A method for estimating a 3D vector angle from a 2D face image, a method for creating a face replacement database and a method for replacing a face image includes steps of capturing a face image, detecting a rotation angle of the face image, defining a region to be replaced in the face image, creating a face database for storing replaced images corresponding to the region to be replaced, and pasting one of the replaced images having the corresponding rotation angle of the face image into a target replacing region. Therefore, the region to be replaced of a static or dynamic face image can be replaced by a replaced image quickly by a single camera without requiring a manual setting of the feature points of a target image. These methods support face replacement at different angles and compensate the color difference to provide a natural look of the replaced image.

Techniques to verify a participant's visit to a specific location are described. An embodiment may provide a system that generates a pattern that is unique to the location, and that may further be unique to a date or time, a transaction, or other criteria. Participants may capture the pattern, for example, using a mobile device, and transmit the pattern to a verification system. The verification system may decode, translate, decrypt or otherwise obtain information from the pattern. The information obtained from the pattern may be used to verify that the pattern came from the location. The participant may then receive credit for the visit. Other embodiments are described and claimed.

Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation. (d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.

Methods and apparatus are disclosed for extracting a one-dimensional digital signal from a two-dimensional digital image along a projection line. In some embodiments a repeating sequence of pixel weight templates, and a sequence of relative positions, are selected in response to the orientation of a projection line and used to compute a sequence of weighted sums. The sequence can be selected to achieve desirable properties, for example photometric accuracy, geometric accuracy, resolution, and/or noise reduction. In some embodiments registers and multiply-accumulators are arranged and controlled so as to compute the 1D signal.

Described herein is a method for adjusting one or more images of a sample to correct geometric distortions and/or to properly align the one or more images using a pattern of dots, e.g., a quasiperiodic grid.

Systems, methods, and apparatuses are disclosed for simplifying a point cloud. A point cloud is received where the point cloud has a plurality of points, a global spatial structure, and a local point density. The processor calculates a set of pairwise distances for the plurality of points to at least one other point in the plurality of points. A first distance matrix is generated using the set of pairwise distances. The processor calculates a second pairwise distance set where the plurality of points have a weight and generates a second distance matrix based off the second pairwise distance set. A portion of the points in the second pairwise distance set are removed based on the weight. The processor performs a comparison of the two matrices using the comparison and the global spatial structure and the local point density, and generates a second point cloud based on the second distance matrix.

A method, system, and machine-readable medium for classifying an image element as one of a plurality of categories, including assigning the image element based on a ratio between an unoccluded perimeter of the image element and an occluded perimeter of the image element and coding the image element according to a coding scheme associated with the category to which the image element is classified. Exemplary applications include image compression, where categories include image foreground and background layers.

Technologies for performing two-dimensional gesture recognition are described. In some embodiments the technologies include systems, methods, and computer readable media for performing two-dimensional gesture recognition on one or more input images. In some embodiments the technologies use an object detector to detect one or more suspected gestures in an input image, and to generate a first set of hits correlating to detected gestures in the input image. At least a portion of false positive hits may then be removed by the application of one or more filters to the first set of hits. Custom hand gesture filters are also described.

A method and a system of stitching a plurality of views of a scene, the method including extracting points of interest in each view to include a point set from each of the plurality of image views of the scene, matching the points of interest and reducing an outlier, grouping the matched points of interest in a plurality of groups, determining a similarity transformation with a smallest rotation angle for each grouping of the match points, generating virtual matching points on a non-overlapping area of the plurality of image views, generating virtual matching points on an overlapping area for each of the plurality of image views, and calculating piecewise projective transformations for the plurality of image views.

To detect the presence of a logo in a search image, the positions of edges, in an image of the logo and in the search image, are represented by ratios of distances between the respective edge and a plurality of preceding edges. This can be carried out both horizontally and vertically. A sequence of edges at a selected position in the logo image is then compared with sequences of edges from a plurality of positions in the search image, and the position of the logo in the search image is determined from the position of a match between the respective sequences of edges.