González Martin, Sánchez-Pedraza Antonio, Marfil Rebeca, Rodríguez Juan A, Bandera Antonio
Dpto. Tecnología Electrónica, University of Málaga, Campus de Teatinos, 29071 Málaga, Spain.
Sensors (Basel). 2016 Nov 26;16(12):2003. doi: 10.3390/s16122003.
There exist image processing applications, such as tracking or pattern recognition, that are not necessarily precise enough to maintain the same resolution across the whole image sensor. In fact, they must only keep it as high as possible in a relatively small region, but covering a wide field of view. This is the aim of foveal vision systems. Briefly, they propose to sense a large field of view at a spatially-variant resolution: one relatively small region, the fovea, is mapped at a high resolution, while the rest of the image is captured at a lower resolution. In these systems, this fovea must be moved, from one region of interest to another one, to scan a visual scene. It is interesting that the part of the scene that is covered by the fovea should not be merely spatial, but closely related to perceptual objects. Segmentation and attention are then intimately tied together: while the segmentation process is responsible for extracting perceptively-coherent entities from the scene (proto-objects), attention can guide segmentation. From this loop, the concept of foveal attention arises. This work proposes a hardware system for mapping a uniformly-sampled sensor to a space-variant one. Furthermore, this mapping is tied with a software-based, foveal attention mechanism that takes as input the stream of generated foveal images. The whole hardware/software architecture has been designed to be embedded within an all programmable system on chip (AP SoC). Our results show the flexibility of the data port for exchanging information between the mapping and attention parts of the architecture and the good performance rates of the mapping procedure. Experimental evaluation also demonstrates that the segmentation method and the attention model provide results comparable to other more computationally-expensive algorithms.
存在一些图像处理应用，比如跟踪或模式识别，它们不一定精确到足以在整个图像传感器上保持相同的分辨率。事实上，它们只需要在一个相对较小的区域内尽可能保持高分辨率，但要覆盖广阔的视野。这就是中央凹视觉系统的目标。简而言之，它们提议以空间可变分辨率感知广阔的视野：一个相对较小的区域，即中央凹，以高分辨率映射，而图像的其余部分以较低分辨率捕获。在这些系统中，这个中央凹必须从一个感兴趣区域移动到另一个区域，以扫描视觉场景。有趣的是，被中央凹覆盖的场景部分不应仅仅是空间上的，而应与感知对象密切相关。分割和注意力因此紧密相连：分割过程负责从场景中提取感知上连贯的实体（原始对象），而注意力可以引导分割。从这个循环中，产生了中央凹注意力的概念。这项工作提出了一种硬件系统，用于将均匀采样的传感器映射到空间可变的传感器。此外，这种映射与一种基于软件的中央凹注意力机制相关联，该机制将生成的中央凹图像流作为输入。整个硬件/软件架构被设计为嵌入在一个全可编程片上系统（AP SoC）中。我们的结果展示了架构中映射和注意力部分之间用于交换信息的数据端口的灵活性以及映射过程的良好性能率。实验评估还表明，分割方法和注意力模型提供的结果与其他计算成本更高的算法相当。
