Abstract: A medical image processing method includes: obtaining a biological tissue image including a biological tissue, recognizing, in the biological tissue image, a first region of a lesion object in the biological tissue; recognizing a lesion attribute matching the lesion object; dividing an image region of the biological tissue in the biological tissue image into a plurality of quadrant regions; obtaining target quadrant position information of a quadrant region in which the first region is located; and generating medical service data according to the target quadrant position information and the lesion attribute.

Abstract: Provided is a method, performed by a computer, for correcting a brain image by using a brain structure. The method comprises: a step for obtaining a head image including the brain of a subject; a step for dividing the head image into a plurality of regions on the basis of the brain structure; and a step for performing a correction on the plurality of regions by using a layer arrangement condition of the brain.

Abstract: An intelligent analysis system applied to ethology of various kinds of high-density minimal polypides is provided, including an operating platform subsystem and an analysis subsystem; the operating platform subsystem is used for providing an activity medium capable of obtaining behaviors of the minimal polypides and shooting the behaviors of the minimal polypides; the behavior collecting module in the analysis subsystem is used for obtaining an ethology video of the high-density minimal polypides through the operating platform subsystem; the analysis modeling module is used for obtaining ethology big data of various kinds of high-density minimal insects according to the ethology video, and establishing an ethology model; the simulation predicting module is used for dynamically predicting comprehensive control strategies and control effects of minimal pests in a field ecosystem according to the ethology model; the visual demonstrating module is used for displaying the above behavior data.

Abstract: Disclosed herein are image-based object recognition method and system by and in which a learning server performs image-based object recognition based on the learning of environment variable data. The image-based object recognition method includes: receiving an image acquired through at least one camera, and segmenting the image on a per-frame basis; primarily learning labeling results for one or more objects in the image segmented on a per-frame basis; performing primary reasoning by performing object detection in the image through a model obtained as a result of the primary learning; performing data labeling based on the results of the primary reasoning, and performing secondary learning with weights allocated to respective boxes obtained by the primary reasoning and estimated as object regions; and finally detecting one or more objects in the image through a model generated as a result of the secondary learning.

Abstract: The disclosure provides a three-dimensional (3D) image classification method and apparatus, a device, and a storage medium. The method includes: obtaining a 3D image, the 3D image including first-dimensional image information, second-dimensional image information, and third-dimensional image information; extracting a first image feature corresponding to planar image information from the 3D image; extracting a second image feature corresponding to the third-dimensional image information from the 3D image; fusing the first image feature and the second image feature, to obtain a fused image feature corresponding to the 3D image; and determining a classification result corresponding to the 3D image according to the fused image feature corresponding to the 3D image.

Abstract: A pixel lamp of a motor vehicle emanates light based on a control signal that represents an image sequence. A respective individual image from the image sequence corresponds to a respective light distribution currently needing to be provided by the pixel lamp. The route is captured by use of a vehicle camera, and the camera data are evaluated and the control signal is provided based on an ascertained advisory route accoutrement. The image sequence is provided at an image sequence frequency of greater than 24 Hz and includes at least one reference image for uniformly lighting the route, the vehicle camera is synchronized to the reference image, the advisory route accoutrement and information data associated with the advisory route accoutrement are ascertained from the provided camera data, the associated information data are compared with data of a database, and the control signal is ascertained based on the comparison.

Abstract: Embodiments may: select a set of training images; extract a first set of features from each training image of the set of training images to generate a first feature tensor for each training image; extract a second set of features from each training image to generate a second feature tensor for each training image; reduce a dimensionality of each first feature tensor to generate a first modified feature tensor for each training image; reduce a dimensionality of each second feature tensor to generate a second modified feature tensor for each training image; construct a first generative model representing the first set of features and a second generative model representing the second set of features of the set of training images; identify a first candidate image; and apply a regression algorithm to the first candidate image and each of the first generative model and the second generative model to determine whether the first candidate image is similar to the set of training images.

Abstract: A method for controlling a robotic device includes observing a first object associated with an object type at one or more first locations in an environment over a period of time prior to a current time. The method also includes generating a probability distribution associated with the one or more first locations based on observing the first object over the period of time. The method further includes observing, at the current time, a second object associated with the object type at a second location in the environment. The method still further includes determining a probability of the second object being at the second location based on observing the second object at the second location. The probability is based on the probability distribution associated with the one or more first locations. The method also includes controlling the robotic device to perform an action based on the probability being less than a threshold.

Abstract: A computer device, obtains a mammographic image of a unilateral breast. The mammographic image includes a cranial-caudal (CC)-position mammographic image and a mediolateral-oblique (MLO)-position mammographic image. The computer device invokes a breast detection model to perform a prediction of a condition of the unilateral breast according to the CC-position mammographic image and the MLO-position mammographic image. The device obtains a prediction result of the unilateral breast, and generates and outputs a detection report that includes the prediction result.

Abstract: Training an estimation model using soft labels includes receiving an image. It further includes generating a continuous target map corresponding to the image that includes hard labels and soft labels. A model is trained using the corresponding continuous target map.

Abstract: Apparatus for powder-free intra-oral 3D imaging by using a projected texture pattern. A projector projects a random texture pattern of light to teeth to be imaged, and a digital image sensor receives the projected texture pattern from the teeth. The reflected pattern of light reflects and scatters from the teeth. The texture pattern can be a grid having clusters of bright and dark blocks in a pseudo-random arrangement and can provide for powder-free intra-oral 3D imaging by using the pattern to optically simulate powder applied to the teeth. Polarizers can be used in the optical path to transmit the directly reflected light to the image sensor and suppress or discard some of the unwanted scattered light.

Abstract: The system obtains a first digital image and a hash associated with the first digital image, where the hash associated with the first digital image is a hash of analog values recorded by a sensor involved in generating the first digital image. Based on the hash, the system retrieves the analog values from a database. Based on the analog values, the system reconstructs a second digital image. The system determines whether the first digital image and the second digital image are substantially the same. Upon determining that the first digital image and the second digital image are substantially the same, the system provides a first notification that the first digital image is the same as the second digital image, otherwise the system provides a second notification that the first digital image is different from the second digital image.

Abstract: In one embodiment, a method includes capturing, by a camera of an image capturing module, a first image of a target. The image capturing module and a drum are attached to a fixture and the target is attached to the drum. The method also includes determining a number of lateral pixels in a lateral pitch distance of the image of the target, determining a lateral object pixel size based on the number of lateral pixels, and determining a drum encoder rate based on the lateral object pixel size. The drum encoder rate is programmed into a drum encoder. The method further includes capturing, by the camera, a second image of the target while the target is rotated about an axis of the drum, determining a number of longitudinal pixels in a longitudinal pitch distance of the second image, and comparing the number of lateral pixels to the number of longitudinal pixels.

Abstract: An information processing device acquires an image captured by a transmission electron microscope. The information processing device, for each partial region in the image, calculates a variation in pixel values of pixels included in the partial region. The information processing device, for each partial region in the image, determines a degree of crystallinity of the partial region based on the calculated variation in the pixel values of the partial region.

Abstract: A left object detection device for detecting an object left behind in a monitoring target area includes an acquisition section which acquires an image of the area from an image pickup device for picking up an image of the monitoring target area, a processing section, and an output section which outputs in accordance with a result of the processing section. Upon detection of the object which has been kept static for a given time period as an article from the image acquired by the acquisition section, the processing section starts a timer for counting time from detection of the article, determines whether or not a person who owned the article matches a person in the image acquired by the acquisition section, stops the timer upon determination that they match, and does not stop the timer upon determination that they do not match. The processing section determines whether or not the timer counts in excess of a predetermined time period.

Abstract: A looking away determination device includes: a determination unit that determines that a driver is in a looking away state when a proportion of an image in which a face of the driver is not detected with respect to a plurality of images obtained by imaging the driver is equal to or greater than a first predetermined value, the first predetermined value being determined based on driving state information that indicates a state during driving by the driver.

Abstract: Provided is a determination method capable of non-destructively and simply determining a state of a sphere that is an aggregate of a plurality of cells. A phase difference image of a sphere that is an aggregate of a plurality of cells is generated from a hologram obtained by imaging the sphere, and a state of the sphere is determined on the basis of the phase difference image and a shape index value corresponding to a shape of the sphere.

Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.

Abstract: A system for characterizing a specimen is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images a specimen, and a controller communicatively coupled to the characterization sub-system. The controller may be configured to: receive training images of one or more features of a specimen from the characterization sub-system; receive training three-dimensional (3D) design images corresponding to the one or more features of the specimen; generate a deep learning predictive model based on the training images and the training 3D design images; receive product 3D design images of one or more features of a specimen; generate simulated images of the one or more features of the specimen based on the product 3D design images with the deep learning predictive model; and determine one or more characteristics of the specimen based on the one or more simulated images.

Abstract: Provided is a traffic sign recognition method based on a lightweight neural network, which including: a lightweight neural network model is constructed for training and pruning to obtain a lightweight neural network model; the lightweight neural network model comprises a convolution feature extraction part and a classifier part; the convolution feature extraction part includes one layer of conventional 3×3 convolution and 16 layers of separable asymmetric convolution. The classifier part includes three layers of separable full connection modules.