A system includes a sensor, a weight sensor, and a tracking subsystem. The tracking subsystem receives an image feed of top-view images generated by the sensor and weight measurements from the weight sensor. The tracking subsystem detects an event associated with an item being removed from a rack in which the weight sensor is installed. The tracking subsystem determines that a first person and a second person may be associated with the event. In response, the tracking subsystem dilates contours associated with the first and second person from a first depth to a second depth until the contours enter a zone adjacent to the rack. A number of iterations is determined for each contour to enter the zone adjacent to the rack. If the first person's contour enters the zone in fewer iterations, the item is assigned to the first person.

A method and apparatus of detection, registration and quantification of an image is described. The method may include obtaining an image of a lithographically created structure, and applying a level set method to an object, representing the structure, of the image to create a mathematical representation of the structure. The method may include obtaining a first dataset representative of a reference image object of a structure at a nominal condition of a parameter, and obtaining second dataset representative of a template image object of the structure at a non-nominal condition of the parameter. The method may further include obtaining a deformation field representative of changes between the first dataset and the second dataset. The deformation field may be generated by transforming the second dataset to project the template image object onto the reference image object. A dependence relationship between the deformation field and change in the parameter may be obtained.

A level of detail-transformation adaptive enhancement method for image contrast includes: dividing a remote sensing image into a plurality images of different levels of detail, the lowest level of detail defined as L and the highest level of detail defined as H, and gradually transforming an image Image.sub.i of an arbitrary level of detail i between the image Image.sub.H of the highest level of detail H and the image Image.sub.L of the lowest level of detail L from Image.sub.L to Image.sub.H through the following equation: Image.sub.i=R.sub.i×Image.sub.H+(1−R.sub.i)×Image.sub.L. The image Image.sub.H of the highest level of detail H is an image Image.sub.ACE produced with adaptive contrast enhancement processing, or an image produced with a contrast enhancement method such as Gaussian or histogram equalization; the image Image.sub.L of the lowest level of detail L is an image Image.sub.LCE produced by common linear contrast enhancement.

A method for detecting anomalies in digital images of products, wherein a region of a digital image is detected as a maximum anomaly if the value of a property of the region is greater than a predetermined maximum threshold, and/or wherein a region is detected as a minimum anomaly if the value of the property of the region is less than a predetermined minimum threshold. The maximum threshold value and/or the minimum threshold value are determined in a learning process using relatively few digital images based on a statistical distribution of the largest or smallest values of a specific quantity used for the detection of anomalies in digital images to be examined.

In a method for generating a surrogate marker based on medical image data mapping an image region, the medical image data is detected using a first interface, a first subregion of the image region is selected by segmenting a first structure included in the image region, a first property of the first subregion is extracted, the surrogate marker is determined based on the first property, and the surrogate marker is provided using a second interface.

A device and a method for post-processing of computed tomography (CT), which are adapted to improve an identification image of a focal nodular hyperplasia (FNH) of a liver, are provided. The method includes: obtaining the identification image including a liver region and a non-liver region and a Hounsfield unit (HU) value of each pixel corresponding the identification image, wherein the liver region includes an FNH candidate region; calculating an average HU of the liver region; adjusting an HU value of the non-liver region to the average HU value of the liver region with respect to the identification image to generate a processed identification image; and updating the FNH candidate region according to a morphological algorithm based on the processed identification image to generate an updated FNH candidate region.

Image segmentation based on the combination of a deep learning network and a shape-guided deformable model is provided. In various embodiments, a time sequence of images is received. The sequence of images is provided to a convolutional network to obtain a sequence of preliminary segmentations. The sequence of preliminary segmentations labels a region of interest in each of the images of the sequence. A reference and auxiliary mask are generated from the sequence of preliminary segmentations. The reference mask corresponds to the region of interest. The auxiliary mask corresponds to areas outside the region of interest. A final segmentation corresponding to the region of interest is generated for each of the sequence of images by applying a deformable model to the composite mask with reference to the auxiliary mask.

The present disclosure describes a system and methods for autonomous segmentation of volumetric dental images, such as those produced by an imaging system, The methods, implemented by the system, acquire a volume image of a patient and extract a volume of interest comprising patient dentition from the acquired volume image. A first plane is extended through maxillary portions of the patients jaw and a second plane through mandibular portions of the patients jaw. A maxillary sub-volume is generated from the volume of interest according to the first plane and a mandibular sub-volume from the volume of interest according to the second plane. Maximum intensity projection images are formed for each sub-volume and teeth are delineated from these images. Teeth are segmented within each sub-volume according to the tooth delineation for their respective sub-volume.

A computer-implemented method, an apparatus, and a system for estimating synthetic values of quantitative metrics are provided. They involve calculating new, more accurate boundaries using a classifier based on local intensity and spatial estimators, for the segmentation mask provided by a non-local means patch-based segmentation in a test image, and estimating for the pixels of interest at least one synthetic value of a quantitative metric using a given value of the quantitative metric assigned to the reference images and the boundaries. The method, apparatus, and system provide the advantage of generating synthetic values directly comparable against known values for given subjects or against predetermined scales for diagnostic or prognostic purposes. In the specific case of Alzheimer's disease, the invention stretches the predictive range up to two full decades, which constitutes a significant advance in the field of medical diagnostics.

A method and apparatus of detection, registration and quantification of an image is described. The method may include obtaining an image of a lithographically created structure, and applying a level set method to an object, representing the structure, of the image to create a mathematical representation of the structure. The method may include obtaining a first dataset representative of a reference image object of a structure at a nominal condition of a parameter, and obtaining second dataset representative of a template image object of the structure at a non-nominal condition of the parameter. The method may further include obtaining a deformation field representative of changes between the first dataset and the second dataset. The deformation field may be generated by transforming the second dataset to project the template image object onto the reference image object. A dependence relationship between the deformation field and change in the parameter may be obtained.