A method for creating a plane panorama from point cloud data using Hough transformations is disclosed. The method involves converting the three-dimensional point cloud into a two-dimensional histogram with bins grouping neighboring points, and performing a Hough transformation on the histogram. The resulting transformed data is segmented and the method searches the segments iteratively for a major line, followed by lines that are orthogonal, diagonal, or parallel to the major line, and discards outlying data in each bin as lines are identified. The detected lines are connected to form planes, and the planes are assembled into a hole- and gap-filled panorama. The method may also use an algorithm such as a Random Sample Consensus (RANSAC) algorithm to detect a ground plane.

A novel method can determine the mechanical properties of additively manufactured structures using artificial neural network and computer vision models. Using this methodology, simulation times can be dramatically reduced, allowing for the implementation of a genetic algorithm which can determine the optimal AM parameters to achieve a targeted mechanical response.

A container for a foodstuff or beverage preparation machine, the container for containing beverage or foodstuff material and comprising a code encoding preparation information, the code comprising a reference portion and a data portion, the reference portion comprising an arrangement of at least two reference units defining a reference line r, the data portion comprising: a plurality of adjacent sectors arranged on an encoding line D, whereby each sector is bounded by a first circumferential position and a second circumferential position on the encoding line D and each sector comprises a data unit arranged on the encoding line D between said first and second circumferential position, the data unit arranged a distance d extending from the first circumferential position as a variable to at least partially encode a parameter of the preparation information, whereby the encoding line D is circular and is arranged with a tangent thereto orthogonal the reference line r at an intersection point.

A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

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 system and method for detecting a stop line on a roadway. The system and method a front view camera that generates images of the roadway. The system and method also include a controller that receives the images generated from the front view camera, including a bird's eye view image, said controller further programmed to provide a composite image that includes an original bird's eye view image and a rotated bird's eye view image. The controller is also programmed to use the composite image to determine if a stop line is present on the roadway.

An Internet of Thing (IoT) device includes a body with a processor, a camera and a wireless transceiver coupled to the processor.

A method for positioning an intelligent terminal apparatus comprises: acquiring data points scanned by a position detection device in the intelligent terminal apparatus at the current position of the intelligent terminal apparatus; converting valid data points into segment features to obtain a first set of segment features; and converting point features in an established map of the intelligent terminal apparatus into segment features to obtain a second set of segment features; selecting, from the first and second set of segment features, segments having the same position relationship respectively to form a first and a second candidate subset of the first and the second set of segment features respectively; and determining a transformation matrix that matches the first candidate subset to the second candidate subset, and identifying the current position and the orientation angle of the intelligent terminal apparatus in the established map based on the transformation matrix.

An image-based road cone recognition method, apparatus, storage medium, and vehicle. Said method comprises: acquiring, during vehicle driving, an image of an object to be recognized; performing differential processing of the image, so as to acquire an image on which the differential processing has been performed, and performing, according to a preset threshold, ternary processing of the image on which the differential processing has been performed, so as to acquire a ternary image comprising forward boundary pixels and negative boundary pixels; acquiring, according to the forward boundary pixels and the negative boundary pixels, a forward straight line segment and a negative straight line segment which represent the trend of the boundaries of the object to be recognized; when position information of the forward and negative straight line segments matches boundary position information of a known road cone, determining that the object to be recognized is a road cone.