A cargo position tracking routine implemented in an electronic control unit of an automotive vehicle uses machine vision to monitor the position of cargo in a cargo area of an automotive vehicle and determine whether the cargo has shifted. Upon determining that the cargo has shifted, the cargo position tracking routine causes a driver of the vehicle to be alerted.

A cargo position tracking routine implemented in an electronic control unit of an automotive vehicle uses machine vision to monitor the position of cargo in a cargo area of an automotive vehicle and determine whether the cargo has shifted. Upon determining that the cargo has shifted, the cargo position tracking routine causes a driver of the vehicle to be alerted.

Systems and methods for tracking moving objects in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, an object tracking system comprises a processor, a communications interface, and a memory configured to store an object tracking application. The object tracking application configures the processor to receive a sequence of images; estimate and subtract background pixel values from pixels in a sequence of images; compute sets of summed intensity values for different per frame pixel offsets from a sequence of images; identify summed intensity values from a set of summed intensity values exceeding a threshold; cluster identified summed intensity values exceeding the threshold corresponding to single moving objects; and identify a location of at least one moving object in an image based on at least one summed intensity value cluster.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

A three-dimensional object detection device includes an image capturing unit, an image conversion unit, a three-dimensional object detection unit and a light source detection unit. The image conversion unit converts a viewpoint of the images obtained by the image capturing unit to create bird's-eye view images. The three-dimensional object detection unit detects a presence of a three-dimensional object within the adjacent lane. The three-dimensional object detection unit determines the presence of the three-dimensional object within the adjacent lane-when the difference waveform information is at a threshold value or higher. The three-dimensional object detection unit set a threshold value lower so that the three-dimensional object is more readily detected in a rearward area than forward area with respect to a line connecting the light source and the image capturing unit.

A three-dimensional object detection device includes an image capturing unit, an image conversion unit, a three-dimensional object detection unit and a light source detection unit. The image conversion unit converts a viewpoint of the images obtained by the image capturing unit to create bird's-eye view images. The three-dimensional object detection unit detects a presence of a three-dimensional object within the adjacent lane. The three-dimensional object detection unit determines the presence of the three-dimensional object within the adjacent lane-when the difference waveform information is at a threshold value or higher. The three-dimensional object detection unit set a threshold value lower so that the three-dimensional object is more readily detected in a rearward area than forward area with respect to a line connecting the light source and the image capturing unit.

A X-ray chest image rib suppression method based on Poisson model. It conducts contourlet transformation on the image and utilizes transformation coefficient correlation between different scales to conduct texture enhancement on the image; it designs strip-type detection filter in accordance with the Hessian matrix eigenvalue to the image and detects the area where the rib locates in; it combines enhanced texture and rib area information, establishes and solves rib suppression Poisson model, realizing the rib suppression in the image. Anisortropy and contourlet transformation multi-direction feature is utilized, scale and coefficients direction information are combined and distinction degree between texture and noise improves, enhancing texture while restraining noise; it realizes ribs suppression through solving the Poisson model, which does not need to conduct accurate segmentation on the rib, prevents unnatural transition problem of edges resulted from explicit ribs suppression and effectively suppress the ribs, improving observation effect of X-ray chest image.

In one embodiment, a method of amplifying temporal variation in at least two images includes converting two or more images to a transform representation. The method further includes, for each spatial position within the two or more images, examining a plurality of coefficient values. The method additionally includes calculating a first vector based on the plurality of coefficient values. The first vector can represent change from a first image to a second image of the at least two images describing deformation. The method also includes modifying the first vector to create a second vector. The method further includes calculating a second plurality of coefficients based on the second vector.

In one embodiment, a method of amplifying temporal variation in at least two images includes converting two or more images to a transform representation. The method further includes, for each spatial position within the two or more images, examining a plurality of coefficient values. The method additionally includes calculating a first vector based on the plurality of coefficient values. The first vector can represent change from a first image to a second image of the at least two images describing deformation. The method also includes modifying the first vector to create a second vector. The method further includes calculating a second plurality of coefficients based on the second vector.