Study on an environmental embedding type "eye camera"

在这项研究中，我们提出了一种利用虹膜轮廓来估计视觉方向的复杂方法。这种方法只需要一台焦距未知的相机拍摄一张单眼图像。为了估计视觉方向，我们假设双眼的视觉方向是平行的，虹膜边界在三维空间中是圆的。在这种情况下，虹膜边界所在的两个平面也是平行的。我们使用扩展的“双圆”算法从输入图像中提取虹膜轮廓来估计两个平面的法向量。在本研究中，我们还提出了一种通用的目标跟踪方法（称为“K-means跟踪器”），该方法通过基于K-means聚类的算法将搜索区域内的像素划分为“目标”组和“背景”组来实现。我们首先使用5D特征向量来均匀地描述每个像素的颜色（“Y，U，V”）和位置（“x， Y”）。这使得在跟踪过程中能够同时适应颜色和几何特征。其次，我们使用一个可变椭圆模型来描述搜索区域的形状，并对周围的背景进行建模。这保证了在各种几何变换下的稳定目标跟踪。此外，我们提出了一种高性能的目标跟踪系统，通过控制安装在电机驱动的平移装置上的有源摄像机，以视频速率获得运动物体的高质量图像。我们使用“K-means跟踪器”在图像空间上跟踪目标。根据“k -均值跟踪器”的结果，采用PID控制方案控制平移装置的角度位置和速度。利用该系统，我们可以获得运动物体的无运动模糊图像。

In this research, we propose a sophisticated method to estimate visual direction using iris contours. This method requires only one monocular image taken by a camera with unknown focal length. In order to estimate the visual direction, we assume the visual directions of both eyes are parallel and iris boundaries are circles in 3D space. In this case, the two planes where the iris boundaries reside are also parallel. We estimate the normal vector of the two planes from the iris contours extracted from an input image by using an extended "two-circle" algorithm.In this research, we also propose a general object tracking method (called as "K-means tracker"), which is realized by classifying the pixels within the search area into "target" and "background" groups with a K-means clustering based algorithm. We first use a 5D feature vector to describe both the color ("Y,U,V") and the position ("x,y") of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking. Secondly, we use a variable ellipse model to describe the shape of the search area and to model the surrounding background. This guarantees the stable object tracking under various geometric transformations.Moreover, we propose a high performance object tracking system for obtaining high quality images of a moving object at video rate by controlling an active camera mounted on a motor droved pan-tilt unit. We use "K-means tracker" for tracking object on image space. The PID control scheme is employed for controlling the angular position and speed of the pan-tilt units according to the results of the "K-means tracker". With this system, we can obtain motion-blur-free images of a moving object.