Reducing blurring-effect in high resolution mosaic generation

Pixel-based and motion vector-based global motion estimation (GME) techniques are evaluated in this paper with an automatic system for camera motion characterization. First, the GME techniques are compared with a frame-by-frame PNSR measurement using five video sequences. The best motion vector-based GME method is then evaluated together with a common and a simplified pixel-based GME technique for camera motion characterization. For this, selected unedited videos from the TRECVid 2005 BBC rushes corpus are used. We evaluate how the estimation accuracy of global motion parameters affects the results for camera motion characterization in terms of retrieval measures. The results for this characterization show that the simplified pixel-based GME technique obtains results that are comparable with the common pixel-based GME method, and outperforms significantly the results of an earlier proposed motion vector-based GME approach.

The growing use of cameras embedded in autonomous robotic platforms and worn by people is increasing the importance of accurate global motion estimation (GME). However, existing GME methods may degrade considerably under illumination variations. In this paper, we address this problem by proposing a biologically-inspired GME method that achieves high estimation accuracy in the presence of illumination variations. We mimic the early layers of the human visual cortex with the spatio-temporal Gabor motion energy by adopting the pioneering model of Adelson and Bergen and we provide the closed-form expressions that enable the study and adaptation of this model to different application needs. Moreover, we propose a normalisation scheme for motion energy to tackle temporal illumination variations. Finally, we provide an overall GME scheme which, to the best of our knowledge, achieves the highest accuracy on the Pose, Illumination, and Expression (PIE) database.

We present an algorithm for face 3D motion estimation in videoconference scenes. The algorithm uses a modification of the CANDIDE face model and is based on feature tracking and the extended Kalman filter (EKF). Various techniques are adopted to increase the robustness of the feature tracking procedure and, in particular, a filtering technique on reference blocks. Global motion estimation is used as a starting point for local motion detection. To this purpose, we generate, by texture mapping, a synthetic image of the mouth, whose shape is changed using a set of action units (AU). The optimal AU values are determined via a gradient-based minimization procedure of the error energy between the template and the actual mouth image. The proposed scheme is quite robust and was tested with success on long sequences.

In this paper, an efficient and fast global motion estimation method is proposed. This method is a generic one that can accommodate various global motion models, ranging from a simple translation model to an eight parameters perspective model. At the first stage, a simple filter is implemented to classify different parts of the frames. Since pixels from homogeneous regions and highly textured regions do not contribute much in global motion estimation, it is proposed that they-be excluded from processing. The remained pixels are fed into a three-level hierarchical motion estimation system and a dynamic sprite is eventually generated. From the experimental results, we can conclude that the proposed global motion estimation system manages to speed up the processing time for sprite generation while maintaining quality in the reconstructed frames.

Most global motion estimation (GME) methods are oriented to video coding while video object segmentation methods either assume no global motion (GM) or directly adopt a coding-oriented method to compensate for GM. This paper proposes a hierarchical differential GME method oriented to video object segmentation. A scheme which combines three-step search and motion parameters prediction is proposed for initial estimation to increase efficiency. A robust estimator that uses object information to reject outliers introduced by local motion is also proposed. For the first frame, when the object information is unavailable, a robust estimator is proposed which rejects outliers by examining their distribution in local neighborhoods of the error between the current and the motion-compensated previous frame. Subjective and objective results show that the proposed method is more robust, more oriented to video object segmentation, and faster than the referenced methods.

This paper presents an image-based velocity control method for a VTOL UAV. For increased autonomy, a realtime embedded global motion estimation process using efficient feature tracking was developed. The VTOL vehicle is assumed to be equipped with a minimum sensor suite — i.e. a camera, altitude sensor and IMU — and to hover over a featured flat plane. This paper first presents a fast feature-based global motion estimation method, and then proposes a control strategy based on this method. Finally, experimental results using a quadrotor demonstrate the performance of the proposed control strategy and feature-based motion estimation.

Sprite coding is a very efficient method for the representation and compression of background video object, which has been accepted by MPEG-4 standard. However, how to automatically generate sprite image from raw video sequence is still an open issue, because the motion of foreground video objects not only disturbs the accuracy of motion estimation but also blurs the generated sprite image. This paper presents a novel technique for the generation of background sprite image with improved global motion estimation (GME) and automatic segmentation. Since GME plays an important role in sprite generation, a hybrid scheme jointing the short-term and long-term motion estimation techniques is first developed in this paper. Automatic video segmentation based on available motion information is incorporated into sprite generation, which can mask moving foreground objects and meanwhile protect background sprite from blur. Therefore, the proposed technique is used to construct high quality sprite directly from a raw video sequence. Furthermore, with this technique the existing sprite coding can be extended to the field of frame-based coding.

Two-dimensional echocardiography continues to be the most widely used modality for the assessment of cardiac function due to its effectiveness, ease of use, and low costs. Echocardiographic images are derived from the mechanical interaction between the ultrasound field and the contractile heart tissue. Previously, in [6], based on B-mode echocardiographic simulations, we showed that motion estimation errors are significantly higher in shift-varying simulations when compared to shift-invariant simulations. In order to ascertain the effect of the spatial variance of the Ultrasonic field point spread function (PSF) and the transducer geometry on motion estimation, in the current paper, several simple canonical cardiac motions such as translation in axial and horizontal direction, and out-of-plane motion were simulated and the motion estimation errors were calculated. For axial motions, the greatest angular errors occurred within the lateral regions of the image, irrespective of the motion estimation technique that was adopted. We hypothesize that the transducer geometry and the PSF spatial-variance were the underlying sources of error for the motion estimation methods. No similar conclusions could be made regarding motion estimation errors for azimuthal and out-of-plane ultrasound simulations.

In order to improve the real-time of global motion estimation, we proposed a adaptive global motion estimation method based on improved SUSAN algorithm and SIFT algorithm. According to the five matching results before the current matching, the method uses Kalman filter algorithm to predict overlap regions of the current matching two images, and then extracts feature points in the overlapping regions instead of the whole regions of the images. In the part of extracting feature points of the method, improving the SUSAN algorithm according to the geometric characteristics of the SUSAN templates, which improves the speed of extracting feature points. Writing code to implement the method in VS2008, and experimental verification: This method accelerates the executing speed of the algorithm ensuring the accuracy at the same time.

One important recent application of image registration has been in the estimation of global motion parameters for object-based video coding. A limitation of current global motion estimation approaches is the additional complexity of the gradient-descent optimization that is typically required to calculate the optimal set of global motion parameters. In this paper we propose a new low-complexity algorithm for global motion estimation. The complexity of the proposed algorithm is reduced by performing the majority of the operations in the gradient-descent optimization using logic operations rather than full-precision arithmetic operations. This use of logic operations means that the algorithm can be implemented much more easily in hardware platforms such as field programmable gate arrays (FPGAs). Experimental results show that the execution time for software implementations of the new algorithm is reduced by a factor of almost four when compared to existing fast implementations without any significant loss in registration accuracy.

This paper investigates a novel motion vector outlier rejection method based on using mean shift clustering on block motion vectors. The accuracy of compressed domain global motion estimation techniques is largely influenced by its ability to counter the outlier motion vectors. These outliers occur in the block motion vector field due to moving objects, noise or due to large matching errors as a result of the encoders priority on rate distortion optimization. In the present work it is shown that by using mean shift clustering on block motion vectors, those clusters which correspond to outlier motion vectors can be identified. Once detected these clusters are kept out of the global motion estimation process thereby increasing the robustness of estimated camera parameters. The proposed method is compared with existing state-of-the-art outlier removal methods using synthetic and real video sequences to establish and validate its superiority.

Global motion estimation (GME) and compensation is one of the key modules in MPEG-4 Advanced Simple Profile (ASP). However, there are no hardware architectures for GME since existing algorithms are not suitable for hardware implementation. In this paper, GME in MPEG-4 ASP is analyzed, and a hardware-oriented GME algorithm is proposed according to the analysis results, which is a combination of a feature points based algorithm and a novel iterative sprite point matching algorithm. The associated hardware architecture is also proposed. Simulation results show that the performance of the proposed algorithm is the same as that of MPEG-4 Verification Model. The implementation results show the proposed hardware architecture can achieve the requirements of MPEG-4 ASP@L3 with 66K gates and 31Kb internal memory at 100 MHz working frequency.

Summary form only given. A sprite which is an image composed of pixels belonging to a video object visible throughout a video segment is a very important concept proposed by MPEG4. Because of the search region limitation in the global motion estimation, the performance of traditional sprite coding technology is not satisfactory in the case of fast camera motion. Only enlarging the search region is difficult to ensure the right motion estimation. An improved algorithm is proposed with enlarging the search region, predicting the motion of the current VOP (video object plane) and shortening the iterative time. Three main techniques are adopted in the new algorithm. They are: (1) enlarging search region; (2) weighting sum of absolute difference (SAD); and (3) shortening iteration. Two group experiments present a comparison between the original algorithm and the improved algorithm. The first group experiment shows the improved algorithm gets the same performance as the original algorithm in coding the general motion sequences. In the second group experiment, the Stefan background sequences which frame rates of 15 Hz, 10 Hz, 7.5 Hz, and 6 Hz are encoded in various transformations. The results given in a table show the coding performances of the algorithm are significantly improved.

Global motion generally describes the motion of the camera, although it may comprise large object motion. The region of support for global motion representation consists of the entire image frame. Therefore, estimating global motion parameters tends to be computationally costly due to the involvement of all the pixels in the calculation. Efficient global motion estimation (GME) techniques are sought after in many applications such as video coding, image stabilization and super-resolution. In this paper, we propose to select only a small subset of the pixels in estimating the global motion parameters, based on a combination of fixed and random subsampling patterns. Simulation results demonstrate that the proposed method was able to speed up the conventional all-pixel GME approach by up to 7 times, without significant loss in the estimation accuracy. The combined subsampling patterns were also found to provide better motion estimation accuracy/complexity tradeoffs than those achievable by using either fixed or random patterns alone.

This paper proposes a two-stage global motion estimation method using a novel quadtree block-based motion estimation technique and an active mesh model. In the first stage, motion parameters are estimated by fitting block-based motion vectors computed using a new efficient quadtree technique, that divides a frame into equilateral triangle blocks using the quad-tree structure. Arbitrary partition shapes are achieved by allowing 4-to-1, 3-to-1 and 2-1 merge/combine of sibling blocks having the same motion vector. In the second stage, the mesh is constructed using an adaptive triangulation procedure that places more triangles over areas with high motion content; these areas are estimated during the first stage. Finally the motion compensation is achieved by using a novel algorithm that is carried by both the encoder and the decoder to determine the optimal triangulation of the resultant partitions followed by affine mapping at the encoder. Computer simulation results show that the proposed method gives better performance that the conventional ones in terms of the peak signal-to-noise ratio (PSNR) and the compression ratio (CR).