Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

A method of extracting and reconstructing court lines includes the steps of binarizing a court image of a court including court lines to form a binary image; performing horizontal projection for the binary image; searching for plural corners in the binary image and defining a court line range by the corners; forming plural linear segments from images within the court line range by linear transformation; defining at least one first cluster and at least one second cluster according to the characteristics of the linear segments and categorizing the linear segments into plural groups; taking an average of each group as a standard court line and creating a linear equation of the standard court line to locate the point of intersection of the standard court lines; and reconstructing the court lines according to the point of intersection. This method is capable of extracting the image of a portion of the court line from a dynamic or static image having a court line quickly to eliminate interference caused by noises coming from a portion other than the court line such as the background color, ambient brightness, people or advertisement, and reconstructing the court lines quickly and accurately to facilitate the determination of the boundary of a court line or the computation of data.

A mask structure optimization device includes a classification target image size acquisition unit that is configured to acquire a size of a classification target image which is an image including a classification target, a mask size setting unit that is configured to set a size of a mask applied to the classification target image, a brightness detection unit that is configured to detect a brightness of each pixel within the classification target image at a position on an opposite side of the mask from the classification target image, a sum total brightness calculation unit that is configured to calculate the sum total brightness of the each pixel within the classification target image detected by the brightness detection unit, an initial value setting unit that is configured to set an initial value for a mask pattern of the mask, and a movement unit that is configured to relatively move the mask with respect to the classification target image. The sum total brightness calculation unit is configured to calculate the sum total brightness of the each pixel within the classification target image every time the movement unit relatively moves the mask by a predetermined movement amount. The mask structure optimization device further includes a mask pattern optimization unit that is configured to optimize the mask pattern of the mask on the basis of the sum total brightness.

An apparatus and a method for controlling an airbag are provided The apparatus includes an imaging device that includes cameras and lights and an image processor that performs an image processing for an image obtained by the imaging device. A collision sensor senses a collision of a vehicle and an airbag driver operates the airbag. A controller extracts face information of a passenger from the image of which the image processing is performed by the image processor, determines airbag control information based on the extracted face information, and then operates the airbag driver based on the determined airbag control information when the collision of the vehicle is predicted.

Mechanisms are provided for determining a measure of radiodensity of anatomical structures of interest and classifying medical imaging study data structures (studies) with regard to contrast phase. In some embodiments, this classification may be used to select/exclude slices for processing by other downstream computing systems. A subset of slices are selected from the study and, for each slice in the subset, a corresponding body part regression (BPR) score is determined. A linear regression on the BPR scores is performed and a representative slice is selected based on results of the linear regression. The representative slice is segmented and a statistical measure of a radiodensity metric for each segment in the representative slice is determined.

This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

A change degree deriving apparatus includes a receiving unit and a deriving unit. The receiving unit is configured to receive first image data of an object including an achromatic color and a first color and reference image data of the object. The first image data relates to the first color. The reference image data serves as a reference. The deriving unit is configured to derive a change degree of the object from a first difference based on the first image data and the reference image data received by the receiving unit. The first difference is a difference between the first image data and the reference image data, which occurs at a chromatic color portion when a portion corresponding to the achromatic color is set as a reference.

The embodiment of the present invention provides a human face recognition method and recognition system. The method includes that: a human face recognition request is acquired, and a statement is randomly generated according to the human face recognition request; audio data and video data returned by a user in response to the statement are acquired; corresponding voice information is acquired according to the audio data; corresponding lip movement information is acquired according to the video data; and when the lip movement information and the voice information satisfy a preset rule, the human face recognition request is permitted. By performing fit goodness matching between the lip movement information and voice information in a video for dynamic human face recognition, an attack by human face recognition with a real photo may be effectively avoided, and higher security is achieved.

Techniques for removing artifacts, such as shadows, from document images are described. A shadow map is generated for a digital image by first determining local background colors using clusters of local pixel intensities. Then, a global reference background color is selected from all pixel intensities of the digital image. Next, a per-pixel scaling factor is determined that maps the local background colors to the global reference background color, which applies localized adjustment to the digital image to remove local shadow.

Methods and apparatus automatically classify intravascular plaque using features extracted from intravascular optical coherence tomography (IVOCT) imagery. One example apparatus includes an image acquisition circuit that accesses a set of IVOCT images, a pre-processing circuit that generates a blood vessel mask based on the IVOCT images, a feature extraction circuit that defines a three dimensional (3D) volume of interest centered on a location in a member of the set of IVOCT images, a classification circuit that generates a classification based on a probability that a voxel represents a type of plaque, and a visualization circuit that provides a visualization, substantially in real time, of a member of the set of IVOCT images and the classification, where the visualization includes a sector classification image, a labeled image, or a 3D visualization. A prognosis or treatment plan may be provided based on the visualization or the classification.