In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image-classification systems depend on the gray-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image-classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. locality-constrained linear coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image-classification system for the evaluation. Moreover, we propose a simple \(\ell _2\) -norm regularized local distance to improve the traditional LLC method. The real experiments are carried out on several benchmarks. With the enhancements to color SIFT and \(\ell _2\) -norm regularization, the proposed image-classification system obtains approximately \(2\,\%\) improvement of classification accuracy on the Caltech-101 dataset and approximately \(5\,\%\) improvement of classification accuracy on the Caltech-256 dataset.

Inserting synthetic objects into video sequences has gained much interest in recent years. Fast and robust vision-based algorithms are necessary to make such an application possible. Traditional pose tracking schemes using recursive structure from motion techniques adopt one Kalman filter and thus only favor a certain type of camera motion. We propose a robust simultaneous pose tracking and structure recovery algorithm using the interacting multiple model (IMM) to improve performance. In particular, a set of three extended Kalman filters (EKFs), each describing a frequently occurring camera motion in real situations (general, pure translation, pure rotation), is applied within the IMM framework to track the pose of a scene. Another set of EKFs,one filter for each model point, is used to refine the positions of the model features in the 3-D space. The filters for pose tracking and structure refinement are executed in an interleaved manner. The results are used for inserting virtual objects into the original video footage. The performance of the algorithm is demonstrated with both synthetic and real data. Comparisons with different approaches have been performed and show that our method is more efficient and accurate.

This paper presents a novel method that acquires camera position and orientation from a stereo image sequence without prior knowledge of the scene. To make the algorithm robust, the interacting multiple model probabilistic data association filter (IMMPDAF) is introduced. The interacting multiple model (IMM) technique allows the existence of more than one dynamic system in the filtering process and in return leads to improved accuracy and stability even under abrupt motion changes. The probabilistic data association (PDA) framework makes the automatic selection of measurement sets possible, resulting in enhanced robustness to occlusions and moving objects. In addition to the IMMPDAF, the trifocal tensor is employed in the computation so that the step of reconstructing the 3-D models can be eliminated. This further guarantees the precision of estimation and computation efficiency. Real stereo image sequences have been used to test the proposed method in the experiment. The recovered 3-D motions are accurate in comparison with the ground truth data and have been applied to control cameras in a virtual environment.