SBIR Phase I: Neuromorphic Color Sensor for Object and Place Recognition

Iguana Robotics，Inc.的这个小型企业创新研究第一阶段项目建议构建一种单芯片、彩色aVLSI/dVLSI神经形态传感器(摄像头)，具有板载分割以及物体和位置识别能力。对于每个像素，该芯片将计算到焦平面中的色调和饱和值的转换，基于颜色执行类分配，并基于类分配执行直方图。这个芯片将成为共生系统的一部分。除了芯片，鬣蜥机器人公司还将设计特殊的目标颜色和纹理编码图案。通过设计容易识别的目标，调查人员将达到很高的识别率。该设备的用途是：(1)能够将符号标签与自然环境中的物体相关联；(2)估计物体的位置；(3)作为人机交互应用的预处理步骤，跟踪并找到图像中的人脸和手；(4)为个人机器人提供位置识别技术。此外，这种芯片将(1)非常低的功耗(1 MW)；(2)非常低的成本；以及(3)非常小和紧凑。这些功能将促进该对象在成本和电力敏感型应用中的广泛使用。鬣蜥机器人公司为对象和位置识别提供低成本、紧凑的神经形态颜色传感器，可以为娱乐、教育、工业、界面和银行等不同应用领域提供实时对象和位置识别。该研究项目有可能对手势识别中自动化的发现和理解产生重大影响，并推动片上计算机视觉硬件的最先进水平。

This Small Business Innovation Research Phase I project from Iguana Robotics, Inc. proposes the construction of a single chip, color aVLSI/dVLSI Neuromorphic sensor (camera) with onboard segmentation and object and place recognition capability. For each pixel, this chip will compute the transformation to hue and saturation values in the focal plane, perform class assignment based on color, and perform histogramming based on the class assignment. This chip will be part of a symbiotic system. In addition to the chip, Iguana Robotics will design special target color and texture coded patterns. By designing the targets to be easy to recognize, the investigators will achieve a very high recognition rate. The utility of this device is that it will (1) be capable of associating symbolic tags with objects in natural environment; (2) estimate the position of objects; (3) track and find human face and hands in an image as a pre-processing step in HCI applications; and (4) provide a technique for place recognition for personal robots. In addition, this chip that will be (1) very low power (1mw); (2) very low cost; and (3) very small and compact. These features will facilitate the wide spread use of this object in cost and power sensitive applications.Iguana Robotics proffers low-cost, compact neuromorphic color sensor for object and place recognition that can provide real-time object and place recognition for diverse areas of application such as entertainment, education, industry, interfaces, and banking. The research project has the potential to make a significant impact on the discovery and understanding of automation in gesture recognition and to advance the state-of-the-art in on-chip computer vision hardware.