Abstract: A local bi-gram model object recognition system and method for constructing a local bi-gram model and using the model to recognize objects in a query image. In a learning phase, the local bi-gram model is constructed that represents objects found in a set of training images. The local bi-gram model is a local spatial model that only models the relationship of neighboring features without any knowledge of their global context. Object recognition is performed by finding a set of matching primitives in the query image. A tree structure of matching primitives is generated and a search is performed to find a tree structure of matching primitives that obeys the local bi-gram model. The local bi-gram model can be found using unsupervised learning. The system and method also can be used to recognize objects unsupervised that are undergoing non-rigid transformations for both object instance recognition and category recognition.

Abstract: Described is a user interface that displays a representation of a stereo scene, and includes interactive mechanisms for changing parameter values that determine the perceived appearance of that scene. The scene is modeled as if viewed from above, including a representation of a viewer's eyes, a representation of a viewing screen, and an indication simulating what each of the viewer eyes perceives on the viewing screen. Variable parameters may include a vergence parameter, a dolly parameter, a field-of-view parameter, an interocular parameter and a proscenium arch parameter.

Abstract: Described is a technology in which video shots are clustered based upon the location at which the shots were captured. A global energy function is optimized, including a first term that computes clusters so as to be reasonably dense and well connected, to match the possible shots that are captured at a location, e.g., based on similarity scores between pairs of shots. A second term is a temporal prior that encourages subsequent shots to be placed in the same cluster. The shots may be represented as nodes of a minimum spanning tree having edges with weights that are based on the similarity score between the shots represented by their respective nodes. Agglomerative clustering is performed by selecting pairs of available clusters, merging the pairs and keeping the pair with the lowest cost. Clusters are iteratively merged until a stopping criterion or criteria is met (e.g., only a single cluster remains).

Abstract: The stereo movie editing technique described herein combines knowledge of both multi-view stereo algorithms and human depth perception. The technique creates a digital editor, specifically for stereographic cinema. The technique employs an interface that allows intuitive manipulation of the different parameters in a stereo movie setup, such as camera locations and screen position. Using the technique it is possible to reduce or enhance well-known stereo movie effects such as cardboarding and miniaturization. The technique also provides new editing techniques such as directing the user's attention and easier transitions between scenes.

Abstract: A system and process for compressing and decompressing multiple video streams depicting substantially the same dynamic scene from different viewpoints that from a grid of viewpoints. Each frame in each contemporaneous set of video frames of the multiple streams is represented by at least a two layers—a main layer and a boundary layer. Compression of the main layers involves first designating one or more of these layers in each set of contemporaneous frames as keyframes. For each set of contemporaneous frames in time sequence order, the main layer of each keyframe is compressed using an inter-frame compression technique. In addition, the main layer of each non-keyframe within the frame set under consideration is compressed using a spatial prediction compression technique. Finally, the boundary layers of each frame in the current frame set are each compressed using an intra-frame compression technique. Decompression is generally the reverse of the compression process.

Abstract: Described are techniques for image deconvolution to deblur an image given a blur kernel. Localized color statistics derived from the image to be deblurred serve as a prior constraint during deconvolution. A pixel's color is formulated as a linear combination of the two most prevalent colors within a neighborhood of the pixel. This may be repeated for many or all pixels in an image. The linear combinations of the pixels serve as a two-color prior for deconvolving the blurred image. The two-color prior is responsive to the content of the image and it may decouple edge sharpness from edge strength.

Abstract: A two-dimensional blur kernel is computed for a digital image by first estimating a sharp image from the digital image. The sharp image is derived from the digital image by sharpening at least portions of the digital image. The two-dimensional blur function is computed by minimizing an optimization algorithm that estimates the blur function.

Abstract: A local bi-gram model object recognition system and method for constructing a local bi-gram model and using the model to recognize objects in a query image. In a learning phase, the local bi-gram model is constructed that represents objects found in a set of training images. The local bi-gram model is a local spatial model that only models the relationship of neighboring features without any knowledge of their global context. Object recognition is performed by finding a set of matching primitives in the query image. A tree structure of matching primitives is generated and a search is performed to find a tree structure of matching primitives that obeys the local bi-gram model. The local bi-gram model can be found using unsupervised learning. The system and method also can be used to recognize objects unsupervised that are undergoing non-rigid transformations for both object instance recognition and category recognition.

Abstract: A content-based image retrieval technique for sharing image information between images. In the present image information sharing technique digital photographs containing relevant comments and links to web pages are supplied by a set of authors. A user of the image information sharing technique takes a digital picture of something that interests them, such as, for example, a poster, a product, a painting, and uploads it to an image information sharing server. The image information sharing technique then matches the photo to an image in an image information sharing database. The comments and web links associated with the matched photo are subsequently sent to the user.

Abstract: An image-wide matting technique that involves modeling an image using a layered representation is presented. This representation includes a main pixel color layer, a secondary pixel color layer, an alpha layer and a noise layer. The four-layer representation is generated using a statistical model. Once generated, this representation can be used advantageously in a number of image editing operations.

Abstract: An image editing technique employing a layered representation of the image is presented. The image representation includes a main pixel color layer, a secondary pixel color layer, an alpha layer and a noise layer. Generally, the pixel values of one or more pixel locations of one or more of the layers of the image representation are manipulated to effect a change. Once changed, the layers are combined to produce a revised image.

Abstract: A system and process for generating a two-layer, 3D representation of a digital or digitized image from the image and a pixel disparity map of the image is presented. The two layer representation includes a main layer having pixels exhibiting background colors and background disparities associated with correspondingly located pixels of depth discontinuity areas in the image, as well as pixels exhibiting colors and disparities associated with correspondingly located pixels of the image not found in these depth discontinuity areas. The other layer is a boundary layer made up of pixels exhibiting foreground colors, foreground disparities and alpha values associated with the correspondingly located pixels of the depth discontinuity areas. The depth discontinuity areas correspond to prescribed sized areas surrounding depth discontinuities found in the image using a disparity map thereof.