An information processing device that includes a control unit configured to track an object in an image using images input in time series, using a tracking result obtained by performing tracking in units of a tracking region corresponding to a specific part of the object.

Disclosed is a gamification system for overlaying user-controlled graphical targeting elements over a real-time video feed of an instrument being inspected, and providing interactive controls for firing virtual weapons or other graphical indicators to designate and/or record the presence of contaminants, defects, and/or other issues at specific locations within or on the instrument. The system may receive and present images of the instrument under inspection in a graphical user interface (“GUI”). The system may receive user input that tags a particular region of a particular image with an issue identifier, and may generate a visualization that is presented in conjunction with the particular image in the GUI in response to receiving the input. The visualization corresponds to firing of a virtual weapon and other gaming visuals associated with tagging the particular region of the particular image with the issue identifier.

A medical image segmentation method based on a U-Net, including: sending real segmentation image and original image to a generative adversarial network for data enhancement to generate a composite image with a label; then putting the composite image into original data set to obtain an expanded data set, and sending the expanded data set to improved multi-feature fusion segmentation network for training. A Dilated Convolution Module is added between the shallow and deep feature skip connections of the segmentation network to obtain receptive fields with different sizes, which enhances the fusion of detail information and deep semantics, improves the adaptability to the size of the segmentation target, and improves the medical image segmentation accuracy. The over-fitting problem that occurs when training the segmentation network is alleviated by using the expanded data set of the generative adversarial network.

An improvement to automatic classifying of threat level of objects in CT scan images of container content, methods include automatic identification of non-classifiable threat level object images, and displaying on a display of an operator a de-cluttered image, to improve operator efficiency. The de-cluttered image includes, as subject images, the non-classifiable threat level object images. Improvement to resolution of non-classifiable threat objects includes computer-directed prompts for the operator to enter information regarding the subject image and, based on same, identifying the object type. Improvement to automatic classifying of threat levels includes incremental updating the classifying, using the determined object type and the threat level of the object type.

Systems and methods for generating assessments based on construction site images are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. Further, at least one electronic record associated with the construction site may be obtained. The image data and the at least one electronic record may be analyzed to generate at least one assessment related to the construction site. For example, the image data may be analyzed to identify at least one discrepancy between the at least one electronic record and the construction site, and the identified at least one discrepancy may be used in the generation of the at least one assessment.

A distance measurement accuracy is improved without increasing power consumption of an image processing apparatus that performs distance-measuring processing. In one embodiment, an image processing apparatus for calculating distance information on an image has a reliability calculation unit 113 configured to calculate reliability in accordance with contrast for each pixel of the image and a distance calculation unit 116 configured to calculate distance information on each of the pixels based on reliability of each of the pixels. The distance calculation unit 116 calculates the distance information about a second pixel group whose reliability is lower than that of a first pixel group by using a collation area whose size is larger than a predetermined size in a range in which an amount of calculation in a case where a collation area of the predetermined size is used for all the pixels of the image is not exceeded.

Methods and apparatuses for asynchronously identifying and modeling a gingiva strip from the three-dimensional (3D) dental model of the patient's dentition. These methods may reduce the time required to generate accurate 3D dental models and therefore may reduce and streamline the process of generating dental treatment plans.

The invention relates to a medical data processing method for determining the position of a region of interest serving as a start condition for conducting diffusion image-based tracking of nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.

For obtaining an good yet easy to use luminance dynamic range conversion, we describe an image color processing apparatus (200) arranged to transform an input color (R,G,B) of a pixel of an input image (Im_in) having a first luminance dynamic range into an output color (Rs, Gs, Bs) of a pixel of an output image (Im_res) having a second luminance dynamic range, which first and second dynamic ranges differ in extent by at least a multiplicative factor 2, comprising: a maximum determining unit (101) arranged to calculate a maximum (M) of color components of the input color, the color components at least comprising a red, green and blue component; —a uniformization unit (201) arranged to apply a function (FP) to the maximum (M) as input, which function has a logarithmic shape and was predetermined to be of a fixed shape enabling to transform a linear input to a more perceptually uniform output variable (u); a function application unit (203) arranged to receive a functional shape of a function, which was specified previously by a human color grader, and apply the function to the uniform output variable (u), yielding a transformed uniform value (TU); a linearization unit (204) arranged to transform the transformed uniform value (TU) to a linear domain value (LU); a multiplication factor determination unit (205) arranged to determine a multiplication factor (a) being equal to the linear domain value (LU) divided by the maximum (M); and a multiplier (104) arranged to multiply at least three linear color components (R,G,B) by the multiplication factor (a), yielding the output color.

The invention relates to a medical data processing method for determining the position of a nerve fiber based on a diffusion image-based tracking method of tracking nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.