A Native imitative computer vision for a following generation human interface

联合收割机的鲁棒性和实时性是未来人机界面计算机视觉系统的重要组成部分。为此，我们必须建立一种新的图像理解方法，将图像表示为层次结构化的信息。在本研究中，我们提出了一种新的方法多分辨率分析的图像模式。我们还将所提出的方法应用到一些问题，包括人脸检测，这是很重要的应用领域的人机界面。在多分辨率分析方面，本文采用了一种新的"尺度空间图像偶极子分析"方法。在每一尺度下，图像被表示为偶极网络，它由图像的局部极大点和局部极小点组成。而且，每个尺度上的网络沿该尺度沿着连接并形成超网络。在这个超网络上，我们也将目标对象（例如人脸）表示为子图结构，并且我们可以检测这个图表示的对象。在此基础上，提出了一种新的目标检测方法，并将其应用于人机接口中，实现了一个鲁棒性强、实时性好的系统。在人脸检测应用中，通过多分辨率分析，从人脸样本集中提取了人脸偶极子网络的标准模式。对于输入图像，我们得到超网络，并将子图作为人脸的候选与标准模式进行匹配。并对候选项进行了详细的分析，最后得出检测结果.

It is important to combine the robustness with the real-time processing, when we build a computer-vision system for future-generation human interface. For this purpose, we must establish a new image understanding method that represents the image as a hierarchical structured information. In this research, we propose a new method multi-resolution analysis for an image pattern. We also apply the proposed method to some problems including Face Detection, which is important in the area of the application to the human interface. The results show the proposed method works efficiently with our system.For the multi-resolution analysis, we adopt a new method called "Image dipole analysis in Scale-Space". At each scale, the image is represented as a dipole network, which consist of local-maximal point and local-minimal point of the image. Moreover, the network at each scale is connected along the scale and forms a hyper network. On this hyper network, we also represent the target object (for example, human face) as the subgraph structure and we can detect the object this graph representation. Based on this image representation, we propose a new object detection method and apply this to human interface and realize a robust and real-time system.In the application to the face detection, we prepare a standard pattern of the dipole network of human face from a sample set with multi-resolution analysis. For an input image, we obtain the hyper network and match the subgraph which is the candidates of the face with standard pattern. Moreover, we analyze the candidates in detail and finally we acarise the result of detection.