A drive assist apparatus includes a storage unit that stores a three-dimensional model indicating a moving region, an input unit that receives, from a sensor group installed in the moving region, first height information indicating a first height which is a height of the mobile object and second height information indicating a second height which is a height of an object that satisfies a predetermined distance criterion from the mobile object, an extraction unit that extracts, from the three-dimensional model, a first plan view based on the first height information and a second plan view based on the second height information, a generation unit that generates a combined map for two-dimensionally showing the moving region and assisting the driving of the mobile object by combining the first plan view and the second plan view, and an output unit that transmits a generated combined map to the mobile object.

Provided is a method for extracting an image salient curve. The method comprises the following steps: drawing an approximate curve along a salient edge of an image from which a salient curve is to be extracted; obtaining short edges in the image; calculating a harmonic vector field by using the drawn curve as a boundary condition; filtering the short edges in the image by using the harmonic vector field; updating the vector field by using the short edges left in the image as boundary conditions; and obtaining an optimal salient curve of the image by using the energy of a minimized spline curve in the vector field. Also provided is a system for extracting an image salient curve. The image salient curve can ensure the smoothness and a bending characteristic.

Methods and systems of reconstructing an eyelid are provided. A method of reconstructing an eyelid includes obtaining one or more images of the eyelid, generating one or more image input data for the one or more images of the eyelid, generating one or more reconstruction data for the one or more images of the eyelid, and reconstructing a spatio-temporal digital representation of the eyelid using the one or more input image data and the one or more reconstruction data.

Provided is a contour shape recognition method, including: sampling and extracting salient feature points of a contour of a shape sample; calculating a feature function of the shape sample at a semi-global scale by using three types of shape descriptors; dividing the scale with a single pixel as a spacing to acquire a shape feature function in a full-scale space; storing feature function values at various scales into a matrix to acquire three types of feature grayscale map representations of the shape sample in the full-scale space; synthesizing the three types of grayscale map representations of the shape sample, as three channels of RGB, into a color feature representation image; constructing a two-stream convolutional neural network by taking the shape sample and the feature representation image as inputs at the same time; and training the two-stream convolutional neural network, and inputting a test sample into a trained network model to achieve shape classification.

Methods, systems and processor-readable media for side window detection in near-infrared (NIR) images utilizing machine learning. An image-capturing unit can capture an image/video in a near-infrared (NIR) band via a side window of an incoming vehicle. A deformable part model can be generated utilizing a side window detection and B-frame detection in order to obtain a set of candidate side-windows. Side window detection can be performed based on a mixture of a tree model and a shared pool and can be globally optimized with dynamic programming and still-capture to detect the backseat side window boundary utilizing a B-pillar. A false alarm with respect to the deformable part model can be removed utilizing a super pixel generation and a longest-line detection unit in order to generate a refined deformable part model.

There is provided an image registration device, method, and program that enable easy initial registration between a target object included in a video and a simulation image. A first registration unit performs first registration that is initial registration between an intraoperative video and a simulation image. At this time, a boundary image showing the boundary of the simulation image is displayed on a display so as to be superimposed on the intraoperative video. An operator performs registration between a target object included in the intraoperative video and the boundary image. After the end of the first registration, a second registration unit performs second registration between the simulation image and the target object included in the intraoperative video based on the result of the first registration.

An image measuring apparatus according to an embodiment of the present invention comprises: an imaging device that images a workpiece to acquire an image of this workpiece; and a processing device that performs measurement of the workpiece based on this image and outputs a measurement result. Moreover, the processing device sets a region in the image, sets a plurality of first points along a contour line of this region, sequentially moves these plurality of first points so that the plurality of first points approximate to the contour line included in the image, acquires the moved plurality of first points as a plurality of second points, and calculates the measurement result based on these plurality of second points.

Techniques for segmentation include determining an edge of voxels in a range associated with a target object. A center voxel is determined. Target size is determined based on the center voxel. In some embodiments, edges near the center are suppressed, markers are determined based on the center, and an initial boundary is determined using a watershed transform. Some embodiments include determining multiple rays originating at the center in 3D, and determining adjacent rays for each. In some embodiments, a 2D field of amplitudes is determined on a first dimension for distance along a ray and a second dimension for successive rays in order. An initial boundary is determined based on a path of minimum cost to connect each ray. In some embodiments, active contouring is performed using a novel term to refine the initial boundary. In some embodiments, boundaries of part-solid target objects are refined using Markov models.

A computational lithography process uses machine learning models. An aerial image produced by a lithographic mask is first calculated using a two-dimensional model of the lithographic mask. This first aerial image is applied to a first machine learning model, which infers a second aerial image. The first machine learning model was trained using a training set that includes aerial images calculated using a more accurate three-dimensional model of lithographic masks. The two-dimensional model is faster to compute than the three-dimensional model but it is less accurate. The first machine learning model mitigates this inaccuracy.

A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.