Perception-based model simplification for motion blur rendering

Motion blur effects are important to motion perception in visual arts, interactive games and animation applications. Usually, such motion blur rendering is quite time consuming, thus blocking the online/interactive use of the effects. Motivated by the human perception in relation to moving objects, this paper presents simplified geometric models that enable to speedup motion blur rendering, which has not been tracked in motion blur rendering specifically. We develop a novel algorithm to simplify models with motion-aware, to preserve the features whose characteristics are perceivable in motion. We deduce the formula to outline the level of detail simplification by the object moving velocity. Using our simplified models, methods for motion blur rendering can achieve the rendering quality as using the original models, and obtain the processing acceleration mostly. The experimental results have shown the effectiveness of our approach, more acceleration with the larger models or faster motion (e.g. for the dragon model with over a million facets, the motion-blur rendering via hierarchical stochastic rasterization is sped up by over 27 times).     

Photo-consistent synthesis of motion blur and depth-of-field effects with a real camera model

Depth-of-field (DOF) and motion blur are important visual cues used for computer graphics and photography to illustrate focus of attention and object motion. In this work, we present a method for photo-realistic DOF and motion blur generation based on the characteristics of a real camera system. Both the depth–blur relation for different camera focus settings and the nonlinear intensity response of image sensors are modeled. The camera parameters are calibrated and used for defocus and motion blur synthesis. For a well-focused real scene image, DOF and motion blur effects are generated by post-processing techniques. Experiments have shown that the proposed method generates more photo-consistent results than the commonly used graphical models.     

运动模糊图像的质量分析与评价

提出了一种新的图像质量评价标准,通过图像的运动模糊参数来估计出图像由于运动而造成的信息损失量,并通过信息损失的多少来评价图像的质量。实验表明,该方法能客观地体现出运动模糊图像的质量与运动模糊参数之间的关系,这种关系对于图像的质量评价特别是有参考条件下的图像质量评价具有良好的效果。同时还根据活动度和图像灰度梯度能客观地表示图像细节部分的特性。将图像分块,并从8个方向对图像进行分析,客观地评价出无参考条件下直线运动模糊图像的质量。     

利用拉氏算子鉴别运动模糊方向

提出一种新的鉴别运动模糊图像的运动模糊方向的方法,它利用拉氏算子对运动模糊图像进行无方向性的二阶微分,并求微分图像的自相关,发现自相关图像中数值较大的点(鉴别点)能够有效标示出运动模糊方向。选取适当数目的候选鉴别点,并利用聚类方法剔除其中的奇异点,得到鉴别点;过零频尖峰(自相关图像的中心点)画一条直线,计算各个鉴别点到该直线的距离,求距离和;改变直线方向,当距离和最小时,直线的方向即为运动模糊方向。数据实验表明,这一新的运动模糊方向鉴别方法,具有抗噪声能力强、适用范围广、计算量小、鉴别精度高、稳定性好的优点．     

Motion blur identification in noisy images using mathematical models and statistical measures

Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and robustness in noisy environments. In this paper, we have presented a novel algorithm to estimate linear motion blur parameters such as direction and length. We used Radon transform to find direction and bispectrum modeling to find the length of motion. Our algorithm is based on the combination of spatial and frequency domain analysis. The great benefit of our algorithm is its robustness and precision in noisy images. We used statistical measures to prove goodness of our model. Our method was tested on 80 standard images that were degraded with different directions and motion lengths, with additive Gaussian noise. The error tolerance average of the estimated parameters was 0.9° in direction and 0.95 pixel in length and the standard deviations were 0.69 and 0.85, respectively.     

High-speed human motion recovery employing back projection

This paper describes a novel technique for high-speed human motion recovery. For 3-D human motion recovery, stereoscopic vision is a normal technique, as well as the employment of a magnetic field, but this cannot avoid the difficulty of marker tracking. In this article, a novel motion recovery technique is proposed based on the back projection of silhouette images. This technique has some advantages over others because it does not employ markers, and it has a simple architecture. In the experiment performed, the proposed motion recovery technique is implemented on a system containing a LAN, a host computer, and four pairs of camera and computer, and it achieves high-speed human motion recovery. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

应用运动模糊方法仿真铅笔画纹理

通过分析真实铅笔纹理的特点,提出了一种基于运动滤波器的仿真铅笔纹理的新方法.首先对用户输入的图像进行边缘检测,提取轮廓信息,然后对传统白噪声生成法进行改进,将参考图像由RGB色彩空间转换到HSV色彩空间,通过亮度值控制噪声点的产生几率,最后应用运动模糊方法代替传统的线积分卷积(LIC)方法模拟铅笔纹理,得到具有素描风格的结果图像.实验结果表明,该方法生成效果接近于手绘风格,且无需用户交互,适合于动画制作等很多领域的应用.     

H.223协议实时处理中透明码生成的快速算法及其实现

本文给出了一种基于软件实现的透明码生成的快速算法。字节是Ｃ语言中所能处理的最小数据单元,因而对字节的处理（包括存取、运算等）有着位处理无法比拟的优点。根据这一特点,该算法以字节为单位,通过查表方式,快速生成（和还原）透明码。从而避免了以位操作所带来的时间开销较大的缺点     

A real-time system for motion retrieval and interpretation

This paper proposes a new examplar-based method for real-time human motion recognition using Motion Capture (MoCap) data. We have formalized streamed recognizable actions, coming from an online MoCap engine, into a motion graph that is similar to an animation motion graph. This graph is used as an automaton to recognize known actions as well as to add new ones. We have defined and used a spatio-temporal metric for similarity measurements to achieve more accurate feedbacks on classification. The proposed method has the advantage of being linear and incremental, making the recognition process very fast and the addition of a new action straightforward. Furthermore, actions can be recognized with a score even before they are fully completed. Thanks to the use of a skeleton-centric coordinate system, our recognition method has become view-invariant. We have successfully tested our action recognition method on both synthetic and real data. We have also compared our results with four state-of-the-art methods using three well known datasets for human action recognition. In particular, the comparisons have clearly shown the advantage of our method through better recognition rates.     

Motion‐blur evaluation: A comparison of approaches

Abstract— In this paper, the results obtained from two independent evaluations of motion‐blur effects with respect to the agreement between the two different approaches used, imaging and non‐imaging, are analyzed. The measurements have been carried out in different laboratories by different operators without the prior intention of a subsequent analysis as presented here. The resulting data is analyzed to quantify the repeatability of each instrument and, in a second step, the comparability of results from the two approaches is investigated. The imaging approach used in these experiments is based on a stationary high‐speed camera with temporal oversampling and numerical image‐data processing to obtain the intensity distribution on the retina of an observer under the condition of smooth pursuit eye tracking. Results from that approach are compared to results obtained from the evaluation of step responses acquired with optical transient recorders by frame‐period convolution. Measurements are carried out with a first LCD monitor with test patterns of both contrast polarities, with three velocities of translation, and four levels of gray. A second object of measurement is used for investigation of the effect of operator intervention in the process of evaluation of the imaging approach, especially on the determination of the reference levels that are needed for evaluation of the normalized blurred edge (NBE). Possible sources of uncertainties are identified for all approaches and instruments. Based on the analysis of that data, the practicability of step‐response‐based evaluations of the “blurred edge width/time” compared to the results obtained using the high‐speed imaging approach, as long as there is no motion‐dependent image processing, are confirmed.     

Simultaneous people tracking and motion pattern learning

The field of Human Robot Interaction (HRI) encompasses many difficult challenges as robots need a better understanding of human actions. Human detection and tracking play a major role in such scenarios. One of the main challenges is to track them with long term occlusions due to agile nature of human navigation. However, in general humans do not make random movements. They tend to follow common motion patterns depending on their intentions and environmental/physical constraints. Therefore, knowledge of such common motion patterns could allow a robotic device to robustly track people even with long term occlusions. On the other hand, once a robust tracking is achieved, they can be used to enhance common motion pattern models allowing robots to adapt to new motion patterns that could appear in the environment. Therefore, this paper proposes to learn human motion patterns based on Sampled Hidden Markov Model (SHMM) and simultaneously track people using a particle filter tracker. The proposed simultaneous people tracking and human motion pattern learning has not only improved the tracking robustness compared to more conservative approaches, it has also proven robustness to prolonged occlusions and maintaining identity. Furthermore, the integration of people tracking and on-line SHMM learning have led to improved learning performance. These claims are supported by real world experiments carried out on a robot with suite of sensors including a laser range finder.     

基于切比雪夫矩无参考模糊图像质量评价

提出了一种新的基于Tchebichef矩的无参考模糊图像质量评价方法。将模糊图像通过低通滤波得到再模糊图像;将模糊图像和再模糊图像分别进行8×8分块并计算每一图像块的Tchebichef矩;根据Tchebichef矩块的值将原始图像块分为平滑块,纹理块和边缘块,计算原始图像和再模糊图像对应块之间的Tchebichef矩向量相似度,得到三类图像块的局部平均相似度;进行融合得到原始图像的最终评价质量。实验结果表明,该方法优于其他算法,与主观评分有更好的一致性,能够更准确地评价模糊图像质量。     

Hybrid motion graph for character motion synthesis

ObjectiveThis paper proposes a novel framework of Hybrid Motion Graph (HMG) for creating character animations, which enhances the graph-based structural control by motion field representations for efficient motion synthesis of diverse and interactive character animations.MethodsIn HMG framework, the motion template of each class is automatically derived from the training motions for capturing the general spatio-temporal characteristics of an entire motion class. Typical motion field for each class is then constructed. The smooth transitions among motion classes are then generated by interpolating the related motion templates with spacetime constraints. Finally, a hybrid motion graph is built by integrating the separate motion fields for each motion class into the global structural control of motion graph through smooth transition.ResultsIn motion synthesis stage, a character may freely ‘switch’ among different motion classes in the hybrid motion graph via smooth transitions between motion templates and ‘flow’ within each class through the continuous space of motion field with agile and the continuous control process.ConclusionExperimental results show that our framework realizes the fast connectivity among different motion classes and high responsiveness and interactivity for creating realistic character animation of rich behaviors with limited motion data and computational resources.     

Development and implementation of a NURBS curve motion interpolator

This paper deals with the issues of development and implementation of a real-time NURBS interpolator for a six-axis robot. Using an open-architecture controller system as a testbed, a real-time NURBS curve interpolator was developed, implemented and tested. Sample runs were conducted with the resulting trajectories measured in real-time during robot motion. The resulting trajectories are analyzed, discussed and compared with those from a commonly used point-to-point approximation technique. The real-time NURBS curve interpolator's feasibility, advantages and related issues are also discussed.     

Interactive development of object handling programs

We describe work on development of a software system for writing and testing programs for a computer controlled manipulator. We examine in particular how the development of working programs is facilitated by the use of an interactive system based on an interpreter. The paper presents the main features of POINTY, the system developed at Stanford Artificial Intelligence Laboratory as a tool for writing assembly programs. The user, interacting with the manipulator, constructs an incremental model of the objects involved in the assembly and develops the corresponding symbolic program.     

Robust real-time ground plane motion compensation from a moving vehicle

One method to detect obstacles from a vehicle moving on a planar road surface is the analysis of motion-compensated difference images. In this contribution, a motion compensation algorithm is presented, which computes the required image-warping parameters from an estimate of the relative motion between camera and ground plane. The proposed algorithm estimates the warping parameters from displacements at image corners and image edges. It exploits the estimated confidence of the displacements to cope robustly with outliers. Knowledge about camera calibration, measuremts from odometry, and the previous estimate are used for motion prediction and to stabilize the estimation process when there is not enough information available in the measured image displacements. The motion compensation algorithm has been integrated with modules for obstacle detection and lane tracking. This system has been integrated in experimental vehicles and runs in real time with an overall cycle of 12.5 Hz on low-cost standard hardware. Received: 23 April 1998 / Accepted: 25 August 1999     

Improved estimation of defocus blur and spatial shifts in spatial domain: a homotopy-based approach

This paper presents a homotopy-based algorithm for the recovery of depth cues in the spatial domain. The algorithm specifically deals with defocus blur and spatial shifts, that is 2D motion, stereo disparities and/or zooming disparities. These cues are estimated from two images of the same scene acquired by a camera evolving in time and/or space. We show that they can be simultaneously computed by resolving a system of equations using a homotopy method. The proposed algorithm is tested using synthetic and real images. The results confirm that the use of a homotopy method leads to a dense and accurate estimation of depth cues. This approach has been integrated into an application for relief estimation from remotely sensed images.     

Reconfigurable architectures and processors for real-time video motion estimation

With the recent proliferation of multimedia applications, several fast block matching motion estimation algorithms have been proposed in order to minimize the processing time in video coding. While some of these algorithms adopt pre-defined search patterns that directly reflect the most probable motion structures, other data-adaptive approaches dynamically configure the search pattern to avoid unnecessary computations and memory accesses. Either of these approaches leads to rather difficult hardware implementations, due to their configurability and adaptive nature. As a consequence, two different but quite configurable architectures are proposed in this paper. While the first architecture reflects an innovative mechanism to implement motion estimation processors that support fast but regular search algorithms, the second architecture makes use of an application specific instruction set processor (ASIP) platform, capable of implementing most data-adaptive algorithms that have been proposed in the last few years. Despite their different natures, these two architectures provide highly configurable hardware platforms for real-time motion estimation. By considering a wide set of fast and adaptive algorithms, the efficiency of these two architectures was compared and several motion estimators were synthesized in a Virtex-II Pro XC2VP30 FPGA from Xilinx, integrated within a ML310 development platform. Experimental results show that the proposed architectures can be easily reconfigured in run-time to implement a wide set of real-time motion estimation algorithms.

UM-RTCOM: An analyzable component model for real-time distributed systems

Component-based development is a key technology in the development of software for modern real-time systems. However, standard component models and tools are not suitable for this type of system, since they do not explicitly address real time, memory or cost constraints. This paper presents a new predictable component model for real-time systems (UM-RTCOM) together with a set of tools to support it. The environment allows new components to be developed which can then be assembled to build complete applications, including hardware interaction. The model includes support for real-time analysis at the component and application level. The analysis is achieved by combining component meta-information in the form of an abstract behaviour model and a method to measure worst-case execution times in the final platform. Additionally, we propose an implementation model based on RT-CORBA where the developer uses the UM-RTCOM components and a set of tools to map these elements to elements of the desired platform. In order to apply our proposals, we have used the model and tools in real applications specifically in the context of nuclear power plant simulators.     

A data collection protocol for real-time sensor applications

The nature of many sensor applications as well as continuously changing sensor data often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwidth, memory and energy of sensor platforms, and packet collisions that can potentially lead to an unbounded number of retransmissions, timeliness techniques designed for real-time systems and real-time databases cannot be applied directly to wireless sensor networks. Our objective is to design a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as a graph coloring problem. We then present TIGRA (Timely Sensor Data Collection using Distributed Graph Coloring) — a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node. Although the end-to-end delay incurred by sensor data collection largely depends on a specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees a deterministic delay on sensor data collection.