Vania V. Estrela

Spatially-adaptive regularized pel-recursive motion estimation based on cross-validation

Pel-recursive motion estimation is a well established method for finding the displacement vector-field (DVF) between adjacent image frames. The motion due to the optical flow in image sequences is estimated recursively. In this paper, we improve the Wiener-based pel-recursive algorithm by using spatially-adaptive regularization. The outcome of the regularized solution is dependent upon the value of the regularization parameter. This work employs a data-driven approach called generalized cross-validation (GCV) to determine the optimal value of the regularization parameter for each pixel. Experimental results are presented and the linear minimum mean-squared (LMMSE) solution (also known as the Wiener solution) is compared to the proposed approach.

EM-Based Mixture Models Applied to Video Event Detection

Surveillance system (SS) development requires hi-tech support to prevail over the shortcomings related to the massive quantity of visual information from SSs. Anything but reduced human monitoring became impossible by means of its physical and economic implications, and an advance towards an automated surveillance becomes the only way out. When it comes to a computer vision system, automatic video event comprehension is a challenging task due to motion clutter, event understanding under complex scenes, multilevel semantic event inference, contextualization of events and views obtained from multiple cameras, unevenness of motion scales, shape changes, occlusions and object interactions among lots of other impairments. In recent years, state-of-the-art models for video event classification and recognition include modeling events to discern context, detecting incidents with only one camera, low-level feature extraction and description, high-level semantic event classification, and recognition. Even so, it is still very burdensome to recuperate or label a specific video part relying solely on its content. Principal component analysis (PCA) has been widely known and used, but when combined with other techniques such as the expectation-maximization (EM) algorithm its computation becomes more efficient. This chapter introduces advances associated with the concept of Probabilistic PCA (PPCA) analysis of video event and it also aims at looking closely to ways and metrics to evaluate these less intensive EM implementations of PCA and KPCA.

Sub-pixel accuracy edge fitting by means of B-spline

Local perturbations around contours strongly disturb the final result of computer vision tasks. It is common to introduce a priori information in the estimation process. Improvement can be achieved via a deformable model such as the snake model. In recent works, the deformable contour is modeled by means of B-spline snakes which allows local control, concise representation, and the use of fewer parameters. The estimation of the sub-pixel edges using a global B-spline model relies on the contour global determination according to a Maximum Likelihood framework and using the observed data likelihood. This procedure guarantees that the noisiest data will be filtered out. The data likelihood is computed as a consequence of the observation model which includes both orientation and position information. Comparative experiments of this algorithm and the classical spline interpolation have shown that the proposed algorithm outperforms the classical approach for Gaussian and Salt & Pepper noise.

Regularized pel-recursive motion estimation using generalized cross-validation and spatial adaptation

The computation of 2D optical flow by means of regularized pel-recursive algorithms raises a host of issues, which include the treatment of outliers, motion discontinuities and occlusion among other problems. We propose a new approach which allows us to deal with these issues within a common framework. Our approach is based on the use of a technique called generalized cross-validation to estimate the best regularization scheme for a given pixel. In our model, the regularization parameter is a matrix whose entries can account for diverse sources of error. The estimation of the motion vectors takes into consideration local properties of the image following a spatially adaptive approach where each moving pixel is supposed to have its own regularization matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.

Error concealment by means of clustered blockwise PCA

This paper analyzes two variants of Principal Component Analysis (PCA) for error-concealment: blockwise PCA and clustered blockwise PCA. Realistic communication channels are not error free. Since the signals transmitted on real-world channels are highly compressed, regardless of cause, the quality of images reconstructed from any corrupted data can be very unsatisfactory. Error concealment is intended to ameliorate the impact of channel impairments by utilizing a priori information about typical images in conjunction with available picture redundancy to provide subjectively acceptable renditions of affected picture regions. Some experiments have been performed with the two proposed algorithms and they are shown.

Spatially adaptive regularized pel-recursive motion estimation based on the EM algorithm

Pel-recursive motion estimation is a well-established approach for motion estimation. However, in the presence of noise, it becomes an il-posed problem that requires regulation. In the past, regularization for pel-recursive estimations was addressed in an ad-hoc manner. In this paper, a Bayesian estimation framework is used to deal with this issue. More specifically, motion vectors and regularization parameters are estimated in an iterative fashion by means of the Expectation- Maximization (EM) algorithm and a Gaussian data model. The proposed algorithm utilizes the local image properties to regularize the motion vector estimates following a spatially adaptive approach. Numerical experiments are presented that demonstrate the merits of the proposed algorithm.

A Study on the Effect of Regularization Matrices in Motion Estimation

Inverse problems are very frequent in computer vision and machine learning applications. Since noteworthy hints can be obtained from motion data, it is important to seek more robust models. The advantages of using a more general regularization matrix such as Λ=diag{λ1,…,λ K } to robustify motion estimation instead of a single parameter λ (Λ=λ I ) are investigated and formally stated in this paper, for the optical flow problem. Intuitively, this regularization scheme makes sense, but it is not common to encounter high-quality explanations from the engineering point of view. The study is further confirmed by experimental results and compared to the nonregularized Wiener filter approach.

On improving sub-pixel accuracy by means of B-spline

Local perturbations nearby contours strongly perturb the final result of processing remotely sensed images (RSI). It is common to establish a priori data to aid the estimation process. One can move some steps forward by means of a deformable model, for example, the snake model. In up to date research, the deformable contour is represented via B-spline snakes, which allows local control, concise depiction, and the use of fewer parameters. The estimation of edges with sub-pixel accuracy via a global B-spline depiction depends on determining the edge according to a Maximum Likelihood (ML) agenda and using the observed information likelihood. This practice guarantees that outliers present in data will be cleaned out. The data likelihood is calculated as a result of the observation model comprising both orientation and position data. Experiments where this procedure and the traditional spline interpolation have revealed that the algorithm introduced outperforms the conventional method for Gaussian as well as Salt and Pepper noise.

State-of-the Art Motion Estimation in the Context of 3D TV

Progress in image sensors and computation power has fueled studies to improve acquisition, processing, and analysis of 3D streams along with 3D scenes/objects reconstruction. The role of motion compensation/motion estimation (MCME) in 3D TV from end-to-end user is investigated in this chapter. Motion vectors (MVs) are closely related to the concept of disparities, and they can help improving dynamic scene acquisition, content creation, 2D to 3D conversion, compression coding, decompression/decoding, scene rendering, error concealment, virtual/augmented reality handling, intelligent content retrieval, and displaying. Although there are different 3D shape extraction methods, this chapter focuses mostly on shape-from-motion (SfM) techniques due to their relevance to 3D TV. SfM extraction can restore 3D shape information from a single camera data.

Error concealment by means of motion refinement and regularized bregman divergence

This work addresses the problem of error concealment in video transmission systems over noisy channels employing Bregman divergences along with regularization. Error concealment intends to improve the effects of disturbances at the reception due to bit-errors or cell loss in packet networks. Bregman regularization gives accurate answers after just some iterations with fast convergence, better accuracy and stability. This technique has an adaptive nature: the regularization functional is updated according to Bregman functions that change from iteration to iteration according to the nature of the neighborhood under study at iteration n. Numerical experiments show that high-quality regularization parameter estimates can be obtained. The convergence is sped up while turning the regularization parameter estimation less empiric, and more automatic.