Thomson Licensing S.a.

Thomson Licensing

Grass Valley (u.s.) Inc.

Tut Systems, Inc.

Anil M Murching

Ali Jezu Tabatabai

Thumpudi Naveen

Naveen Thumpudi

Radu S Jasinschi

A method of coarse representation of a visual object's shape for search/query/filtering applications uses a binding box that fully encompasses the object of interest within the image to extract a feature vector. Once the feature vector is available, matching based on specific queries may be performed using a search engine to compare the query number to an appropriate element of the feature vector, performing sorting to pick the best matches.

Coarse representation of visual object's shape for search/query/filtering applications

A method of extracting regions of homogeneous color from a digital picture divides the digital picture into blocks and generates a feature vector for each block as a set of moments of the data for the block. The distance between the feature vector of each block and the feature vectors of the nearest neighboring blocks are determined using either a weighted Euclidean distance metric or a probability mass function-based distance metric. The maximum distance is the gradient value for the block, and the set of gradient values over all the blocks form a color gradient field. The gradient field is digitized and smoothed, and then segmented into regions of similar color characteristics using a watershed algorithm.

Process to extract regions of homogeneous color in a digital picture

A semi-automatic method of tracking color objects in a video image sequence starts by separating the objects on the basis of color and identifying an object of interest to track. A Kalman predictive algotithm in used to predict the position of the centroid of the object of interest through successive frames. From the predicted position the actual centroid is measured and the position and velocity are smoothed using a Kalman filter. Error recovery is provided in the event the centroid falls outside the field of view or falls into an area of a different color, or in the event the tracking algorithm breaks down.

Kalman tracking of color objects

A method of generating normalized bitmap representation for the shape of a visual object for use in search/query/filtering applications segments an image into visual objects. The samples belonging to a visual object of interest are identified. The identified samples that form the largest connected blob are reduced to an un-normalized bitmap. The un-normalized bitmap is then normalized using the mean and covariance of the valid samples to generate the normalized bitmap representation having a standard height and having an orientation such that a principal direction is along a vertical direction. The normalized bitmap representation may be used with a query to search a database of images where the visual objects all have associated normalized bitmap representations. The query bitmap is normalized and matched to each normalized bitmap representation. The visual objects having the lowest mismatch values of their normalized bitmap representation with the query bitmap are identified as the objects of the search.

Normalized bitmap representation of visual object's shape for search/query/filtering applications

A method of extracting regions of homogeneous texture in a digital picture divides the digital picture into blocks, and for each block generates a feature vector as a function of the data moments. From the feature vectors a gradient for each block is extracted in one of two ways, either using a weighted Euclidean distance between the feature vectors or a probability mass function-based distance metric. The gradients are submitted to morphological preprocessing to remove small bumps in the gradient field. A watershed algorithm is then applied to the preprocessed gradient field to segment the gradient field into a set of spatially connected regions of homogeneous texture.

Process to extract regions of homogeneous texture in a digital picture

A method of refining a pixel-based segmentation mask derived by upsampling a block-based segmentation mask for an image having multiple object classes determines a likelihood that each pixel in the pixel-based segmentation mask exists in each object class. Boundary pixels between bordering classes are extracted and processed to find a true class for each boundary pixel. The pixel-based segmentation mask is then updated with the true class for each of the boundary pixels to produce a smoothed pixel-based segmentation mask.

Refining a pixel-based segmentation mask derived by unsampling a block-based segmentation mask

A histogram-based segmentation of an image, frame or picture of a video signal into objects via color moments is initiated by defining a relatively large area within the object. The defined area is characterized by its color information in the form of a limited set of color moments representing a color histogram for the area. Based upon the set of color moments, color moments generated for small candidate blocks within the image, an automatically generated weighting vector, distance measures for the blocks from a central block in the object and a tolerance the area is grown to encompass the object to the extent of its boundaries. The initial set of color moments are then updated for the entire object. Those candidate blocks within the object serve to segment the object from the image.

Histogram-based segmentation of objects from a video signal via color moments

A histogram-based segmentation of images in a video signal via color moments is initialized by a user defining regions in objects of interest from one or more images, key frames or pictures of the video signal. For each rectangle a normalized average color moment and associated co-variance matrix are determined which define a color class for that rectangle. From the normalized average color moment and associated co-variance garbage parameters are generated. Segmentation is then performed on a block basis on each image of the video sequence, a normalized color moment being generated for each block. Using a log likelihood test the closest color class for the block is determined. Based upon the closest color class and the garbage parameters for that color class a final determination is made in a two stage test as to whether the block belongs to the closest class or to a &ldquo;garbage&rdquo; class. All the continguous blocks that belong to a specific color class form the segmented object, and all of the objects are segmented in this manner.

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Beaverton, OR, United States of America

Anil M Murching