An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor, to identify a pixel location within the first frame, and to determine an estimated sensor location for the sensor by applying a homography to the pixel location. The homography includes coefficients that translate between pixel locations in a frame from the sensor and (x,y) coordinates in the global plane. The system is further configured to determine an actual sensor location for the sensor and to determine a location difference between the estimated sensor location and the actual sensor location. The system is further configured to compare the location difference to a difference threshold level and to recompute the homography in response to determining that the location difference exceeds the difference threshold level.

Character recognition processing suitable to a handwritten character area and a printed character area among character areas in a scanned image of a document is performed. Next, character recognition results for the handwritten character area and character recognition results for the printed character area are integrated and a likelihood indicating a probability of being an extraction target is calculated for a candidate character string that is an extraction candidate among the integrated character recognition results and a character string that is the item value is determined. Then, at the time of the determination, different evaluation indications are used in a case where a character originating from the handwritten character area is included in characters constituting the candidate character string and in a case where such a character is not included.

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.

An image forming apparatus capable of controlling execution of inspection without increasing a time period required to complete printing. On a registration screen, whether or not to execute inspection of data to be printed is set, and keywords indicative of confidentiality are registered. Text information is extracted from the data to be printed, and whether or not any keyword matching the text information has been registered is determined. Execution of print processing of the data to be printed is controlled based on a result of the determination. When non-execution of inspection is set, the print processing of the data to be printed is executed without executing the determination, whereas when execution of inspection is set, the print processing of the data to be printed is controlled based on a result of the determination.

Disclosed is a content-based image retrieval (CBIR) system and related methods that serve as a diagnostic aid for diagnosing whether a dermoscopic image correlates to a skin cancer type. Systems and methods according to aspects of the invention use as a reference a set of images of pathologically confirmed benign or malignant past cases from a collection of different classes that are of high similarity to the unknown new case in question, along with their diagnostic profiles. Systems and methods according to aspects of the invention predict what class of skin cancer is associated with a particular patient skin lesion, and may be employed as a diagnostic aid for general practitioners and dermatologists.

A method for shadow and cloud masking for remote sensing images of an agricultural field using multi-layer perceptrons includes electronically receiving an observed image, performing using at least one processor an image segmentation of the observed image to divide the observed image into a plurality of image segments or superpixels, extracting features for each of the image segments using the at least one processor, and determining by a cloud mask generation module executing on the at least one processor a classification for each of the image segments using the features extracted for each of the image segments, wherein the cloud mask generation module applies a classification model including an ensemble of multilayer perceptrons to generate a cloud mask for the observed image such that each pixel within the observed image has a corresponding classification.

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 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.

Provided is a process for generating specifications for lenses of eyewear based on locations of extents of the eyewear determined through a pupil location determination process. Some embodiments capture an image and determine, using computer vision image recognition functionality, the pupil locations of a human's eyes based on the captured image depicting the human wearing eyewear.

A pill identification system identifies a pill type for a pharmaceutical composition from images of the pharmaceutical composition. The system extracts features from images taken of the pill. The features extracted from the pill image include color, size, shape, and surface features of the pill. In particular, the features include rotation-independent surface features of the pill that enable the pill to be identified from a variety of orientations when the images are taken. The feature vectors are applied to a classifier that determines a pill identification for each image. The pill identification for each image is scored to determine identification for the pharmaceutical composition.