Robust visual recognition of high-level form in human observers

人类观察者对高级形式的鲁棒视觉识别

• 批准号: RGPIN-2019-05554
• 负责人: Oruc, Ipek
• 金额: $ 3.42万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761873
• 关键词: Robust; visual; recognition; level; form

Visual recognition is hard. Objects are seen in a variety of conditions differing in illumination, viewing distance, and orientation, which introduce drastic changes to their image. In addition, observer factors-from distortions introduced by the eye and the brain to varying visual experience over the observer's lifespan-influence visual recognition. Despite significant advances, a comprehensive understanding of the impact of these factors in computations underlying visual recognition remains elusive. Our recent work showed that the visual system takes advantage of sustained regularities, e.g., those in size or expression, in the visual input to facilitate recognition of faces. In a naturalistic observation study, we acquired footage of daily visual experiences via eyewear-embedded cameras. Analysis of this footage revealed that most views of faces are from social interaction distances, i.e., visually large. Indeed, we found that face sizes most common in social interactions are associated with better recognition. Our work on regularities in size and blur, also uncovered evidence consistent with the overarching hypothesis of this proposal that the visual system adopts strategies to optimize recognition performance based on the most frequent types of visual input in the environment. Long-term objectives of my research are: (1) Describe various statistical regularities in human observers' visual input and their impact on recognition, including physical regularities, such as those that arise from the geometry of optics, as well as those that are consequences of the ways in which observers interact with their environment, (2) Uncover strategies of the visual system that utilize these regularities to facilitate recognition, and (3) Develop novel applications, such as computer programs, tailored to the human visual system to enhance recognition in low visibility conditions, e.g., of grainy images. My short-term objectives focus on specific statistical regularities that arise from (1) viewing distance to faces (e.g., visual size and blur), (2) ethnicities of faces most prevalent in the social environment, and (3) overall face exposure duration, in pursuit of the long-term objectives above. In the next 5-year cycle, we will continue to rely on our expertise in methodologies such as behavioural testing (e.g., visual psychophysics), computational modelling (e.g., ideal observers), and naturalistic observation of visual image statistics, (e.g., based on footage acquired through eyewear-embedded cameras). Highly qualified personnel at all levels will gain scientific and technical skills that will serve them in future careers in academia, industry and medicine. This work will advance our understanding of the fundamental principles underlying visual recognition in the brain and inform the development of tools that improve recognition of high-level form in a variety of settings from computer applications to visual aids in low-visibility conditions.

视觉识别很难。物体在各种不同的照明条件下被看到，观看距离和方向，这给它们的图像带来了巨大的变化。此外，观察者的因素从眼睛和大脑引入的扭曲到观察者一生中不同的视觉体验都会影响视觉识别。尽管取得了重大进展，但对这些因素在视觉识别计算中的影响的全面理解仍然是难以捉摸的。我们最近的研究表明，视觉系统利用持续的反射，例如，那些在大小或表情，在视觉输入，以促进识别的面孔。在一项自然主义的观察研究中，我们通过眼镜嵌入式摄像机获取了日常视觉体验的镜头。对这段录像的分析显示，大多数人的面孔都来自社会互动距离，即，视觉上大。事实上，我们发现在社交互动中最常见的面部大小与更好的识别有关。我们在尺寸和模糊方面的工作也发现了与该提议的总体假设一致的证据，即视觉系统根据环境中最常见的视觉输入类型采取策略来优化识别性能。我研究的长期目标是：（1）描述人类观察者视觉输入中的各种统计干扰及其对识别的影响，包括物理干扰，例如由光学几何学引起的干扰，以及观察者与其环境相互作用方式的后果，（2）揭示视觉系统的策略，利用这些策略来促进识别，（3）开发新的应用程序，如计算机程序，为人类视觉系统量身定制，以增强低能见度条件下的识别，例如，颗粒状的图像。我的短期目标集中在特定的统计数据上，这些数据来自（1）到人脸的观看距离（例如，视觉尺寸和模糊度），（2）在社会环境中最普遍的面部种族，以及（3）整体面部暴露持续时间，以追求上述长期目标。在下一个5年周期，我们将继续依靠我们在行为测试（例如，视觉心理物理学），计算建模（例如，理想观察者），以及视觉图像统计的自然观察，（例如，基于通过眼镜嵌入式摄像机获取的连续镜头）。各级高素质人员将获得科学和技术技能，这将有助于他们在学术界，工业界和医学界的未来职业生涯。这项工作将促进我们对大脑视觉识别基本原理的理解，并为开发工具提供信息，这些工具可以在各种环境中提高对高级形式的识别，从计算机应用到低可见度条件下的视觉辅助。