人工视觉辅助系统：现状与展望

通过视觉获取图像信息是人类学习和生活的重要功能,失明则会显著降低其生活质量.因视网膜色素变性、青光眼和黄斑变性等疾病而造成后天失明者,以及由意外事故、战争等造成眼部创伤者,有可能通过人工视觉辅助系统的帮助恢复部分视觉,或者完成复杂的生活任务.一些盲症患者视觉通路的神经传导剩余部分依然有功能,因此可以借助电极阵列刺激视神经向大脑传递视觉信息,也可在大脑视觉皮层贴敷电极阵列的方法输入视觉信息.此外,还能借助体外装置,如通过人工智能将视觉转换成语音指令、触觉阵列编码等,帮助盲症患者获得环境信息.本文综述各类人工视觉辅助系统的现状,展望其发展趋势,并提出了新的植入器件与随身体外装置的新设想.     

一种新的视觉皮层简单细胞工作机制的模型

提出了一种基于独立元分析（ICA）的视觉皮层简单细胞工作机制的模型。用Gabor函数逼近对自然图像进行ICA而获得的基函数,揭示了ICA基函数与视觉皮层简单细胞感受野反应间存在内在的关系。并对水平条纹的图像进行ICA,模拟在特殊视觉环境下生长的幼年动物的视觉皮层发育过程,证实了1970年Blakemore和Cooper在幼猫上的实验结果。从而说明ICA可以模拟动物的视觉皮层简单细胞工作过程。     

视觉假体的研究进展与面临的挑战

人工视觉假体是当今国际上对视网膜色素变性和老年性黄斑病变患者进行视觉修复的研究热点,该人工装置采集外界图像信息,并进行编码处理,通过微电流刺激器将刺激微电流信号加载到微电极阵列,对视觉神经系统进行作用,从而在视觉中枢产生光幻视,实现视觉功能修复。根据目前的国际研究现状,视觉假体可以对视觉通路的任意位置进行电刺激,以期产生视光感。按照植入位置的不同,视觉假体基本上可以分为视皮层假体、视网膜上假体、视网膜下假体和视神经假体。本文着重介绍了中国的C-Sight小组在视神经假体方面的工作进展和面临的挑战。     

不同视觉经验中zif268基因的表达模式

zif268基因编码一转录因子ZIF268. 在发育的视皮层中,zif268基因的表达模式受发育的调节. 在具有正常视觉经验的视皮层中,zif268基因有较高水平的表达. 视觉废置后,视皮层内该基因的表达水平显著降低. 通过视觉刺激可显著增强该基因的表达. 有关zif268基因表达模式的研究对于阐明该基因在哺乳动物视觉系统中的生理功能起到借鉴的作用.     

一种基于chirp编码的高帧速率超声成像方法

在医学超声成像系统中,帧速率由每帧图像的扫查发射次数所决定.同时发射多条波束可以提高图像的帧速率,但是这会带来不同波束间相互干扰的问题,形成伪像.本文基于编码激励的原理,提出了一种新的高帧速率成像方法.该方法通过发射一组线性频率调制编码信号,有效的降低了波束间的互扰.可以在不影响图像质量的情况下,成倍的提高图像的帧速率.     

基于街景数据的绿视率模型构建及其影响因子研究

以行人的视觉直观感受为出发点, 以匈牙利塞克什白堡为研究区, 基于街景数据, 深入研究植被信息提取方法, 针对传统像素级分类容易造成过度提取的现象, 提出一种面向对象的街景图像分类方法, 构建了基于街景数据的绿视率模型, 并分析总结了街景图像拍摄时的水平视角、垂直视角、水平方向范围等镜头参数对绿视率计算的影响。研究结果表明: 面向对象的分类方法提升了街景图像分类的精度和效率, 为绿视率计算模型提供了新的数据源和计算方法; 采集街景图像时, 增加水平视角和垂直视角、扩大水平方向范围能使绿视率计算结果更加真实地反映行人视觉感受。构建的绿视率计算模型能从行人角度为街道绿化的布局和空间结构优化提供依据, 可为城市绿地规划设计、居住区视觉生态设计等提供参考依据。     

视网膜神经节细胞的信息编码特性

在脊椎动物的视觉系统中,信息的初级处理发生在视网膜。视网膜神经节细胞是视网膜唯一的输出神经元,在不同视觉刺激条件下会表现出不同的放电活动模式。研究表明视网膜神经节细胞可以利用多种编码方式,包括频率编码、时间结构编码以及群体协同编码等,有效地编码外界刺激。另外,大千世界的视觉场景变化几乎是无限的,长期的进化赋予了视网膜良好的适应能力,以实现通过有限的神经元活动对无限变化的视觉场景的编码。本文回顾了近年来关于视网膜神经节细胞编码方式和适应特性的相关研究,对多种编码方式在不同刺激下的动态改变、适应特性及生理功能进行讨论。     

复眼透镜光学信息保密编码及译码的研究——Ⅱ.分解掩模编码与综合再现

本文根据复眼透镜光学信息编译码的技术原理,实现了对二维图像进行分解编码记录以及综合译码再现.一幅m×n个目标单元的二维图像,通过1×k阵列的复眼透镜,得到(1×k)(m×n)个像元.经过一个特制的掩模板,得到一幅随机分解编码像,并根据透镜的物、像共轭原理,综合再现了原始图像.进而还实现了同时记录多幅二维图像信息的互补编码像,以及将互补编码像分离重现了每一幅原始目标图像.此互补编码像携带了更大的信息量,同时也大大提高了保密性能.     

视网膜神经节细胞对自然刺激的时空反应模式

神经系统信息处理的理论研究和计算结果表明,视皮层可以通过稀疏编码 (sparse coding) 模式来处理自然刺激信息.神经元群体中,单个神经元在大多数时间里没有强的脉冲发放 (时间维稀疏性,lifetime sparseness),而针对某一刺激,只有少数神经元在特定的时间内发放 (空间维稀疏性,population sparseness).从神经元放电的时间和空间模式两个方面考察了视网膜神经节细胞群体对自然刺激(电影)的编码方式,并同实验室常用的伪随机棋盘格刺激下视网膜的反应模式进行比较,分析了视网膜神经节细胞反应的稀疏性指标,并深入探讨了其内在的时间和空间特点.结果提示,视觉系统在其最初阶段——视网膜——即开始采用一种高效节能的稀疏编码方式来处理自然视觉信息,单个神经元的时间维稀疏性节省了代谢能量消耗,而群体神经元中邻近神经元的动态成组协同发放,提高了信息向突触后神经元传递的有效性.     

双重模糊编码遗传算法及聚焦搜索策略

针对实数编码导致的交叉操作子代取值区间受限、并行机制丧失等问题,提出了一种双重模糊编码遗传算法.在双重模糊编码遗传算法中一个连续变量被同时表示成实数编码和由3个码位组成的二进制编码;二进制编码对应变量部分实数区间.双重模糊编码使交叉操作产生的子代取值区间扩展到整个值域,传统遗传算法的并行计算机制得到部分恢复.配合双重模糊编码遗传算法提出的聚焦搜索策略解决了实数编码遗传算法局部搜索效率低、质量差的问题.     

Cortical sensitivity to visual features in natural scenes

A central hypothesis concerning sensory processing is that the neuronal circuits are specifically adapted to represent natural stimuli efficiently. Here we show a novel effect in cortical coding of natural images. Using spike-triggered average or spike-triggered covariance analyses, we first identified the visual features selectively represented by each cortical neuron from its responses to natural images. We then measured the neuronal sensitivity to these features when they were present in either natural images or random stimuli. We found that in the responses of complex cells, but not of simple cells, the sensitivity was markedly higher for natural images than for random stimuli. Such elevated sensitivity leads to increased detectability of the visual features and thus an improved cortical representation of natural scenes. Interestingly, this effect is due not to the spatial power spectra of natural images, but to their phase regularities. These results point to a distinct visual-coding strategy that is mediated by contextual modulation of cortical responses tuned to the spatial-phase structure of natural scenes.     

A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields

Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically motivated spiking network, relying solely on synaptically local information, that can predict the full diversity of V1 simple cell receptive field shapes when trained on natural images. This represents the first demonstration that sparse coding principles, operating within the constraints imposed by cortical architecture, can successfully reproduce these receptive fields. We further prove, mathematically, that sparseness and decorrelation are the key ingredients that allow for synaptically local plasticity rules to optimize a cooperative, linear generative image model formed by the neural representation. Finally, we discuss several interesting emergent properties of our network, with the intent of bridging the gap between theoretical and experimental studies of visual cortex.     

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

Filling-in at the blind spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. It is known that during filling-in, nonlinear neural responses are observed in the early visual area that correlates with the perception, but the knowledge of underlying neural mechanism for filling-in at the blind spot is far from complete. In this work, we attempted to present a fresh perspective on the computational mechanism of filling-in process in the framework of hierarchical predictive coding, which provides a functional explanation for a range of neural responses in the cortex. We simulated a three-level hierarchical network and observe its response while stimulating the network with different bar stimulus across the blind spot. We find that the predictive-estimator neurons that represent blind spot in primary visual cortex exhibit elevated non-linear response when the bar stimulated both sides of the blind spot. Using generative model, we also show that these responses represent the filling-in completion. All these results are consistent with the finding of psychophysical and physiological studies. In this study, we also demonstrate that the tolerance in filling-in qualitatively matches with the experimental findings related to non-aligned bars. We discuss this phenomenon in the predictive coding paradigm and show that all our results could be explained by taking into account the efficient coding of natural images along with feedback and feed-forward connections that allow priors and predictions to co-evolve to arrive at the best prediction. These results suggest that the filling-in process could be a manifestation of the general computational principle of hierarchical predictive coding of natural images.     

Innate visual learning through spontaneous activity patterns

Patterns of spontaneous activity in the developing retina, LGN, and cortex are necessary for the proper development of visual cortex. With these patterns intact, the primary visual cortices of many newborn animals develop properties similar to those of the adult cortex but without the training benefit of visual experience. Previous models have demonstrated how V1 responses can be initialized through mechanisms specific to development and prior to visual experience, such as using axonal guidance cues or relying on simple, pairwise correlations on spontaneous activity with additional developmental constraints. We argue that these spontaneous patterns may be better understood as part of an "innate learning" strategy, which learns similarly on activity both before and during visual experience. With an abstraction of spontaneous activity models, we show how the visual system may be able to bootstrap an efficient code for its natural environment prior to external visual experience, and we continue the same refinement strategy upon natural experience. The patterns are generated through simple, local interactions and contain the same relevant statistical properties of retinal waves and hypothesized waves in the LGN and V1. An efficient encoding of these patterns resembles a sparse coding of natural images by producing neurons with localized, oriented, bandpass structure-the same code found in early visual cortical cells. We address the relevance of higher-order statistical properties of spontaneous activity, how this relates to a system that may adapt similarly on activity prior to and during natural experience, and how these concepts ultimately relate to an efficient coding of our natural world.     

The human visual system is optimised for processing the spatial information in natural visual images

A fundamental tenet of visual science is that the detailed properties of visual systems are not capricious accidents, but are closely matched by evolution and neonatal experience to the environments and lifestyles in which those visual systems must work. This has been shown most convincingly for fish and insects. For mammalian vision, however, this tenet is based more upon theoretical arguments than upon direct observations. Here, we describe experiments that require human observers to discriminate between pictures of slightly different faces or objects. These are produced by a morphing technique that allows small, quantifiable changes to be made in the stimulus images. The independent variable is designed to give increasing deviation from natural visual scenes, and is a measure of the Fourier composition of the image (its second-order statistics). Performance in these tests was best when the pictures had natural second-order spatial statistics, and degraded when the images were made less natural. Furthermore, performance can be explained with a simple model of contrast coding, based upon the properties of simple cells in the mammalian visual cortex. The findings thus provide direct empirical support for the notion that human spatial vision is optimised to the second-order statistics of the optical environment.     

Is sparse and distributed the coding goal of simple cells?

The question of why the receptive fields of simple cells in the primary visual cortex are Gabor-like is a crucial one in vision research. Many research efforts (Olshausen and Field 1996, 1997; van Hateren and Ruderman 1998; van Hateren and van der Schaaf 1998) that yield a set of localized, oriented, and bandpass Gabor-like receptive fields believe that sparse and distributed is the coding goal of simple cells. This paper investigates a more general coding strategy that measures equally any departure from normality in the simple cells responses. That is, we investigate the possibility that highly kurtotic response histograms may result if simple cells explicitly seek, not maximally kurtotic, but rather maximally non-Gaussian response histograms to natural images. It is found that, under this coding strategy, the simulations produce a majority of localized, oriented, bandpass (Gabor-like) receptive fields. Some receptive fields, however, are spatially distributed and show little oriented structure. Nearly all receptive fields, regardless of whether they are Gabor-like or non-Gabor-like, yield highly kurtotic response histograms to natural images. Thus, in seeking maximally non-Gaussian response histograms, receptive fields spontaneously yield highly kurtotic histograms. The presence in our ensemble of nonlocalized, nonoriented receptive fields may be due to the artificial requirement that receptive fields be orthonormal. We conclude that the high kurtoses observed in the response histograms of simple-cell receptive fields to natural images may reflect a property of natural images themselves rather than an explicit coding goal used to structure simple-cell receptive fields.Acknowledgement This work was supported by the US Office of Naval Research under agreement number N68936-00-2-0002.     

Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding

Predictive coding has been previously introduced as a hierarchical coding framework for the visual system. At each level, activity predicted by the higher level is dynamically subtracted from the input, while the difference in activity continuously propagates further. Here we introduce modular predictive coding as a feedforward hierarchy of prediction modules without back-projections from higher to lower levels. Within each level, recurrent dynamics optimally segregates the input into novelty and familiarity components. Although the anatomical feedforward connectivity passes through the novelty-representing neurons, it is nevertheless the familiarity information which is propagated to higher levels. This modularity results in a twofold advantage compared to the original predictive coding scheme: the familiarity-novelty representation forms quickly, and at each level the full representational power is exploited for an optimized readout. As we show, natural images are successfully compressed and can be reconstructed by the familiarity neurons at each level. Missing information on different spatial scales is identified by novelty neurons and complements the familiarity representation. Furthermore, by virtue of the recurrent connectivity within each level, non-classical receptive field properties still emerge. Hence, modular predictive coding is a biologically realistic metaphor for the visual system that dynamically extracts novelty at various scales while propagating the familiarity information.     

Efficient sparse coding in early sensory processing: lessons from signal recovery

Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision.     

Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms

The nervous system has evolved in an environment with structure and predictability. One of the ubiquitous principles of sensory systems is the creation of circuits that capitalize on this predictability. Previous work has identified predictable non-uniformities in the distributions of basic visual features in natural images that are relevant to the encoding tasks of the visual system. Here, we report that the well-established statistical distributions of visual features -- such as visual contrast, spatial scale, and depth -- differ between bright and dark image components. Following this analysis, we go on to trace how these differences in natural images translate into different patterns of cortical input that arise from the separate bright (ON) and dark (OFF) pathways originating in the retina. We use models of these early visual pathways to transform natural images into statistical patterns of cortical input. The models include the receptive fields and non-linear response properties of the magnocellular (M) and parvocellular (P) pathways, with their ON and OFF pathway divisions. The results indicate that there are regularities in visual cortical input beyond those that have previously been appreciated from the direct analysis of natural images. In particular, several dark/bright asymmetries provide a potential account for recently discovered asymmetries in how the brain processes visual features, such as violations of classic energy-type models. On the basis of our analysis, we expect that the dark/bright dichotomy in natural images plays a key role in the generation of both cortical and perceptual asymmetries.     

Role of statistical symmetries in sensory coding: an optimal scale invariant code for vision

The visual system is the most studied sensory pathway, which is partly because visual stimuli have rather intuitive properties. There are reasons to think that the underlying principle ruling coding, however, is the same for vision and any other type of sensory signal, namely the code has to satisfy some notion of optimality--understood as minimum redundancy or as maximum transmitted information. Given the huge variability of natural stimuli, it would seem that attaining an optimal code is almost impossible; however, regularities and symmetries in the stimuli can be used to simplify the task: symmetries allow predicting one part of a stimulus from another, that is, they imply a structured type of redundancy. Optimal coding can only be achieved once the intrinsic symmetries of natural scenes are understood and used to the best performance of the neural encoder. In this paper, we review the concepts of optimal coding and discuss the known redundancies and symmetries that visual scenes have. We discuss in depth the only approach which implements the three of them known so far: translational invariance, scale invariance and multiscaling. Not surprisingly, the resulting code possesses features observed in real visual systems in mammals.