Koninklijke Philips Corporation N.V.

Vasanth Philomin

Srinivas Gutta

Miroslav Trajkovic

Yun-Ting Lin

Philip Steven Newton

Christiaan Varekamp

Wiebe De Haan

Wilhelmus Hendrikus Alfonsus Bruls

Johan Cornelis Talstra

Tomas Brodsky

Mi-Suen Lee

Christian Benien

Marc Helbing

Georgios Parlantzas

Eric Cohen-Solal

Antonio Colmenarez

Hugo Strubbe

Antonio J Colmenarez

Serhan Dagtas

Gerardus Wilhelmus Theodorus Van Der Heijden

Felix Gremse

Geradus Wilhelmus Theodorus Van Der Heijden

Maarten Tobias

Three dimensional [3D] image data and auxiliary graphical data are combined for rendering on a 3D display () by detecting depth values occurring in the 3D image data, and setting auxiliary depth values for the auxiliary graphical data () adaptively in dependence of the detected depth values. The 3D image data and the auxiliary graphical data at the auxiliary depth value are combined based on the depth values of the 3D image data. First an area of attention () in the 3D image data is detected. A depth pattern for the area of attention is determined, and the auxiliary depth values are set in dependence of the depth pattern.

Method and apparatus for combining 3D image and graphical data

Three dimensional [3D] image data and auxiliary graphical data are combined for rendering on a 3D display by detecting depth values occurring in the 3D image data and setting auxiliary depth values for the auxiliary graphical data adaptively in dependence of the detected depth values. The 3D image data and the auxiliary graphical data at the auxiliary depth value are combined based on the depth values of the 3D image data. First an area of attention in the 3D image data is detected. A depth pattern for the area of attention is determined, and the auxiliary depth values are set in dependence of the depth pattern.

Combining 3D image and graphical data

A three dimensional display system comprises a display () presenting a plurality of views of a scene where each view corresponds to a viewing direction for the scene. A rendering unit () generates an image for each of the views. The rendering unit () is capable of generating differentiated images for neighboring views containing differentiated image data providing a three dimensional depth effect when neighboring views are viewed by different eyes of a viewer. A user processor () generates a user presence estimate in response to an attempted detection of a user in a viewing area for the display system. A 3D mode controller () modifies the three dimensional effect in response to the user presence estimate. For example, the display system may provide a two dimensional image when no user is present and a three dimensional image when a user is present. This may reduce discomfort experienced by viewers in suboptimal positions.

Three dimensional display system

An image processing system () and method () are disclosed for correcting a head pose in a video phone image, so that a frontal view is presented on a display. A disclosed head pose corrector () estimates the orientation of a head pose and adjusts the orientation of the head pose, if necessary, to present a frontal view. The orientation of the head pose is adjusted by generating a three dimensional model of the face surface and adjusting the orientation of the three dimensional face model to provide the desired frontal view. The head pose corrector () may be included in a video phone () to correct the head pose of transmitted or received images (or both) or may be included in a server on a network to automatically adjust the head shots of one or more participants to a video phone communication.

Method and apparatus for correcting a head pose in a video phone image

A system and apparatus are disclosed for modeling patterns of behavior of humans or other animate objects and detecting a violation of a repetitive pattern of behavior. The behavior of one or more persons is observed over time and features of the behavior are recorded in a multi-dimensional space. Over time, the multi-dimensional data provides an indication of patterns of human behavior. Activities that are repetitive in terms of time, location and activity, such as sleeping and eating, would appear as a Gaussian distribution or cluster in the multi-dimensional data. Probability distribution functions can be analyzed using known Gaussian or clustering techniques to identify repetitive patterns of behavior and characteristics thereof, such as a mean and variance. Deviations from repetitive patterns of behavior can be detected and an alarm can be triggered, if appropriate.

Method and apparatus for detecting an event based on patterns of behavior

A system and method for classifying facial image data, the method comprising the steps of: training a classifier device for recognizing one or more facial images and obtaining corresponding learned models the facial images used for training; inputting a vector including data representing a portion of an unknown facial image to be recognized into the classifier; classifying the portion of the unknown facial image according to a classification method; repeating inputting and classifying steps using a different portion of the unknown facial image at each iteration; and, identifying a single class result from the different portions input to the classifier.

System and method of face recognition using proportions of learned model

A system and method wherein the parameters used for face recognition are determined via an evolutionary algorithm, such as a genetic algorithm. Candidate sets of parameters are evaluated for face recognition performance in a given environment. The most effective sets of parameters are used to generate offspring sets of parameters, and these offspring sets are subsequently evaluated for face recognition performance. This process is repeated, with each generation of offspring sets inheriting attributes of the most successful sets, until the system converges on a set of parameters that exhibits superior performance for the given environment. In like manner, other sets of parameters are determined for other environments. When the face recognition system is deployed, the appropriate set of parameters is used, dependent upon the environment in which the target images originate. In this manner, the system exhibits superior performance in each of the environments in which it is deployed.

Face recognition using evolutionary algorithms

A telephone user who is at a first telephone station and is placed on hold at a second station is prevented from listening to an objectionable audio signal while a call is in process between the user and the second telephone station. The objectionable audio signal is detected while the call is in process and the user is prevented from hearing the objectionable audio signal while the objectionable audio signal is being detected. In response to detection of the end of the objectionable audio signal, the user is coupled to the second telephone station.

Method of and apparatus wherein a telephone user on hold is not required to listen to an objectionable audio signal

A method and apparatus are disclosed for automatically learning and identifying events in image data using hierarchical HMMs to define and detect one or more events. The hierarchical HMMs include multiple paths that encompass variations of the same event. Hierarchical HMMs provide a framework for defining events that may be exhibited in various ways. Each event is modeled in the hierarchical HMM with a set of sequential states that describe the paths in a high-dimensional feature space. These models can then be used to analyze video sequences to segment and recognize each individual event to be recognized. The hierarchical HMM is generated during a training phase, by processing a number of images of the event of interest in various ways, typically observed from multiple viewpoints.

Video monitoring system employing hierarchical hidden markov model (HMM) event learning and classification

Linear method for performing head motion estimation from facial feature data, the method comprising the steps of: obtaining first facial image and detecting a head in the first image; detecting position of four points P of said first facial image where P={p, p, p, p}, and p=(x, y); obtaining second facial image and detecting a head in the second image; detecting position of four points P' of the second facial image where P&#x2032;={p&#x2032;, p&#x2032;, p&#x2032;, p&#x2032;} and p&#x2032;=(x&#x2032;, y&#x2032;); and, determining the motion of the head represented by a rotation matrix R and translation vector T using the points P and P&#x2032;. The head motion estimation is governed according to an equation:whereand T=[TTT]represents camera rotation and translation respectively.

Head motion estimation from four feature points

Method and system of detecting and analyzing the presence of a logo in one or more datastreams. In the method, at least one video datastream of an event is first received. Next, one or more regions of interest (ROIs) for the logo in one or more images comprising the at least one datastream are identified. The one or more ROIs are analyzed to detect if the logo is present in the ROI. If so, the detection of the presence of the logo is used in making either a broadcasting decision or an advertising decision.

Identification and evaluation of audience exposure to logos in a broadcast event

The learning rate used for updating the weights of a self-ordering feature map is determined by a process that injects some type of perturbation into the value so that it is not simply monotonically decreased with each training epoch. For example, the learning rate may be generated according to a pseudorandom process. The result is faster convergence of the synaptic weights.

Self-organizing feature map with improved performance by non-monotonic variation of the learning rate

An image processing system is disclosed that provides automatic face or skin blurring for images. All faces or skin can be blurred, or specific faces can be blurred. In one aspect of the invention, a particular face is blurred on an image or on a series of images in a video. Faces are determined in an image, and face matching is performed to match a particular face to faces in the image. If a match is found, the face or a portion of the face is blurred in the image. The blurring is performed on a portion of the image containing the particular face. Blurring may be performed through a variety of techniques. In another aspect of the invention, voice processing is used as an adjunct to or in place of face analysis to determine if a face in an image or series of images should be blurred. In another aspect of the invention, all faces or human skin in an image or series of images is blurred.

Method and apparatus for automatic face blurring

A system and method is disclosed for taking the results of a search (i.e., electronic documents) using the TFIDF of terms contained in those search result documents to quantify how related the search result documents are to one another, and organizing the search results based on those qualified relations. Using such a system and method, the search result documents can be displayed in such a manner that the relationships between the search result documents is evident to the viewer.

Method and apparatus for providing search results in response to an information search request

Cameras and image processing techniques are applied to the problem of spill detection. Fixed background data may be removed using a technique based on image subtraction. The remainder of one or more images may be segmented. The segments may then be tested to identify and remove from further analysis segments corresponding to non-spill foreground objects, for example by forming luminance and/or chrominance profiles to detect the presence of a high rate of specular albido that would be associated with a wet spill. Further testing may be done, for example by determining if a segment corresponds to a planar object lying in the plane of the floor using planar projection transforms and multiple cameras. If a segment is likely to indicate a spill, a notification may be generated.

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Hopewell Junction, NY, United States of America

Vasanth Philomin