University of Illinois

Rockwell Science Center, Inc.

Lucent Technologies Inc.

Thomas S Huang

Homer H Chen

Hai Tao

Arun N Netravali

Ziyou Xiong

Alfred M Bruckstein

Tsae-Pyng Janice Shen

Wei Wu

Robert J Holt

Jilin Tu

Sylvian R Ray

Makoto Yoshida

Thomas J Anastasio

A method for detecting an object in an image includes calculating a log L likelihood of pairs of pixels at select positions in the image that are derived from training images. The calculated log L likelihood of the pairs of pixels is compared with a threshold value. The object is detected when the calculated log L likelihood is greater than the threshold value.

Method for detecting objects in an image using pair-wise pixel discriminative features

A method for performing shape localization in an image includes deriving a model shape from a database of a plurality of sample shapes. The model shape is defined by a set of landmarks. The method further includes deriving a texture likelihood model of present sub-patches of the set of landmarks defining the model shape in the image, and proposing a new set of landmarks that approximates a true location of features of the shape based on a sample proposal model of the present sub-patches. A CONDENSATION algorithm is used to derive the texture likelihood model and the proposed new set of landmarks.

Method of performing shape localization

A method of determining spatial target probability using a model of multisensory processing by the brain includes acquiring at least two inputs from a location in a desired environment where a first target is detected, and applying the inputs to a plurality of model units in a map corresponding to a plurality of locations in the environment. A posterior probability of the first target at each of the model units is approximated, and a model unit with a highest posterior probability is found. A location in the environment corresponding to the model unit with a highest posterior probability is chosen as the location of the next target.

Method for determination of spatial target probability using a model of multisensory processing by the brain

A FAP coding technique that realizes enough coding gain to transmit multiple synthetic talking heads over a band limited channel without introducing perceptible artifacts into the reconstructed synthetic talking heads. This is accomplished by exploiting the spatial correlation of each FAP frame and/or the temporal correlation of the sequence of FAP frames. To remove intra-frame correlation, each FAP frame is transformed prior to segmentation from the n-dimensional space into an m-dimensional subspace where m&lt;n using an energy compaction transform. To remove inter-frame redundancy, the sequence is segmented and each parameter vector is transform coded to decorrelate the vector.

Coding of facial animation parameters (FAPs) for transmission of

Pose estimation of a camera is facilitated by including in the scene a space fiducial, an object that has the property that its appearance as viewed by the camera uniquely determines the direction from which it is viewed.

Process for pose estimation of a camera viewing an image scene

Data which represents an animation sequence of a three dimensional object at a series of discrete time frames is compressed by identifying characteristic features of the object; generating a quadrangular mesh representation of the object, whereby the object is mathematically defined by dividing it into one or more regions and hierarchically representing each region by a mesh, each mesh including three coordinate matrices which define the positions of nodes within the mesh; selecting from the mesh representation of the features a set of animation parameters which are capable of specifying changes in the mesh corresponding to the animation of the object; compressing each region mesh by applying pyramid progressive coding to the coordinate matrices for the mesh; storing the initial values for the animation parameters at the beginning of the animation sequence; and, at each time frame after the beginning of the animation sequence, estimating the current values of the parameters and compressing each parameter by estimating the change in the value of the parameter by subtracting its stored value for the previous time frame from its current value, quantizing the estimated difference, applying entropy coding to the quantized difference and updating the stored value with the decoded value.

Growing community of inventors

Urbana, IL, United States of America

Thomas S Huang