Class-based recognition of 3D Objects represented by volumetric primitives

This paper presents a novel approach to recognizing 3D complex objects that have similar geometric structure but belong to different subclasses. Test scenes are acquired by a laser striper as range images, and the objects are modelled using a composite volumetric representation of superquadrics and geons. Matching is decomposed into two stages: first, an indexing scheme designed to make effective use of the symbolic keys of the representation is implemented in order to direct the search to the models containing the parts identified; second, a method is proposed where the hypotheses picked from the index are searched using an Interpretation Tree algorithm combined with a quality measure to evaluate the bindings and the final valid hypotheses based on Possibility Theory, or Theory of Fuzzy Sets. The valid hypotheses ranked by the matching process are then passed to the pose estimation module. 1 Introduction This paper is concerned with a matching approach developed for model-based recog..

A feasibility study on the use of agent-based image recognition on a desktop computer for the purpose of quality control in a production environment

Thesis (M. Tech.) - Central University of Technology, Free State, 2006A multi-threaded, multi-agent image recognition software application called RecMaster has been developed specifically for the purpose of quality control in a production environment. This entails using the system as a monitor to identify invalid objects moving on a conveyor belt and to pass on the relevant information to an attached device, such as a robotic arm, which will remove the invalid object. The main purpose of developing this system was to prove that a desktop computer could run an image recognition system efficiently, without the need for high-end, high-cost, specialised computer hardware. The programme operates by assigning each agent a task in the recognition process and then waiting for resources to become available. Tasks related to edge detection, colour inversion, image binarisation and perimeter determination were assigned to individual agents. Each agent is loaded onto its own processing thread, with some of the agents delegating their subtasks to other processing threads. This enables the application to utilise the available system resources more efficiently. The application is very limited in its scope, as it requires a uniform image background as well as little to no variance in camera zoom levels and object to lens distance. This study focused solely on the development of the application software, and not on the setting up of the actual imaging hardware. The imaging device, on which the system was tested, was a web cam capable of a 640 x 480 resolution. As such, all image capture and processing was done on images with a horizontal resolution of 640 pixels and a vertical resolution of 480 pixels, so as not to distort image quality. The application locates objects on an image feed - which can be in the format of a still image, a video file or a camera feed - and compares these objects to a model of the object that was created previously. The coordinates of the object are calculated and translated into coordinates on the conveyor system. These coordinates are then passed on to an external recipient, such as a robotic arm, via a serial link. The system has been applied to the model of a DVD, and tested against a variety of similar and dissimilar objects to determine its accuracy. The tests were run on both an AMD- and Intel-based desktop computer system, with the results indicating that both systems are capable of efficiently running the application. On average, the AMD-based system tended to be 81% faster at matching objects in still images, and 100% faster at matching objects in moving images. The system made matches within an average time frame of 250 ms, making the process fast enough to be used on an actual conveyor system. On still images, the results showed an 87% success rate for the AMD-based system, and 73% for Intel. For moving images, however, both systems showed a 100% success rate

3D human body modelling from range data

This thesis describes the design, implementation and application of an integrated and fully automated system for interpreting whole-body range data. The system is shown to be capable of generating complete surface models of human bodies, and robustly extracting anatomical features for anthropometry, with minimal intrusion on the subject. The ability to automate this process has enormous potential for personalised digital models in medicine, ergonomics, design and manufacture and for populating virtual environments. The techniques developed within this thesis now form the basis of a commercial product. However, the technical difficulties are considerable. Human bodies are highly varied and many of the features of interest are extremely subtle. The underlying range data is typically noisy and is sparse at occluded areas. In addressing these problems this thesis makes five main research contributions. Firstly, the thesis describes the design, implementation and testing of the whole integrated and automated system from scratch, starting at the image capture hardware. At each stage the tradeoffs between performance criteria are discussed, and experiments are described to test the processes developed. Secondly, a combined data-driven and model-based approach is described and implemented, for surface reconstruction from the raw data. This method addresses the whole body surface, including areas where body segments touch, and other occluded areas. The third contribution is a library of operators, designed specifically for shape description and measurement of the human body. The library provides high-level relational attributes, an "electronic tape measure" to extract linear and curvilinear measurements,as well as low-level shape information, such as curvature. Application of the library is demonstrated by building a large set of detectors to find anthropometric features, based on the ISO 8559 specification. Output is compared against traditional manual measurements and a detailed analysis is presented. The discrepancy between these sets of data is only a few per cent on most dimensions, and the system's reproducibility is shown to be similar to that of skilled manual measurers. The final contribution is that the mesh models and anthropometric features, produced by the system, have been used as a starting point to facilitate other research, Such as registration of multiple body images,draping clothing and advanced surface modelling techniques