Scene text information extraction of desired text information from an image can be performed and managed. An information management component (IMC) can determine an anchor word based on analysis of an image. To facilitate determining desired text information in the image, WIC can re-orient the image to zero or substantially zero degrees if it determines that the orientation is skewed. IMC can utilize a neural network to determine and apply bounding boxes to text strings in the image. Using a rules-based approach or machine learning techniques, employing a trained machine learning component, IMC can utilize the anchor word along with inline grouping of textual information in the image, deep text recognition analysis, or bounding box prediction to determine or predict the desired text information in the image. IMC can facilitate presenting the desired text information, anchor word, or other information obtained from the image in an editable format.

The present disclosure provides a model training method, an identification method, device, storage medium and program product, relating to computer vision technology and deep learning technology. In the solution provided by the present application, the image is deformed by the means of deforming the first training image without label itself, and the first unsupervised identification result is obtained by using the first model to identify the image before deformation, and the second unsupervised identification result is obtained by using the second model to identify the image after deformation, and the first unsupervised identification result of the first model is deformed, thus a consistency loss function can be constructed according to the second unsupervised identification result and the scrambled identification result. In this way, it is able to enhance the constraint effect of the consistency loss function and avoid destroying the scene semantic information of the images used for training.

The technology disclosed uses neural networks to determine analyte metadata by (i) processing input image data derived from a sequence of image sets through a neural network and generating an alternative representation of the input image data, the input image data has an array of units that depicts analytes and their surrounding background, (ii) processing the alternative representation through an output layer and generating an output value for each unit in the array, (iii) thresholding output values of the units and classifying a first subset of the units as background units depicting the surrounding background, and (iv) locating peaks in the output values of the units and classifying a second subset of the units as center units containing centers of the analytes.

A method of establishing a brain status indication parameter indicative of a brain disorder is disclosed. The method comprising the steps:—determining a brain energy metabolism indicator of at least a part of the brain of a subject,—determining a skull energy metabolism indicator of at least a part of the skull of said subject,—establishing the brain status indication parameter by at least relating said brain energy metabolism indicator to said skull energy metabolism indicator. Also disclosed are a system for establishing such brain status indication parameter, a computer program, and methods for treating a disease.

Systems and methods are provided for capturing time-stamped data from whiteboard video signals and producing high-resolution whiteboard images. Local patches around a multitude of pixels in the whiteboard are used in classifying background white pixels and foreground color pixels for each foreground marker color. Clustering is performed in alternative color spaces globally and locally in defining background white and each foreground marker color. Color normalization is performed for each foreground pixel classified as a foreground marker color and for each image sensor color plane separately utilizing the maximum local background white and the darkest pixel intensities in local patches. Strokes are reconstructed based on spline interpolation of inflection points of cross sections along the length of each stroke for a foreground marker color with a predetermined width. Also provided is an intelligent whiteboard collaboration system including a messaging utility whereby participants based on relevant biometrics information are enabled to access time-lapse whiteboard data and communicate with the system and other participants.

A CMOS image sensor for a code reader in an optical code recognition system incorporates a digital processing circuit that applies a calculation process to the capture image data as said data acquired by the sequential readout circuit of the sensor, in order to calculate a macro-image from the capture image data, which corresponds to location information of code(s) in the capture image, and transmit this macro-image in the image frame following the capture image data, in the footer of the frame.

A method for iris-based living body detection and related product are provided. The method includes the following. An iris image is divided into K regional images, where K is an integer greater than one. Living body detection is performed on the K regional images with P iris-based living body detection schemes to obtain K detection results, where P is an integer greater than one and less than or equal to K. Whether the iris image is obtained from an iris of a living body is determined according to the K detection results.

Methods, devices, and systems for automated cellular analysis of a body fluid sample are disclosed. The methods, devices, and systems apply watershed transform to data, generated by flowing a body fluid sample through a flow cytometer, to determine threshold(s) to be used for analysis of the data.

The present disclosure relates to a target object tracking method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a first reference image of a target object; determining time information and location information of the target object in an image to be analyzed according to the first reference image, the image to be analyzed including the time information and the location information; determining a trajectory of the target object according to the time information and the location information of the target object; and generating tracking information for tracking the target object according to the trajectory of the target object. Embodiments of the present disclosure obtain highly-accurate tracking information of the target object according to the trajectory of the target object determined in the image to be analyzed by using the first reference image of the target object, such that the success rate of target object tracking is improved.

According to one aspect, scene classification may be provided. An image capture device may capture a series of image frames of an environment from a moving vehicle. A temporal classifier may classify image frames with temporal predictions and generate a series of image frames associated with respective temporal predictions based on a scene classification model. The temporal classifier may perform classification of image frames based on a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a fully connected layer. The scene classifier may classify image frames based on a CNN, global average pooling, and a fully connected layer and generate an associated scene prediction based on the scene classification model and respective temporal predictions. A controller of a vehicle may activate or deactivate vehicle sensors or vehicle systems of the vehicle based on the scene prediction.