In many video-based object recognition applications, the object appearances are acquired by visual tracking or detection and are inconsistent due to misalignments. We believe the misalignments can be removed if we can reduce the inconsistency in the object appearances caused by misalignments through clustering the objects in appearance, space and time domain simultaneously. We therefore propose to learn Transform Invariant Mixtures of Probabilistic PCA (TIMPPCA) model from the data while at the same time eliminating the misalignments. The model is formulated in a generative framework, and the misalignments are considered as hidden variables in the model. Variational EM update rules are then derived based on Variational Message Passing (VMP) techniques. The proposed TIMP-PCA is applied to improve head pose estimation performance and to detect the change of attention focus in meeting room video for meeting room video indexing/retrieval and achieves promising performance.

In this paper we present a generative model and learning procedure for unsupervised video clustering into scenes. The work addresses two important problems: realistic modeling of the sources of variability in the video and fast transformation invariant frame clustering. We suggest a solution to the problem of computationally intensive learning in this model by combining the recursive model estimation, fast inference, and on-line learning. Thus, we achieve real time frame clustering performance. Novel aspects of this method include an algorithm for the clustering of Gaussian mixtures, and the fast computation of the KL divergence between two mixtures of Gaussians. The efficiency and the performance of clustering and KL approximation methods are demonstrated. We also present novel video browsing tool based on the visualization of the variables in the generative model.

Forging the special documents such as driver's license, visa and passport, has reached unprecedented level in recent years. In order to stop such crimes, various methods have been presented. One typical way is to do human identification if face picture is present in the documents. But it’s often found that criminals modify the face picture in the documents, thus card validation purely based on face picture may also not be reliable. In this paper we present a novel method for such document authentication problem, we propose to do face image compression and store the data as barcode in an identification document, both of the barcode and original photo are printed in the document. By comparing the face image scanned from the document and the picture reconstructed from barcode, using different image processing methods to enhance the image and Gabor wavelet features, we can make a decision whether the document is valid or not. Index Terms -- Image enhancement, Identification, Data compression, Matching.

This paper presents a probabilistic framework for segmenting and tracking multiple non rigid foreground objects for video surveillance, using a static monocular camera. The algorithm combines information in a probabilistic sense and poses the problem of matching the segmented foreground objects with blobs in the next frame as a non bipartite matching problem. To solve this problem, probability is calculated for each possible matching. Initialization of new objects is also treated in a probabilistic manner. The new framework is shown to be able to handle a greater set of difficult situations and to improve performance significantly.

This paper extends our game-theoretic approach to design and embed watermarks in Gaussian signals in the presence of an adversary. The detector solves a binary hypothesis testing problem. The system is designed to minimize probability of error under the worst-case attack in a prescribed class of attacks. The embedder is allowed to filter the host signal and add a watermark, thereby making the scheme nonadditive. The theory is applied to wavelet-based image watermarking. We find that, in this framework, additive watermarks are clearly suboptimal.

This paper describes a game-theoretic methodology to design and embed watermarks in images. The optimality criterion is the decoder error probability. Analytical solutions are presented for problems involving Gaussian host images and illustrated with examples. Significant improvements over previous designs are obtained.

This paper develops an optimization methodology for designing and embedding synchronization patterns in images, for watermarking applications in which the host image is not available the decoder. Optimality is in the sense of a certain Fisher information game between the embedder and the attacker. Analytical solutions are derived for problems involving translation, rotation, linear filtering, and additive Gaussian noise attacks.