characters, and the color associated with the characters.

Abstract: A controller (11) for a vehicle, the vehicle comprising an imaging device for generating image data and the imaging device further comprising an imaging accelerometer (24) for generating imaging accelerometer data for determining the orientation of the imaging device relative to the vehicle, the controller (11) comprising: an input for receiving a signal indicative of the imaging accelerometer data and the generated image data; a processor arranged to identify alignment artefacts in the received image data in dependence on the received imaging device imaging accelerometer data; and an output for outputting an error signal in dependence on the identified alignment artefacts.

Abstract: An authentication mark system and method are provided for authentication of an object having indicia associated therewith. The authentication mark system includes an inkjet printer configured to propel microscopic ink drops onto the object to form a pattern of ink on the object. A microscope camera has a lens directable at the pattern of ink on the object. The microscope camera records an enlarged image of the pattern of ink on the object. A processor is operatively connected to the microscope camera and is configured to transmit the enlarged image of the pattern of ink and the indicia associated with the object to a database for secure storage.

Abstract: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.

Abstract: An ophthalmologic information processing apparatus according to the embodiments includes a search unit, a correction unit, and a display control unit. The search unit is configured to search for filter information for correcting an aberration in complex OCT data of a subject's eye so that a quality of the complex OCT data becomes a predetermined level. The correction unit is configured to correct the aberration in the complex OCT data based on the filter information searched by the search unit. The display control unit is configured to display, on a display means, aberration information on a pupil surface of the subject's eye corresponding to the filter information searched by the search unit.

Abstract: Systems, apparatuses, and methods for converting data to a tiling format when implementing convolutional neural networks are disclosed. A system includes at least a memory, a cache, a processor, and a plurality of compute units. The memory stores a first buffer and a second buffer in a linear format, where the first buffer stores convolutional filter data and the second buffer stores image data. The processor converts the first and second buffers from the linear format to third and fourth buffers, respectively, in a tiling format. The plurality of compute units load the tiling-formatted data from the third and fourth buffers in memory to the cache and then perform a convolutional filter operation on the tiling-formatted data. The system generates a classification of a first dataset based on a result of the convolutional filter operation.

Abstract: The invention tracks and automatically evaluates plant traits of “individuals of plant canopy” in a growth process of a germination period (from sowing until immediately prior to primary seedling culture), at a plant factory with artificial lighting. Specifically, a two-dimensional distribution of the plant traits is calculated by non-destructively and continuously measuring plant trait information in the growth process of the germination period, on the basis of image information 2a, environmental factor information 2b, genetic characteristic information 2c, and management information 2d.

Abstract: Fiducial markers are printed patterns detected by algorithms in imagery from image sensors for applications such as automated processes and augmented reality graphics. The present invention sets forth extensions and improvements to detection technology to achieve improved performance, and discloses applications of fiducial markers including multi-camera systems, remote control devices, augmented reality applications for mobile devices, helmet tracking, and weather stations.

Abstract: Embodiments described herein provide a surgical duration estimation system for continuously predicting real-time remaining surgical duration (RSD) of a live surgical session of a given surgical procedure based on a real-time endoscope video of the live surgical session. In one aspect, the process receives a current frame of the endoscope video at a current time of the live surgical session, wherein the current time is among a sequence of prediction time points for making continuous RSD predictions during the live surgical session. The process next randomly samples N?1 additional frames of the endoscope video corresponding to the elapsed portion of the live surgical session between the beginning of the endoscope video corresponding to the beginning of the live surgical session and the current frame corresponding to the current time. The process then combines the N?1 randomly sampled frames and the current frame in the temporal order to obtain a set of N frames.

Abstract: The disclosed techniques are focused on processes for encoding an enhanced image with non-image data. Notably, the “non-image data” is distinct from “image data” in that the image data defines display characteristics of an image (e.g., display properties of a pixel) while the non-image data is unrestricted and can describe any data, even data different than display characteristics. An image is accessed, where the image includes at least one pixel that is associated with at least one color channel. Non-image data is encoded into the color channel. An index, which maps where the non-image data has been encoded in the color channel of the pixel, is generated or modified. As a result of encoding the non-image data into the color channel, an enhanced image is generated.

Abstract: This invention applies dynamic weighting between a point-to-plane and point-to-edge metric on a per-edge basis in an acquired image using a vision system. This allows an applied ICP technique to be significantly more robust to a variety of object geometries and/or occlusions. A system and method herein provides an energy function that is minimized to generate candidate 3D poses for use in alignment of runtime 3D image data of an object with model 3D image data. Since normals are much more accurate than edges, the use of normal is desirable when possible. However, in some use cases, such as a plane, edges provide information in relative directions the normals do not. Hence the system and method defines a “normal information matrix”, which represents the directions in which sufficient information is present. Performing (e.g.) a principal component analysis (PCA) on this matrix provides a basis for the available information.

Abstract: A method for correcting abnormal point cloud is disclosed. Firstly, receiving a Primitive Point Cloud Data set by an operation unit for dividing a point cloud array into a plurality of sub-point cloud sets and obtaining a plurality of corresponding distribution feature data according to an original vector data of the Primitive Point Cloud Data set. Furthermore, recognizing the sub-point cloud sets according to the corresponding distribution feature data for correcting recognized abnormal point cloud. Thus, when the point cloud array is rendered to a corresponding image, the color defect of the point cloud array will be improved or decreased for obtaining lossless of the corresponding image.