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一个物体识别任务的观点复杂性。

The viewpoint complexity of an object-recognition task.

作者信息

Tjan B S, Legge G E

机构信息

Max-Planck-Institut für biologische Kybernetik, Tübingen, Germany.

出版信息

Vision Res. 1998 Aug;38(15-16):2335-50. doi: 10.1016/s0042-6989(97)00255-1.

DOI:10.1016/s0042-6989(97)00255-1
PMID:9798003
Abstract

There is an ongoing debate about the nature of perceptual representation in human object recognition. Resolution of this debate has been hampered by the lack of a metric for assessing the representational requirements of a recognition task. To recognize a member of a given set of 3-D objects, how much detail must the objects' representations contain in order to achieve a specific accuracy criterion? From the performance of an ideal observer, we derived a quantity called the view complexity (VX) to measure the required granularity of representation. VX is an intrinsic property of the object-recognition task, taking into account both the object ensemble and the type of decision required of an observer. It does not depend on the visual representation or processing used by the observer. VX can be interpreted as the number of randomly selected 2-D images needed to represent the decision boundaries in the image space of a 3-D object-recognition task. A low VX means the task is inherently more viewpoint invariant and a high VX means it is inherently more viewpoint dependent. By measuring the VX of recognition tasks with different object sets, we show that the current confusion about the nature of human perceptual representation is partly due to a failure in distinguishing between human visual processing and the properties of a task and its stimuli. We find general correspondence between the VX of a recognition task and the published human data on viewpoint dependence. Exceptions in this relationship motivated us to propose the view-rate hypothesis: human visual performance is limited by the equivalent number of 2-D image views that can be processed per unit time.

摘要

关于人类物体识别中感知表征的本质,目前存在一场争论。由于缺乏一种评估识别任务表征要求的度量标准,这场争论的解决受到了阻碍。为了识别给定三维物体集合中的一个成员,物体表征必须包含多少细节才能达到特定的准确性标准?从理想观察者的表现出发,我们推导出了一个称为视图复杂度(VX)的量,以测量所需的表征粒度。VX是物体识别任务的一个固有属性,它既考虑了物体集合,也考虑了观察者所需做出的决策类型。它不依赖于观察者使用的视觉表征或处理方式。VX可以解释为在三维物体识别任务的图像空间中表示决策边界所需随机选择的二维图像数量。低VX意味着该任务本质上更具视角不变性,高VX意味着它本质上更依赖视角。通过测量不同物体集合的识别任务的VX,我们表明,目前关于人类感知表征本质的困惑部分是由于未能区分人类视觉处理与任务及其刺激的属性。我们发现识别任务的VX与已发表的关于视角依赖性的人类数据之间存在普遍对应关系。这种关系中的例外情况促使我们提出视图速率假说:人类视觉表现受每单位时间可处理的二维图像视图等效数量的限制。

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