运动感知计算问题研究
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摘要
运动分析是视觉研究中富有挑战性的研究课题。近年来,生物视觉在运动感知方面的研究形成了相对完备的理论体系,提出人类视觉运动感知至少包含两个分离的子系统,分别加工亮度定义的一阶运动以及对比度、空间频率、时间频率等属性定义的二阶运动。
本文着眼于从计算的角度系统地分析初级视觉一阶和二阶运动的感知原理,并建立相应的计算模型,探索新的生物学启发式运动检测算法。
依据视觉心理学和神经生理学在一阶运动感知方面的成果,设计符合计算原则的时空分离滤波器,实现基于延迟比较原则的运动分析模块,并从理论和实践角度分析各步骤的功能意义。
结合计算应用实际,建立基于不同决策准则的算法,设计实现完整可计算的一阶运动相关模型。利用合成和真实场景的图像序列比较不同算法间的性能,结果显示,赢者取全方式决策的算法具有良好的抗噪性和鲁棒性。
利用时空表达及频谱分析等手段,深入分析二阶运动的本质属性,指出二阶运动实际上是调制信号在三维时空的一种表现形式,并提出其在计算上的分类观点,即按照信号调制的方式将其归纳为空间调制、时间调制和时空调制三类运动。
基于对二阶运动现象的分析,设计通用的非线性预处理算法一纹理捕获器,实现完整的二阶运动检测模型。针对不同类型二阶运动进行的计算分析,结果显示设计的纹理捕获算法能够完成属性的变换以及信息的解调,使得后续的一阶模型能够获取足够的有效信息,并首次从计算角度辅证二阶运动由非线性感知系统加工的理论。
最后,从一阶和二阶运动信息的存在关系出发,分析算法的适用范围,探索合理的信息融合方式以及生物学启发式算法的应用价值。
Motion analysis is a challenge for vision research. In recent years, a relatively complete theoretical system of motion perception has been established. As widely accepted in biological vision, there exist at least two distinct low-level subsystems analyzing motion:a first order system that responds to certain moving luminance patterns, and a second order system that responds to moving modulation of feature types, which are usually defined by contrast, spatial frequency, temporal frequency etc.
From the perspective of computational analysis, we focus on the low-level motion perception mechanism, and establish the corresponding computational model to explore a new biologically inspired motion detection algorithm.
Combination of psychology and neurophysiology outcomes, separable spatial and temporal filters in motion detector are designed, which collaborate to compute the motion based on the delay-and-comparison principle. Furthermore its compositional modules are thoroughly analyzed to reveal the functional connotation respectively.
In consideration of computer applications, an elaborated version of the biological correlation model is proposed with different decision principle. The implementation is valided both on synthetic and real world image sequences. Preliminary experimental results show that the proposed detector with the Winner-Take-All decision has better robustness and anti-noise capability.
In this paper, different types of second order motion are formulized and investigated in detail. We present that second order motions can be divided into three typical groups according to the modulation types:spatial modulate motion, temporal modulate motion and spatiotemporal modulate motion.
Through the analysis of second order motion, a general nonlinear preprocessor, Texture Grabber, is proposed for detecting various types of motions. Experiments are conducted by correlation model preceded with the nonlinear processor. Preliminary analysis demonstrates that the proposed detector can capture effective information from different types of second order motions. The computational results are consistent with the previous suggestion that the second order motions are processed by nonlinear system.
Finally, we discuss the relationship between first and second order motion information. Appropriate combination will obtain more reliable motion estimation, and the bio-plausible exploiture may bring some new advantage to computer vision practice.
本文着眼于从计算的角度系统地分析初级视觉一阶和二阶运动的感知原理,并建立相应的计算模型,探索新的生物学启发式运动检测算法。
依据视觉心理学和神经生理学在一阶运动感知方面的成果,设计符合计算原则的时空分离滤波器,实现基于延迟比较原则的运动分析模块,并从理论和实践角度分析各步骤的功能意义。
结合计算应用实际,建立基于不同决策准则的算法,设计实现完整可计算的一阶运动相关模型。利用合成和真实场景的图像序列比较不同算法间的性能,结果显示,赢者取全方式决策的算法具有良好的抗噪性和鲁棒性。
利用时空表达及频谱分析等手段,深入分析二阶运动的本质属性,指出二阶运动实际上是调制信号在三维时空的一种表现形式,并提出其在计算上的分类观点,即按照信号调制的方式将其归纳为空间调制、时间调制和时空调制三类运动。
基于对二阶运动现象的分析,设计通用的非线性预处理算法一纹理捕获器,实现完整的二阶运动检测模型。针对不同类型二阶运动进行的计算分析,结果显示设计的纹理捕获算法能够完成属性的变换以及信息的解调,使得后续的一阶模型能够获取足够的有效信息,并首次从计算角度辅证二阶运动由非线性感知系统加工的理论。
最后,从一阶和二阶运动信息的存在关系出发,分析算法的适用范围,探索合理的信息融合方式以及生物学启发式算法的应用价值。
Motion analysis is a challenge for vision research. In recent years, a relatively complete theoretical system of motion perception has been established. As widely accepted in biological vision, there exist at least two distinct low-level subsystems analyzing motion:a first order system that responds to certain moving luminance patterns, and a second order system that responds to moving modulation of feature types, which are usually defined by contrast, spatial frequency, temporal frequency etc.
From the perspective of computational analysis, we focus on the low-level motion perception mechanism, and establish the corresponding computational model to explore a new biologically inspired motion detection algorithm.
Combination of psychology and neurophysiology outcomes, separable spatial and temporal filters in motion detector are designed, which collaborate to compute the motion based on the delay-and-comparison principle. Furthermore its compositional modules are thoroughly analyzed to reveal the functional connotation respectively.
In consideration of computer applications, an elaborated version of the biological correlation model is proposed with different decision principle. The implementation is valided both on synthetic and real world image sequences. Preliminary experimental results show that the proposed detector with the Winner-Take-All decision has better robustness and anti-noise capability.
In this paper, different types of second order motion are formulized and investigated in detail. We present that second order motions can be divided into three typical groups according to the modulation types:spatial modulate motion, temporal modulate motion and spatiotemporal modulate motion.
Through the analysis of second order motion, a general nonlinear preprocessor, Texture Grabber, is proposed for detecting various types of motions. Experiments are conducted by correlation model preceded with the nonlinear processor. Preliminary analysis demonstrates that the proposed detector can capture effective information from different types of second order motions. The computational results are consistent with the previous suggestion that the second order motions are processed by nonlinear system.
Finally, we discuss the relationship between first and second order motion information. Appropriate combination will obtain more reliable motion estimation, and the bio-plausible exploiture may bring some new advantage to computer vision practice.
引文
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