Segmentation and depth perception of naturalistic textures by human observers

在日常生活中，我们的视觉系统会无意识地、毫不费力地将杂乱的视觉场景图像解析为离散的3D表面和物体。我们的大脑究竟是如何实现这一惊人的壮举的，我们对此知之甚少，尽管我们对视觉通路早期阶段的神经元如何提取和表示简单的图像特征有着丰富的知识。自然图像通常由纹理丰富的表面组成，其许多小特征极大地加剧了这些计算挑战。特别是，我们很难理解纹理特征是如何整合和/或分离到视觉图像的不同区域，以及它们的深度关系是如何推断的。* 我研究的长期目标是对视觉系统如何编码和分离属于不同表面的图像特征以及确定它们的深度关系有一个明确的理解。由此产生的知识将是我们对人类视觉系统基本理解的重大进步，并将在未来计算机视觉和人工智能技术的设计中发挥不可估量的作用。建议的实验将建立在我们最近的进展，使用纹理刺激在三种人类心理物理任务：同时对比感知纹理密度，分割（“图形-地面隔离”）相邻纹理之间，和深度知觉从运动视差和立体视觉。我们将使用更自然的纹理刺激，以带出现实世界的图像的实际复杂性，并确保结果的适用性，以现实的情况。这项工作还将涉及计算建模，基于生物现实的组件和操作，以预测心理物理反应-从而提供一个严格的，定量的测试模型如何总结或捕捉人类的表现。所有拟议的项目将提供高技术培训的研究生和博士后的。为了进行这项研究，他们必须学习和使用有关线性和非线性系统和信号的理论知识（例如空间卷积，线性滤波，傅立叶光谱，非线性等），计算机编程（Matlab）用于创建和呈现视觉刺激（心理物理学），并在某些情况下用于模拟模型。目标3涉及机器学习，目标4涉及先进的计算机图形学。

From moment to moment in everyday life, our visual system unconsciously and effortlessly parses images of cluttered visual scenes into discrete 3D surfaces and objects. Exactly how our brains achieve this amazing feat is remarkably poorly understood, in spite of a wealth of knowledge about how neurons in early stages of the visual pathways extract and represent simple image features. Natural images are often comprised of richly textured surfaces, whose many small features greatly compound these computational challenges. In particular, we poorly understand how texture features are integrated and/or segregated into distinct regions of the visual image, and how their depth relationships are inferred. *** The long-term goal of my research is to achieve a well-defined understanding of how the visual system is able to encode and segregate image features belonging to distinct surfaces and to ascertain their depth relationships. The resultant knowledge will be a major advance in our fundamental understanding of the human visual system, and will be invaluable in the design of future computer vision and artificial intelligence technology.*** The proposed experiments will build upon our recent progress using texture stimuli in three kinds of human psychophysical task: simultaneous contrast of perceived texture density, segmentation ("figure-ground segregation") between adjacent textures, and depth perception from motion parallax and stereopsis. We will use more naturalistic texture stimuli to bring out the actual complexities of real-world images and to insure the applicability of the results to realistic situations. The work will also involve computational modeling, based on biologically realistic components and operations, to predict psychophysical responses - thus providing a rigorous, quantitative test of how well the model summarizes or captures human performance.*** All of the proposed projects will provide highly technical training to graduate students and post-doc's. To pursue this research, they will have to learn and use theoretical knowledge about linear and nonlinear systems and signals (e.g. spatial convolution, linear filtering, Fourier spectra, nonlinearities, etc), computer programming (Matlab) both for creating and presenting visual stimuli (Psychophysics Toolbox), and in some cases for simulating models. Aim 3 will involve machine learning, and Aim 4, advanced computer graphics.***