Hewlett-packard Development Company, L.p.

Hewlett-packard Company

Daniel R Tretter

Qian Lin

Jonathan Yen

Tong Zhang

Niranjan Damera-Venkata

Jinman Kang

David Bradley Short

Jian Fan

Huitao Luo

Kar-Han Tan

Peng Wu

Clayton Atkins

Yun David Tang

Suk Hwan Lim

Otto Kenneth Sievert

Kar Han Tan

Wei Zhang

William James Allen

Hui Chao

Robert Alan Ulichney

Ramin Samadani

Ullas Gargi

Xuemei Zhang

Pere Obrador

Wei Hong

Su Wang

C Brian Atkins

Feng Tang

Konstantinos Konstantinides

Amit A Marathe

Ying Ying Li

Hao Tang

Kong Kritayakirana

Divya Sharma

Richard E Aufranc, Jr

Cindy Y Sansom-Wai

Debargha Mukherjee

D Amnon Silverstein

Ron Kimmel

Ping Wah Wong

Keith E Moore

Bradley Neal Suggs

Yining Deng

Charles Chi Jia

Liang Tang

Eamonn O'Brien-Strain

Simon Widdowson

Hugh Phu Nguyen

Andrew J Patti

Heng Su

Yong Wang

Diogo Strube De Lima

Truong-Thao Nguyen

Anne-Marie Woodson

Malcolm Stuart Rix

Michael Stokes

Irene H Williams

Thomas D Kite

Ilya Gerasimets

Niaz R Pavel

Neerja Raman

Eamonn O'brien-Strain

Examples disclosed herein relate to detecting misalignment of a touch sensitive mat. Examples include detecting corners of the touch sensitive mat, determining a set of reference corners, performing a comparison of the detected corners of the mat with the set of reference corners, and determining a level of misalignment based on the comparison.

Misalignment detection

A method includes receiving data representing an image captured of an object disposed on a surface in the presence of illumination by a flash light. The technique includes processing the data to identify an object type associated with the object and further processing the data based at least in part on the identified object type.

Processing data representing images of objects to classify the objects

Examples disclosed herein relate to identifying a target touch region of a touch-sensitive surface based on an image. Examples include a touch input detected at a location of a touch-sensitive surface, an image representing an object disposed between a camera that captures the image and the touch-sensitive surface, identifying a target touch region of a touch-sensitive surface based on an image, and rejecting the detected touch input when the location of the detected touch input is not within any of the at least one identified target touch region of the touch-sensitive surface.

Identifying a target touch region of a touch-sensitive surface based on an image

An example processor-implemented method for generating corners of a display area is provided. The method comprises detecting a dominant line for each side of the display area, each dominant line used to identify corners, detecting subline segments on each side of the display area, determining a distance between the corners identified by the dominant lines and the sub-line segments on each side, and generating the corners of the display area based on the distance.

Corner generation in a projector display area

Examples disclosed herein relate to aligning content displayed from a projector on to a touch sensitive mat. Examples include detecting a border of the mat, wherein the mat includes a surface area of a first spectral reflectance characteristic on to which the projector is to project the content, and the border of a second spectral reflectance characteristic different from the first spectral reflectance characteristic surrounding a perimeter of the surface area. As an example, detecting the border of the mat generally includes differentiating the second spectral reflectance characteristic of the border from the first spectral reflectance characteristic of the surface area. Examples include detecting a border of the content displayed on to the mat, and adjusting projector settings for the border of the content displayed on to the mat to fit within the detected border of the mat.

Touch sensitive mat of a system with a projector unit

According to one example for segmenting image data, image data comprising color pixel data, IR data, and depth data is received from a sensor. The image data is segmented into a first list of objects based on at least one computed feature of the image data. At least one object type is determined for at least one object in the first list of objects. The segmentation of the first list of objects is refined into a second list of objects based on the at least one object type. In an example, the second list of objects is output.

Image data segmentation

Examples disclosed herein relate to determining a segmentation boundary based on images representing an object. Examples include an IR image based on IR light reflected by an object disposed between an IR camera and an IR-absorbing surface, a color image representing the object disposed between the color camera and the IR-absorbing surface, and determining a segmentation boundary for the object.

Determining a segmentation boundary based on images representing an object

In one implementation, a system for using a scan recommendation includes a receiver engine to receive a plurality of pictures of a three-dimensional (3D) object from a scanner, a model engine to generate a 3D model of the 3D object by aligning the plurality of pictures of the 3D object, an analysis engine to analyze the 3D model for a volume, a shape, and a color of the 3D object, wherein the volume, the shape, and the color analysis is used to generate scan recommendations, and a display engine to display information relating to the scan recommendations based on the volume, the shape, and the color analysis of the 3D model of the 3D object.

Scan recommendations

One example provides a system. The system receives an infrared image and processes the infrared image to detect lines in the infrared image. The system receives a color image corresponding to the infrared image and processes the color image to detect lines in the color image. The detected lines in the infrared image and the detected lines in the color image are combined. A document region is detected from the combined detected lines.

Document region detection

According to an example, a method for object segmentation may include receiving a digital image, performing initial segmentation on the digital image to generate a segmented digital image, and receiving refinement instructions to refine the initial segmentation. The method may further include inferring an intention of a user to correct a foreground area or a background area of the initial segmentation based on the received refinement instructions, learning a behavior of the user to further infer the intention of the user to correct the foreground area or the background area, and refining, by a processor, the initial segmentation based on the inferred intention.

Object segmentation

Embodiments disclosed herein relate to image prioritization in a collage shape. In one embodiment, image positions are determined within a collage shape based on an importance level map of the collage shape and priorities of the images to be positioned within the collage shape. A collage may be created with the collage images in the determined positions.

Image prioritization in a collage shape

A method and system for clustering images, comprising detecting faces in respective images to generate face data, generating a first cluster of images using the face data, the first cluster comprising images with a common face and representing a corresponding person, generating a second cluster of images using clothes data representing a set of clothing signatures, the second cluster comprising images with a common set of clothes, and using the first and second clusters to link an image of the person to the clusters in order to generate a third cluster for the person.

Clustering images

Information relating to an exclusion zone to be provided on a page is received (), where the exclusion zone defines a corresponding area of the page that is free of content to be inserted in the page. A layout defining plural partitions on the page is produced (), where the layout includes the exclusion zone and the content having a plurality of elements that are inserted by recursively defining the plural partitions on the page, where the exclusion zone and content elements are provided in respective ones of the plural partitions, and where recursively defining the plural partitions considers presence of the exclusion zone.

Inserting content and exclusion zone(s) into a page

A method performed by a physical computing system for determining a layout of graphic objects () according to a layout density map () includes generating the layout density map (), the layout density map () having low density regions () and high density regions (); providing a set of candidate locations () within the layout density map (), a number of the candidate locations () being equal to a number of the graphic objects (), the candidate locations () being arranged away from the low density regions () and from other the candidate locations (); and overlaying the layout density map () with the graphic objects (), each graphic object () being disposed at one of the candidate locations ().

Determining a layout of graphic objects according to a layout density map

The disclosure relates to a system and a method for generating clothing feature data representative of at least one clothing feature of a piece of clothing being worn by the person in a set of images, and training a discriminative clothing classifier using the clothing feature data to provide a personal clothing model that corresponds to the piece of clothing. The personal clothing model can be used to identify additional images in which the person appears.

Image clustering using a personal clothing model

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San Jose, CA, United States of America

Daniel R Tretter