Methods of detecting and/or identifying an artificial target within an image are provided. These methods comprise: applying to a region of the image a primary classification algorithm for performing a feature extraction of the image region, the primary classification algorithm being based on a spectral profile defined by one or more spectral signatures with one or more features in at least part of the infrared spectrum; obtaining a relation between the extracted features of the image region and the spectral profile; verifying whether a level of confidence of the obtained relation between the extracted features and the spectral profile is higher than a first predetermined confirmation level; and, in case of positive (or true) result of said verification, determining that the image region corresponds to artificial target to be detected, thereby obtaining a confirmed artificial target. Systems and computer programs are also provided that are suitable for performing said methods.

A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.

An image pickup device transmits to a server a transmission sample including a detection image detected by a first detection section from a transmitting/receiving section under the control of a transmission sample control section. The server performs detection processing that requires more resources than those of the first detection section on the detection image transmitted by a second detection section from the image pickup device, and determines whether or not the detection image in question is spurious, based on a second detection score which is thereby obtained. A transmission frequency deciding section generates transmission frequency control information such as to raise the transmission frequency by an image pickup device that has a high frequency of spurious detection; a transmitting/receiving section transmits the transmission frequency control information to the image pickup device.

A system for detecting clouds and cloud shadows is described. In one approach, clouds and cloud shadows within a remote sensing image are detected through a three step process. In the first stage a high-precision low-recall classifier is used to identify cloud seed pixels within the image. In the second stage, a low-precision high-recall classifier is used to identify potential cloud pixels within the image. Additionally, in the second stage, the cloud seed pixels are grown into the potential cloud pixels to identify clusters of pixels which have a high likelihood of representing clouds. In the third stage, a geometric technique is used to determine pixels which likely represent shadows cast by the clouds identified in the second stage. The clouds identified in the second stage and the shadows identified in the third stage are then exported as a cloud mask and shadow mask of the remote sensing image.

A farming machine includes one or more image sensors for capturing an image as the farming machine moves through the field. A control system accesses an image captured by the one or more sensors and identifies a distance value associated with each pixel of the image. The distance value corresponds to a distance between a point and an object that the pixel represents. The control system classifies pixels in the image as crop, plant, ground, etc. based on the visual information in the pixels. The control system generates a labelled point cloud using the labels and depth information, and identifies features about the crops, plants, ground, etc. in the point cloud. The control system generates treatment actions based on any of the depth information, visual information, point cloud, and feature values. The control system actuates a treatment mechanism based on the classified pixels.

A classifier is executed including an unsupervised artificial intelligence model and a supervised artificial intelligence model. The classifier is configured to receive run-time input data, and process the run-time input data using the unsupervised artificial intelligence model and an outlier detection algorithm to determine whether the run-time input data is an outlier as compared to training input data. Responsive to determining that the run-time input data is not an outlier, the classifier determines a predicted response label for the run-time input based on the run-time input data processed using the supervised artificial intelligence model. Responsive to determining that the run-time input data is an outlier, the classifier refrains from determining the predicted response label for the run-time input based on the run-time input data processed using the supervised artificial intelligence model, and instead outputs a prompt for user input of a user-curated response label for the run-time input.

Embodiments of the present disclosure describe a system and method for optical character recognition. In one embodiment, a system receives an image depicting text. The system extracts features from the image using a feature extractor. The system applies a first decoder to the features to generate a first intermediary output. The system applies a second decoder to the features to generate a second intermediary output, wherein the feature extractor is common to the first decoder and the second decoder. The system determines a first quality metric value for the first intermediary output and a second quality metric value for the second intermediary output based on a language model. Responsive to determining that the first quality metric value is greater than the second quality metric value, the system selects the first intermediary output to represent the text.

Methods, systems, and computer program products for vision-based cell structure recognition using hierarchical neural networks and cell boundaries to structure clustering are provided herein. A computer-implemented method includes detecting a style of the given table using at least one style classification model; selecting, based at least in part on the detected style, a cell detection model appropriate for the detected style; detecting cells within the given table using the selected cell detection model; and outputting, to at least one user, information pertaining to the detected cells comprising image coordinates of one or more bounding boxes associated with the detected cells.

Aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for identifying data processing activities associated with various data assets based on data discovery results. In accordance various aspects, a method is provided comprising: identifying and scanning data assets to detect a subset of the data assets, wherein each asset of the subset is associated with a particular data element used for target data; generating a prediction for each pair of data assets of the subset on the target data flowing between the pair; identifying a data flow for the target data based on the prediction generated for each pair; and identifying a data processing activity associated with handling the target data based on a correlation identified for the particular data element, the subset, and/or the data flow with a known data element, subset, and/or data flow for the data processing activity.

A hybrid inference facility receives a sequence of data items. For each data item, the facility: forwards the data item to a server; subjects it to a local machine learning model to produce a local inference result for the data item; and the local inference result to a queue; aggregates the inference results contained by the queue to obtain an output inference result; and removes the oldest inference result from the queue. The facility receives from the server cloud inference results each obtained by applying a server machine learning model to one of the data items forwarded to the server. For each received cloud inference result, the facility substitutes the cloud inference result in the queue for the local inference result for the same data item.