Arquitetura com elevada taxa de processamento e reduzida largura de banda de memória para a estimação de movimento em vídeos digitais

Nowadays several electronics devices support digital videos. Some examples of these devices are cellphones, digital cameras, video cameras and digital televisions. However, raw videos present a huge amount of data, millions of bits, for their representation as the way they were captured. To store them in its primary form it would be necessary a huge amount of disk space and a huge bandwidth to allow the transmission of these data. The video compression becomes essential to make possible information storage and transmission. Motion Estimation is a technique used in the video coder that explores the temporal redundancy present in video sequences to reduce the amount of data necessary to represent the information. This work presents a hardware architecture of a motion estimation module for high resolution videos according to H.264/AVC standard. The H.264/AVC is the most advanced video coder standard, with several new features which allow it to achieve high compression rates. The architecture presented in this work was developed to provide a high data reuse. The data reuse schema adopted reduces the bandwidth required to execute motion estimation. The motion estimation is the task responsible for the largest share of the gains obtained with the H.264/AVC standard so this module is essential for final video coder performance. This work is included in Rede H.264 project which aims to develop Brazilian technology for Brazilian System of Digital Television

Projeto e implementação de uma plataforma MP-SoC usando SystemC

This work presents the concept, design and implementation of a MP-SoC platform, named STORM (MP-SoC DirecTory-Based PlatfORM). Currently the platform is composed of the following modules: SPARC V8 processor, GPOP processor, Cache module, Memory module, Directory module and two different modles of Network-on-Chip, NoCX4 and Obese Tree. All modules were implemented using SystemC, simulated and validated, individually or in group. The modules description is presented in details. For programming the platform in C it was implemented a SPARC assembler, fully compatible with gcc s generated assembly code. For the parallel programming it was implemented a library for mutex managing, using the due assembler s support. A total of 10 simulations of increasing complexity are presented for the validation of the presented concepts. The simulations include real parallel applications, such as matrix multiplication, Mergesort, KMP, Motion Estimation and DCT 2D

Implementation of a robust optical flow method for color images

We analyse the influence of colour information in optical flow methods. Typically, most of these techniques compute their solutions using grayscale intensities due to its simplicity and faster processing, ignoring the colour features. However, the current processing systems have minimized their computational cost and, on the other hand, it is reasonable to assume that a colour image offers more details from the scene which should facilitate finding better flow fields. The aim of this work is to determine if a multi-channel approach supposes a quite enough improvement to justify its use. In order to address this evaluation, we use a multi-channel implementation of a well-known TV-L1 method. Furthermore, we review the state-of-the-art in colour optical flow methods. In the experiments, we study various solutions using grayscale and RGB images from recent evaluation datasets to verify the colour benefits in motion estimation.

Visual Fixation for 3D Video Stabilization

Visual fixation is employed by humans and some animals to keep a specific 3D location at the center of the visual gaze. Inspired by this phenomenon in nature, this paper explores the idea to transfer this mechanism to the context of video stabilization for a handheld video camera. A novel approach is presented that stabilizes a video by fixating on automatically extracted 3D target points. This approach is different from existing automatic solutions that stabilize the video by smoothing. To determine the 3D target points, the recorded scene is analyzed with a stateof- the-art structure-from-motion algorithm, which estimates camera motion and reconstructs a 3D point cloud of the static scene objects. Special algorithms are presented that search either virtual or real 3D target points, which back-project close to the center of the image for as long a period of time as possible. The stabilization algorithm then transforms the original images of the sequence so that these 3D target points are kept exactly in the center of the image, which, in case of real 3D target points, produces a perfectly stable result at the image center. Furthermore, different methods of additional user interaction are investigated. It is shown that the stabilization process can easily be controlled and that it can be combined with state-of-theart tracking techniques in order to obtain a powerful image stabilization tool. The approach is evaluated on a variety of videos taken with a hand-held camera in natural scenes.

Generating Realistic Camera Shake for Virtual Scenes

When depicting both virtual and physical worlds, the viewer's impression of presence in these worlds is strongly linked to camera motion. Plausible and artist-controlled camera movement can substantially increase scene immersion. While physical camera motion exhibits subtle details of position, rotation, and acceleration, these details are often missing for virtual camera motion. In this work, we analyze camera movement using signal theory. Our system allows us to stylize a smooth user-defined virtual base camera motion by enriching it with plausible details. A key component of our system is a database of videos filmed by physical cameras. These videos are analyzed with a camera-motion estimation algorithm (structure-from-motion) and labeled manually with a specific style. By considering spectral properties of location, orientation and acceleration, our solution learns camera motion details. Consequently, an arbitrary virtual base motion, defined in any conventional animation package, can be automatically modified according to a user-selected style. In an animation package the camera motion base path is typically defined by the user via function curves. Another possibility is to obtain the camera path by using a mixed reality camera in motion capturing studio. As shown in our experiments, the resulting shots are still fully artist-controlled, but appear richer and more physically plausible.

Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments.

Using augmented state Kalman filter to localize multi autonomous underwater vehicles

The present paper describes a system for the construction of visual maps ("mosaics") and motion estimation for a set of AUVs (Autonomous Underwater Vehicles). Robots are equipped with down-looking camera which is used to estimate their motion with respect to the seafloor and built an online mosaic. As the mosaic increases in size, a systematic bias is introduced in its alignment, resulting in an erroneous output. The theoretical concepts associated with the use of an Augmented State Kalman Filter (ASKF) were applied to optimally estimate both visual map and the fleet position.

Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T.

PURPOSE: The goal of the present study was to use a three-dimensional (3D) gradient echo volume in combination with a fat-selective excitation as a 3D motion navigator (3D FatNav) for retrospective correction of microscopic head motion during high-resolution 3D structural scans of extended duration. The fat excitation leads to a 3D image that is itself sparse, allowing high parallel imaging acceleration factors - with the additional advantage of a minimal disturbance of the water signal used for the host sequence. METHODS: A 3D FatNav was inserted into two structural protocols: an inversion-prepared gradient echo at 0.33 × 0.33 × 1.00 mm resolution and a turbo spin echo at 600 μm isotropic resolution. RESULTS: Motion estimation was possible with high precision, allowing retrospective motion correction to yield clear improvements in image quality, especially in the conspicuity of very small blood vessels. CONCLUSION: The highly accelerated 3D FatNav allowed motion correction with noticeable improvements in image quality, even for head motion which was small compared with the voxel dimensions of the host sequence. Magn Reson Med 75:1030-1039, 2016. © 2015 Wiley Periodicals, Inc.

Dense motion field estimation from myocardial boundary displacements

Minimally invasive cardiovascular interventions guided by multiple imaging modalities are rapidly gaining clinical acceptance for the treatment of several cardiovascular diseases. These images are typically fused with richly detailed pre-operative scans through registration techniques, enhancing the intra-operative clinical data and easing the image-guided procedures. Nonetheless, rigid models have been used to align the different modalities, not taking into account the anatomical variations of the cardiac muscle throughout the cardiac cycle. In the current study, we present a novel strategy to compensate the beat-to-beat physiological adaptation of the myocardium. Hereto, we intend to prove that a complete myocardial motion field can be quickly recovered from the displacement field at the myocardial boundaries, therefore being an efficient strategy to locally deform the cardiac muscle. We address this hypothesis by comparing three different strategies to recover a dense myocardial motion field from a sparse one, namely, a diffusion-based approach, thin-plate splines, and multiquadric radial basis functions. Two experimental setups were used to validate the proposed strategy. First, an in silico validation was carried out on synthetic motion fields obtained from two realistic simulated ultrasound sequences. Then, 45 mid-ventricular 2D sequences of cine magnetic resonance imaging were processed to further evaluate the different approaches. The results showed that accurate boundary tracking combined with dense myocardial recovery via interpolation/ diffusion is a potentially viable solution to speed up dense myocardial motion field estimation and, consequently, to deform/compensate the myocardial wall throughout the cardiac cycle. Copyright © 2015 John Wiley & Sons, Ltd.

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography

This paper presents a new registration algorithm, called Temporal Di eomorphic Free Form Deformation (TDFFD), and its application to motion and strain quanti cation from a sequence of 3D ultrasound (US) images. The originality of our approach resides in enforcing time consistency by representing the 4D velocity eld as the sum of continuous spatiotemporal B-Spline kernels. The spatiotemporal displacement eld is then recovered through forward Eulerian integration of the non-stationary velocity eld. The strain tensor iscomputed locally using the spatial derivatives of the reconstructed displacement eld. The energy functional considered in this paper weighs two terms: the image similarity and a regularization term. The image similarity metric is the sum of squared di erences between the intensities of each frame and a reference one. Any frame in the sequence can be chosen as reference. The regularization term is based on theincompressibility of myocardial tissue. TDFFD was compared to pairwise 3D FFD and 3D+t FFD, bothon displacement and velocity elds, on a set of synthetic 3D US images with di erent noise levels. TDFFDshowed increased robustness to noise compared to these two state-of-the-art algorithms. TDFFD also proved to be more resistant to a reduced temporal resolution when decimating this synthetic sequence. Finally, this synthetic dataset was used to determine optimal settings of the TDFFD algorithm. Subsequently, TDFFDwas applied to a database of cardiac 3D US images of the left ventricle acquired from 9 healthy volunteers and 13 patients treated by Cardiac Resynchronization Therapy (CRT). On healthy cases, uniform strain patterns were observed over all myocardial segments, as physiologically expected. On all CRT patients, theimprovement in synchrony of regional longitudinal strain correlated with CRT clinical outcome as quanti ed by the reduction of end-systolic left ventricular volume at follow-up (6 and 12 months), showing the potential of the proposed algorithm for the assessment of CRT.

Trinocular System for 3D motion and Dense Stucture Estimation

The relief of the seafloor is an important source of data for many scientists. In this paper we present an optical system to deal with underwater 3D reconstruction. This system is formed by three cameras that take images synchronously in a constant frame rate scheme. We use the images taken by these cameras to compute dense 3D reconstructions. We use Bundle Adjustment to estimate the motion ofthe trinocular rig. Given the path followed by the system, we get a dense map of the observed scene by registering the different dense local reconstructions in a unique and bigger one

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study.

Electrical impedance tomography (EIT) is a non-invasive imaging technique that can measure cardiac-related intra-thoracic impedance changes. EIT-based cardiac output estimation relies on the assumption that the amplitude of the impedance change in the ventricular region is representative of stroke volume (SV). However, other factors such as heart motion can significantly affect this ventricular impedance change. In the present case study, a magnetic resonance imaging-based dynamic bio-impedance model fitting the morphology of a single male subject was built. Simulations were performed to evaluate the contribution of heart motion and its influence on EIT-based SV estimation. Myocardial deformation was found to be the main contributor to the ventricular impedance change (56%). However, motion-induced impedance changes showed a strong correlation (r = 0.978) with left ventricular volume. We explained this by the quasi-incompressibility of blood and myocardium. As a result, EIT achieved excellent accuracy in estimating a wide range of simulated SV values (error distribution of 0.57 ± 2.19 ml (1.02 ± 2.62%) and correlation of r = 0.996 after a two-point calibration was applied to convert impedance values to millilitres). As the model was based on one single subject, the strong correlation found between motion-induced changes and ventricular volume remains to be verified in larger datasets.

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Estimation of visual motion parameters used for bio-inspired navigation

Visual motion cues play an important role in animal and humans locomotion without the need to extract actual ego-motion information. This paper demonstrates a method for estimating the visual motion parameters, namely the Time-To-Contact (TTC), Focus of Expansion (FOE), and image angular velocities, from a sparse optical flow estimation registered from a downward looking camera. The presented method is capable of estimating the visual motion parameters in a complicated 6 degrees of freedom motion and in real time with suitable accuracy for mobile robots visual navigation.