Abstract: A method and a device for detecting a placement of wafers in a wafer cassette are provided. The wafer cassette includes a plurality of receiving grooves. The receiving grooves hold and store the wafers. Light is emitted through the wafer cassette. The light passes through the gaps and is imaged, and the image is captured. Characteristic information is extracted from the image. The extracted characteristic information is compared with standard characteristic information of a preset image, and whether a placement of all the wafers in the wafer cassette is qualified and satisfactory is determined according to a compared result.

Abstract: A method for detecting and tracking target object in a captured video using convolutional neural network (CNN) is provided. The method includes: inputting image data into a detecting model to generate detection results, wherein the detecting model is constructed by the CNN; inputting the image data into tracking models to generate tracking results; performing detection score enhancement operation according to the detection results and the tracking results to obtain enhanced detection results; matching the enhanced detection results and the tracking results by a matching operation; processing matched results and unmatched target detection results and unmatched target tracking results; and selectively updating the tracking models using tracking reliability estimation according to the matched results.

Abstract: Provided is an image processing device with which it is possible to automate the setting of a detection parameter that is suitable for obtaining a desired detection result.

Abstract: An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.

Abstract: Disclosed is an image analysis system which includes a first analyzer and a second analyzer. The first analyzer generates first features by encoding images through a first model and adjusts a weight of the first model based on the first features. The second analyzer generates second features based on the first features by encoding the images through a second model, classifies the second features into classes, respectively, and adjusts a weight of the second model based on mutual information and a correlation between a first class among the classes which features corresponding to an original image group from among the second features are classified as and a second class among the classes which features corresponding to an augmentation group of the original image group from among the second features are classified as.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for eye gesture recognition. In one aspect, a method includes obtaining an electrical signal that represents a measurement, by a photodetector, of an optical signal reflected from an eye and determining a depth map of the eye based on phase differences between the electrical signal generated by the photodetector and a reference signal. Further, the method includes determining gaze information that represents a gaze of the eye based on the depth map and providing output data representing the gaze information.

Abstract: A system, apparatus, method and computer program product are provided for determining a mobile device integrity status. Images of a mobile device captured by the mobile device and using a reflective surface are processed with various trained models, such as neural networks, to verify authenticity, detect damage, and to detect occlusions. A mask may be generated to enable identification of concave occlusions or blocked corners of an object, such as a mobile device, in an image. Images of the front and/or rear of a mobile device may be processed to determine the mobile device integrity status such as verified, not verified, or inconclusive. A user may be prompted to remove covers, remove occlusions, and/or move the mobile device closer to the reflective surface. A real-time response relating to the mobile device integrity status may be provided. The trained models may be trained to improve the accuracy of the mobile device integrity status.

Abstract: An image of a financial instrument can be received and analyzed. One or more properties of the financial instrument can be determined based on the image. A uniqueness rating of the financial instrument can be determined based on the property or properties. Terms of the financial instrument are identified, and the financial instrument and identified terms are recorded in a digital storage base.

Abstract: A human activity recognition fusion method and system for ecological protection red line is disclosed. The method includes: obtaining a pre-stage remote sensing image and a post-stage remote sensing image of a target ecological protection red line region, and performing a data pre-processing; inputting the pre-processed pre-stage remote sensing image and the post-stage remote sensing image into a human activity recognition model after a pre-training; identifying a human activity pattern of the target ecological protection red line region as a first detection result; segmenting, calculating and analyzing the latest image data corresponding to the target ecological protection red line region based on a geographical country situation data to obtain a change pattern as a second detection result; and fusing the first detection result and the second detection result to obtain a change detection pattern of the target ecological protection red line region.

Abstract: This disclosure provides alternative medical imaging systems and methods for vascular imaging co-registration. Methods include obtaining extravascular imaging data of a portion of a blood vessel including an extravascular image showing an intravascular imaging device disposed within the vessel with an imaging element disposed at a starting location for a translation procedure. The extravascular image also includes an extravascular contrast image showing the portion of the blood vessel with contrast showing a visualized anatomical landmark. Intravascular imaging data is obtained during the translation procedure that includes one or more intravascular images showing a detected anatomical landmark. The starting location and the ending location of the imaging element on the extravascular imaging data is marked, and the predicted location of the detected anatomical landmark on the extravascular imaging data is marked.

Abstract: An image processing device includes an image processing unit configured to execute image processing on an image based on an input image, and output a result of the image processing. The input image is a post-conversion image obtained by performing image conversion on an original image. The image conversion includes image conversion for further reducing a data size of the original image while maintaining a feature acquired from the original image and related to an object similar to a subject captured in the original image and maintaining processing accuracy of the image processing.

Abstract: A computing device performs initial processing of sensor data. The computing device includes one or more processors and instructions or logic that, when executed by the one or more processors, cause the computing device to perform obtaining sensor data, encoding the sensor data, writing the encoded sensor data to a dynamically allocated buffer, and logging a status of the written encoded sensor data at a static location of the dynamically allocated buffer. The status includes any one or more of memory addresses at which frames of the sensor data begin in the dynamically allocated buffer, valid bit fields corresponding to the frames, and sizes of each of data segments within the frames. The instructions further cause the computing device to perform, in response to receiving a polling request from a computing resource, transmitting the logged status to the computing resource.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for integrated device connectivity and agile device control for dynamic object tracking and management. In this regard, embodiments of the invention provide provides a comprehensive integrated platform for dynamic inventory construction and tracking, dynamic food item identification, expiration tracking, dynamic inventory construction, recipe generation, interactive meal planning and guided cooking, in real-time. The system is also configured for establishing operative communication with a plurality of auxiliary devices for achieving the foregoing. The system may initiate a presentation of parameters identified by the auxiliary devices on a user interface associated with a user device.

Abstract: An apparatus includes at least one processor configured to obtain stereo-spatio-temporal data measurements of plants in a growing area. The stereo-spatio-temporal data measurements include (i) first spatio-temporal data measurements of the plants in the growing area and (ii) second spatio-temporal data measurements of the plants in the growing area. The at least one processor is also configured to analyze the stereo-spatio-temporal data measurements to identify one or more actual or potential problems associated with one or more of the plants. The at least one processor is further configured to generate a graphical user interface identifying at least one of the one or more actual or potential problems with the one or more plants. The first and second spatio-temporal data measurements of each stereo-spatio-temporal data measurement are associated with at least one common plant characteristic and different three-dimensional positions within the growing area taken at one or more known times.

Abstract: A method and apparatus for image processing, an electronic device, and a computer readable storage medium are provided. The method may include acquiring description information of a reference image matching a target image from a reference image information database. The method further includes determining at least one entity from the description information of the reference image, the at least one entity identifying an object associated with the reference image. In addition, the method may further include generating description information of the target image based on the at least one entity.

Abstract: A method and a system for detecting a scene text are provided. The method includes: acquiring a scene text picture in a preset manner, pre-processing the acquired scene text picture, detecting the pre-processed scene text picture with a training model for scene text detection, and acquiring a detection result. Accordingly, the method and the system have an original PSENet (Progressive Scale Expansion Network) backbone network ResNet (Deep Residual Network) is replaced with a rich feature structure network (i.e. Res2NeXt (Combination of Res2Net and ResNeXt)) to improve a network feature extraction capability, thereby increasing a text detection precision of the network; mixed pooling is added at an appropriate location in the backbone network to acquire useful context information by performing pooling operations of different kernel shapes and capture long and short distance dependency relationships between different locations, thereby further increasing the text detection precision of the network.

Abstract: Methods, devices, and systems that are related to facilitating an automated, fast and accurate model for cardiac image segmentation, particularly for image data of children with complex congenital heart disease are disclosed. In one example aspect, a generative adversarial network is disclosed. The generative adversarial network includes a generator configured to generate synthetic imaging samples associated with a cardiovascular system, and a discriminator configured to receive the synthetic imaging samples from the generator and determine probabilities indicating likelihood of the synthetic imaging samples corresponding to real cardiovascular imaging sample. The discriminator is further configured to provide the probabilities determined by the discriminator to the generator and the discriminator to allow the parameters of the generator and the parameters of the discriminator to be adjusted iteratively until an equilibrium between the generator and the discriminator is established.

Abstract: An image filling method and apparatus, a device and a storage medium are disclosed. The image filling method includes: performing multilevel encoding processing on features of an image to be filled to generate multilevel encoded feature layers, sizes of the multilevel encoded feature layers being reduced layer by layer; performing layer-by-layer decoding processing on the multilevel encoded feature layers to obtain multilevel decoded feature layers and a first image, there being no missing region in the first image, wherein the layer-by-layer decoding processing includes a concatenation operation on a decoded feature layer and an encoded feature layer with a same size; and performing up-sampling processing on the first image to obtain multilevel up-sampled feature layers and a second image optimized by the up-sampling processing, the up-sampling processing including a concatenation operation on an up-sampled feature layer and a decoded feature layer with a same size.

Abstract: Embodiments relate to extracting features from images, such as by identifying keypoints and generating keypoint descriptors of the keypoints. An apparatus includes a pyramid image generator circuit, a keypoint descriptor generator circuit, and a pyramid image buffer. The pyramid image generator circuit generates an image pyramid from an input image. The keypoint descriptor generator circuit processes the pyramid images for keypoint descriptor generation. The pyramid image buffer stores different portions of the pyramid images generated by the pyramid image generator circuit at different times and provides the stored portions of the pyramid images to the keypoint descriptor generator circuit for keypoint descriptor generation. When first portions of the pyramid images are no longer needed for the keypoint descriptor generation, the first portions are removed from the pyramid image buffer to provide space for second portions of the pyramid images that are needed for the keypoint descriptor generation.

Abstract: A computer-implemented method for training a normalizing flow. The normalizing flow predicts a first density value based on a first input image. The first density value characterizes a likelihood of the first input image to occur. The first density value is predicted based on an intermediate output of a first convolutional layer of the normalizing flow. The intermediate output is determined based on a plurality of weights of the first convolutional layer. The method for training includes: determining a second input image; determining an output, wherein the output is determined by providing the second input image to the normalizing flow and providing an output of the normalizing flow as output; determining a second density value based on the output tensor and on the plurality of weights; determining a natural gradient of the plurality of weights with respect to the second density value; adapting the weights according to the natural gradient.