Abstract: An “AR Privacy API” provides an API that allows applications and web browsers to use various content rendering abstractions to protect user privacy in a wide range of web-based immersive augmented reality (AR) scenarios. The AR Privacy API extends the traditional concept of “web pages” to immersive “web rooms” wherein any desired combination of existing or new 2D and 3D content is rendered within a user's room or other space. Advantageously, the AR Privacy API and associated rendering abstractions are useable by a wide variety of applications and web content for enhancing the user's room or other space with web-based immersive AR content. Further, the AR Privacy API is implemented using any existing or new web page coding platform, including, but not limited to HTML, XML, CSS, JavaScript, etc., thereby enabling existing web content and coding techniques to be smoothly integrated into a wide range of web room AR scenarios.

Abstract: A method comprises computing an activity map for an input image. The activity map indicates an amount of variable spatial activity in the input image. The method further comprises producing an output image based on the activity map, and controlling a printhead to cause the output image to be printed. The output image contains a plurality of color pixels and each such color pixel is represented by at least one of cyan (C), magenta (M), yellow (Y), and black (K) in a color space.

Abstract: A method comprises computing an activity map for an input image. The activity map indicates an amount of variable spatial activity in the input image. The method further comprises producing an output image based on the activity map, and controlling a printhead to cause the output image to be printed. The output image contains a plurality of color pixels and each such color pixel is represented by at least one of cyan (C), magenta (M), yellow (Y), and black (K) in a color space.

Abstract: According to an example, data pertaining to a plurality of entities recommended for a user may be accessed, in which the data identifies relationships between the plurality of entities with respect to each other. In addition, a relevance map for the user that displays graphical representations of the plurality of entities over a substantially optimized use of space available for display of the graphical representations in the relevance map may be generated, in which the graphical representations of the plurality of entities are arranged in the relevance map according to a predetermined arrangement scheme.

Abstract: A clustering method for high-dimensionality data includes identifying a set of nearest neighbors of a point in a multidimensional space and determining the centroid of the set of nearest neighbors, where the centroid is a member of the set of nearest neighbors. The method is then repeated using the neighbors identified around the computed centroid. In one embodiment, the method may terminate when the computed centroid becomes stationary over successive iterations. The resulting centroid may be returned as a mode of the data set. Points of the data set having common modes may be assigned to the same cluster.