Abstract not found

Anaglyph 3D images encode the left and right views of stereo 3D images using separate color channels (usually red and cyan): There’s a lot of existing anaglyph imagery, for example: We want to convert existing anaglyph videos to the full-color stereo format used by modern displays and projectors

2. IMAGE MATCHING BACKROUND......................................................................... 2

We present a new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto

Abstract. We present the problem of Oblivious Image Matching, where two parties want to determine whether they have images of the same object or scene, without revealing any additional information. While image matching has attracted a great deal of attention in the computer vision community

robust to these issues. We improve upon these using a probabilistic formulation for image matching in terms of maximum-likelihood estimation that can be used for both edge template matching and gray-level image matching. This formulation generalizes previous edge matching methods based on distance

for correspondences – generally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space. This “pyramid match” computation is linear

The problem of determining feature correspondences across multiple views is considered. The term "true multi-image " matching is introduced to describe techniques that make full and efficient use of the geometric relationships between multiple images and the scene. A true multi-image

Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup

The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries