The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods: It does neither require prior training nor learning, nor knowledge of the camera parameters, nor explicit point correspondences or matching features between image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object, and works on a single static image from a given view, and under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection are presented.
A vision system has been developed which can determine the position and orientation of complex curved objects in gray-level noisy scenes. The system organizes and reduces the image data from a digitized picture to a compact representation having the appearance of a line drawing. This compact image representation can be used for forming a model under favorable viewing conditions or for locating a part under poor viewing conditions by a matching process that uses a previously formed model. Thus, models are formed automatically by having the program view the part under favorable lighting and background conditions. The compact image representation describes the boundaries of the part. As a step to automate assembly of various industrial parts, this paper describes a versatile machine vision system that can recognize a variety of complex industrial parts and measure the necessary parameters for assembly, such as the locations of screw holes. Emphasis is given to a method for extracting useful features from the scene data for complex industrial parts so that accurate recognition of them is possible. The proposed method has the following features: 1) simple features are detected first in the scene and more complex features are examined later, using the locations of the previously found features; 2) the system is provided with a high-level supervisor that analyzes the current information obtained from the scene and structural models of various objects, and proposes the feartures to be examined next for recognizing the objects in the scene; 3) the supervisor has problem-solving capabilities to select the most promising feature among many others; 4) the structural models are used to suggest the locations of the features to be examined; and 5) several sophisticated feature extractors are used to detect the complex features. An effort is also made to make the system versatile so that it can be readily applied to a variety of different industrial parts. The proposed system has been tested on several sets of parts for small industrial gasoline engines and the results were satisfactory. 