The methods, devices, and systems may allow a practitioner to obtain information regarding a biological sample, including analytical data, a medical diagnosis, and/or a prognosis or predictive analysis. The method, devices, and systems may provide a grade or level of development for identified diseases. In addition, the methods, devices and systems may generate a confidence value for the predictive classifications generated, which may, for example be generated in a format to show such confidence value or other feature in a graphical representation (e.g., a color code). Further, the methods, devices and system may aid in the identification and discovery of new classes and tissue sub-types.

Certain text editing techniques are provided to enable the user to select a few characters or words from an original collection of text by a limited number of user inputs, such as by touching or swiping a character or word to be edited. Options for probable edits of the original text can be automatically presented to the user in a manner that also only requires a minimal number of user inputs. Such techniques may facilitate text editing that is easier and more convenient for users of portable electronic devices. These techniques may be particularly advantageous for enabling editing of text acquired from an optical character recognition (OCR) process on any computing device. Other text editing shortcuts and similar approaches are also described.

A smart medication identifying system is disclosed herein. It comprises a processing device including a first processing module, a scanning module electrically connected to the first processing module and a first reminding module electrically connected to the first processing module; a cloud storage device electrically connected to the processing device and having a storage module, a login module electrically connected to the storage module, and a medication information database electrically connected to the storage module; and a medication identifying device electrically connected to the processing device and the cloud storage device and having a second processing module, an image identifying module electrically connected to the second processing module and a second reminding module electrically connected to the second processing module.

A biometric input system includes: a biometric sensor, configured to generate a biometric image comprising features of an input biometric object; and a processing system, configured to receive the biometric image, generate a feature map from the biometric image, perform a distance transform on the feature map, and determine an attribute of the biometric image from the distance transform. The processing system is further configured to compare the determined attribute of the biometric image with corresponding attributes of enrolled biometric images, and, based on the comparison, to eliminate enrolled biometric images from a matching process for the biometric image, wherein the matching process is performed after the elimination and comprises comparison of the biometric image to remaining enrolled biometric images based on one or more attributes of the biometric image other than the determined attribute.

An object tracking system includes a sensor and a tracking system. The sensor is configured to capture a first frame of a global plane for at least a portion of a marker grid in a space. The tracking system is configured to receive a first coordinate in the global plane for a first corner of a marker grid, to determine a second coordinate in the global plane for the first marker on the marker grid, and to determine a third coordinate in the global plane where the second marker on the marker grid. The tracking system is further configured to determine a first pixel location for the first marker, to determine a second pixel location for the second marker, and to generate a homography based on the second coordinate for the first marker, the third coordinate for the second marker, the first pixel location, and the second pixel location.

A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

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.

The present disclosure provides a method for joint manifold learning based heterogenous sensor data fusion, comprising: obtaining learning heterogeneous sensor data from a plurality sensors to form a joint manifold, wherein the plurality sensors include different types of sensors that detect different characteristics of targeting objects; performing, using a hardware processor, a plurality of manifold learning algorithms to process the joint manifold to obtain raw manifold learning results, wherein a dimension of the manifold learning results is less than a dimension of the joint manifold; processing the raw manifold learning results to obtain intrinsic parameters of the targeting objects; evaluating the multiple manifold learning algorithms based on the raw manifold learning results and the intrinsic parameters to determine one or more optimum manifold learning algorithms; and applying the one or more optimum manifold learning algorithms to fuse heterogeneous sensor data generated by the plurality sensors.

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.

An image processing apparatus for setting a property of a document file by using a result of a character recognition process performed on a scanned image of a document is provided and includes an obtaining unit and an a setting unit. The obtaining unit obtains a character string by performing the character recognition process on a scanned image relating to a document file to be generated in this operation. The setting unit automatically sets the character string obtained by the obtaining unit as a character string to be used in a property of the document file to be generated in this operation if the character string obtained by the obtaining unit is a character string obtained in the character recognition process performed on a scanned image relating to a document file generated in the past and approved by a user a certain number of times or more.