在上世纪80年代初，David Marr开始在麻省理工学院的人工智能实验室人工视觉的机器人工作。他是人类视觉系统的专家，他的目标是了解为什么第一次尝试构建一个能够理解周围环境的机器人是不成功的。Marr认为，这是建立视觉科学基础的重要，而这样做；必须排除一切限制，取决于培训、文化考察的范围，等等，并注重视觉的机械或非自愿的方面。这种低层次的视觉是一部分，使我们能够重建的三维组织的物理世界，我们周围的激发，刺激视网膜。然后，他开发了工作的算法解决方案来回答每一个问题。Marr的理论是，图像处理在人类视觉系统具有复杂的层次结构，包括处理好几层。在每个处理水平，视网膜系统提供了一个可视化表示，以几何的方式逐步缩放。他的论点集中在对强度变化的检测上。他理论认为，强度变化发生在不同尺度的图像，使其最佳的检测需要使用不同大小的运营商。他还认为，突然强度变化产生的图像的一阶导数的峰值或波谷。这两个假设需要一个视觉滤波器有两个特点：它应该是一个微分算子，它应该能够被调谐到在任何所需的规模行动。Marr算子是一个小波，今天被称为“Marr wavelet”。
In the early 1980s, David Marr began work at MIT's Artificial Intelligence Laboratory on artificial vision for robots. He is an expert on the human visual system and his goal was to learn why the first attempts to construct a robot capable of understanding its surroundings were unsuccessful. Marr believed that it was important to establish scientific foundations for vision, and that while doing so; one must limit the scope of investigation by excluding everything that depends on training, culture, and so on, and focus on the mechanical or involuntary aspects of vision. This low-level vision is the part that enables us to recreate the three-dimensional organization of the physical world around us from the excitations that stimulate the retina. He then developed working algorithmic solutions to answer each of these questions. Marr's theory was that image processing in the human visual system has a complicated hierarchical structure that involves several layers of processing. At each processing level, the retinal system provides a visual representation that scales progressively in a geometrical manner. His arguments hinged on the detection of intensity changes. He theorized that intensity changes occur at different scales in an image, so that their optimal detection requires the use of operators of different sizes. He also theorized that sudden intensity changes produce a peak or trough in the first derivative of the image. These two hypotheses require that a vision filter have two characteristics: it should be a differential operator, and it should be capable of being tuned to act at any desired scale. Marr's operator was a wavelet that today is referred to as a "Marr wavelet."