629 results about "Model image" patented technology

A method and system for processing captured image information in an interactive video display system. In one embodiment, a special learning condition of a captured camera image is detected. The captured camera image is compared to a normal background model image and to a second background model image, wherein the second background model is learned at a faster rate than the normal background model. A vision image is generated based on the comparisons. In another embodiment, an object in the captured image information that does not move for a predetermined time period is detected. A burn-in image comprising the object is generated, wherein the burn-in image is operable to allow a vision system of the interactive video display system to classify the object as background.

A face recognition system and process for identifying a person depicted in an input image and their face pose. This system and process entails locating and extracting face regions belonging to known people from a set of model images, and determining the face pose for each of the face regions extracted. All the extracted face regions are preprocessed by normalizing, cropping, categorizing and finally abstracting them. More specifically, the images are normalized and cropped to show only a person's face, categorized according to the face pose of the depicted person's face by assigning them to one of a series of face pose ranges, and abstracted preferably via an eigenface approach. The preprocessed face images are preferably used to train a neural network ensemble having a first stage made up of a bank of face recognition neural networks each of which is dedicated to a particular pose range, and a second stage constituting a single fusing neural network that is used to combine the outputs from each of the first stage neural networks. Once trained, the input of a face region which has been extracted from an input image and preprocessed (i.e., normalized, cropped and abstracted) will cause just one of the output units of the fusing portion of the neural network ensemble to become active. The active output unit indicates either the identify of the person whose face was extracted from the input image and the associated face pose, or that the identity of the person is unknown to the system.

A method and system for processing captured image information in an interactive video display system. In one embodiment, a special learning condition of a captured camera image is detected. The captured camera image is compared to a normal background model image and to a second background model image, wherein the second background model is learned at a faster rate than the normal background model. A vision image is generated based on the comparisons. In another embodiment, an object in the captured image information that does not move for a predetermined time period is detected. A burn-in image comprising the object is generated, wherein the burn-in image is operable to allow a vision system of the interactive video display system to classify the object as background.

A method of mapping geometrical objects to a digital image. At least one anchor point in a model is selected and mapped to the digital image. A geometric transformation is computed on the basis of at least one correspondence between an anchor object and a corresponding mapped object, and the calculated geometric transformation is applied to at least one geometric object thereby mapping it from the model image to a corresponding mapped position in the digital image.

A method of mapping geometrical objects to a digital image. At least one anchor point in a model is selected and mapped to the digital image. A geometric transformation is computed on the basis of at least one correspondence between an anchor object and a corresponding mapped object, and the calculated geometric transformation is applied to at least one geometric object thereby mapping it from the model image to a corresponding mapped position in the digital image.

In an image recognition apparatus (1), feature point extraction sections (10a) and (10b) extract feature points from a model image and an object image. Feature quantity retention sections (11a) and (11b) extract a feature quantity for each of the feature points and retain them along with positional information of the feature points. A feature quantity comparison section (12) compares the feature quantities with each other to calculate the similarity or the dissimilarity and generates a candidate-associated feature point pair having a high possibility of correspondence. A model attitude estimation section (13) repeats an operation of projecting an affine transformation parameter determined by three pairs randomly selected from the candidate-associated feature point pair group onto a parameter space. The model attitude estimation section (13) assumes each member in a cluster having the largest number of members formed in the parameter space to be an inlier. The model attitude estimation section (13) finds the affine transformation parameter according to the least squares estimation using the inlier and outputs a model attitude determined by this affine transformation parameter.

A face recognition system and process for identifying a person depicted in an input image and their face pose. This system and process entails locating and extracting face regions belonging to known people from a set of model images, and determining the face pose for each of the face regions extracted. All the extracted face regions are preprocessed by normalizing, cropping, categorizing and finally abstracting them. More specifically, the images are normalized and cropped to show only a persons face, categorized according to the face pose of the depicted person's face by assigning them to one of a series of face pose ranges, and abstracted preferably via an eigenface approach. The preprocessed face images are preferably used to train a neural network ensemble having a first stage made up of a bank of face recognition neural networks each of which is dedicated to a particular pose range, and a second stage constituting a single fusing neural network that is used to combine the outputs from each of the first stage neural networks. Once trained, the input of a face region which has been extracted from an input image and preprocessed (i.e., normalized, cropped and abstracted) will cause just one of the output units of the fusing portion of the neural network ensemble to become active. The active output unit indicates either the identify of the person whose face was extracted from the input image and the associated face pose, or that the identity of the person is unknown to the system.

A method for identifying lithographically significant defects. A photomask is illuminated to produce images that experience different parameters of the reticle as imaged by an inspection tool. Example parameters include a transmission intensity image and a reflection intensity image. The images are processed together to recover a band limited mask pattern associated with the photomask. A model of an exposure lithography system for chip fabrication is adapted to accommodate the band limited mask pattern as an input which is input into the model to obtain an aerial image of the mask pattern that is processed with a photoresist model yielding a resist-modeled image. The resist-modeled image is used to determine if the photomask has lithographically significant defects.

A method for detecting an object in a digital image including the steps of performing color quantization on a model image including the object and on a search image that potentially includes the object, generating a plurality of search windows, computing spatial-color joint probability functions of each model and search image, where the color co-occurrence edge histogram is chosen to be the spatial-color joint probability function, assessing the similarity of each search image to the model, and designating search windows as containing the target object.