Carpenter Gail A, Gaddam Chaitanya Sai, Mingolla Ennio
Department of Cognitive and Neural Systems, Boston University, 677 Beacon Street, Boston, MA 02215, USA.
Neural Netw. 2007 Dec;20(10):1109-31. doi: 10.1016/j.neunet.2007.10.002. Epub 2007 Oct 12.
CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the "early vision" stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once the pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.
CONTOUR FIGURE GRound（CONFIGR，轮廓图形背景）是一种基于生物视觉原理的计算模型，用于完成稀疏且有噪声的图像图形。在一个集成的视觉/识别系统中，CONTOUR FIGURE GRound（CONFIGR）假设有一个初始识别阶段，该阶段从空间局部输入信息中识别图形像素。由此产生的、通常不完整的图形会反馈到“早期视觉”阶段，通过填充来进行远距离的图形补全。然后，重建后的图像会再次呈现给识别系统，以执行诸如物体识别等全局功能。在CONTOUR FIGURE GRound（CONFIGR）算法中，最小的独立图像单元是可见像素，其大小定义了一个计算空间尺度。一旦像素大小确定，整个算法就完全确定了，无需额外的参数选择。多尺度模拟展示了该视觉/识别系统。CONTOUR FIGURE GRound（CONFIGR）的开源代码可在线获取，但所有示例都可以通过解析得出，并且在每一步应用的设计原则都是透明的。该模型在将图形填充与作为背景的互补填充之间取得平衡，从而阻止虚假的图形补全。叶计算在亚像素空间尺度上进行。CONTOUR FIGURE GRound（CONFIGR）系统最初设计用于填充不完整图像（如虚线）中缺失的轮廓，同样也能连接和分割稀疏的点，并将在初始识别阶段局部识别为图形的碎片统一成被遮挡的物体。该模型会自我调整其补全距离，在不受阻碍的情况下跨越任何长度的间隙进行填充，同时限制已经具有内在形式的密集图像图形像素组之间的连接。远距离图像补全有望在从高度压缩的视频和静态相机图像中重建图像的自适应处理器中发挥重要作用。
