基于图象比对技术的柔性印刷电路板检测系统

柔性印刷电路板(FPCB)以其特有的优点得到了广泛的应用,但传统的人工检测方法提高了生产成本,降低了效率。为了克服这些问题,笔者开发了基于图像模板比对的柔性印刷电路板快速检测的方法。利用标准模板图象与被测产品图象比对,实现了印刷线路几何尺寸的快速检测;应用图象边缘分析技术,实现了对产品的微小缺陷识别,并具此自动划分产品质量等级。对实施结果表明,该系统采用的方法能够满足生产要求,为实现柔性印刷电路板全面自动化检测奠定了良好的基础。     

Resource-awareness on heterogeneous MPSoCs for image processing

Multiprocessor system-on-chip (MPSoC) designs offer a lot of computational power assembled in a compact design. The computing power of MPSoCs can be further augmented by adding massively parallel processor arrays (MPPA) and specialized hardware with instruction-set extensions. On-chip MPPAs can be used to accelerate low-level image-processing algorithms with massive inherent parallelism. However, the presence of multiple processing elements (PEs) with different characteristics raises issues related to programming and application mapping, among others. The conventional approach used for programming heterogeneous MPSoCs results in a static mapping of various parts of the application to different PE types, based on the nature of the algorithm and the structure of the PEs. Yet, such a mapping scheme independent of the instantaneous load on the PEs may lead to under-utilization of some type of PEs while overloading others.In this work, we investigate the benefits of using a heterogeneous MPSoC for accelerating various stages within a real-world image-processing algorithm for object-recognition. A case study demonstrates that a resource-aware programming model called Invasive Computing helps to improve the throughput and worst observed latency of the application program, by dynamically mapping applications to different types of PEs available on a heterogeneous MPSoC.     

Image registration for automated inspection of printed circuit patterns using CAD reference data

An image registration approach for inspection of printed circuit patterns which has been validated on a prototype system is described. Theoffline procedure forms, selects, prioritizes, and sorts registration features from CAD-generated reference data. A feature is selected if it satisfies clearance rules that account for the maximum expecteddiscongruence between captured and reference images. The sorting scheme considers the detection complexity of a feature and its distance away from the center of the expected image, since outer features represent potential global distortions better. Theruntime registration procedure detects features and finds the parameters that transform pixels into reference data coordinates and vice versa. We represent robust feature-measurement techniques that offer accurate subpixel localization and verify feature authenticity. We describe an edge-detection technique based on a novel way of authenticating zero-crossings and a method that disqualifies edges detected on defects of the part under inspection.     

A reconfigurable computing framework for multi-scale cellular image processing

Cellular computing architectures represent an important class of computation that are characterized by simple processing elements, local interconnect and massive parallelism. These architectures are a good match for many image and video processing applications and can be substantially accelerated with Reconfigurable Computers. We present a flexible software/hardware framework for design, implementation and automatic synthesis of cellular image processing algorithms. The system provides an extremely flexible set of parallel, pipelined and time-multiplexed components which can be tailored through reconfigurable hardware for particular applications. The most novel aspects of our framework include a highly pipelined architecture for multi-scale cellular image processing as well as support for several different pattern recognition applications. In this paper, we will describe the system in detail and present our performance assessments. The system achieved speed-up of at least 100× for computationally expensive sub-problems and 10× for end-to-end applications compared to software implementations.     

Rushing out detection system for safe driving using foveated image processing

We propose a detection system for a dangerous situation while driving using foveated image processing and a neural network (NN). This system detects the situation of a man rushing out who must be avoided while driving. A foveated image is transformed by log polar mapping (LPM) from an image acquired from a camera mounted on a car. LPM imitates the primate visual system, and it is possible not only to obtain both a high central resolution and a wide field of view, but also to significantly reduce processing image data. This transformed image is suited to the detection of an object that moves toward the camera. To detect this object, we calculate flowow vectors on time-scaled images, and process them by a NN that outputs a warning signal when a dangerous situation is detected. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

CYCLOPS: A mobile robotic platform for testing and validating image processing and autonomous navigation algorithms in support of artificial vision prostheses

While artificial vision prostheses are quickly becoming a reality, actual testing time with visual prosthesis carriers is at a premium. Moreover, it is helpful to have a more realistic functional approximation of a blind subject. Instead of a normal subject with a healthy retina looking at a low-resolution (pixelated) image on a computer monitor or head-mounted display, a more realistic approximation is achieved by employing a subject-independent mobile robotic platform that uses a pixelated view as its sole visual input for navigation purposes. We introduce CYCLOPS: an AWD, remote controllable, mobile robotic platform that serves as a testbed for real-time image processing and autonomous navigation systems for the purpose of enhancing the visual experience afforded by visual prosthesis carriers. Complete with wireless Internet connectivity and a fully articulated digital camera with wireless video link, CYCLOPS supports both interactive tele-commanding via joystick, and autonomous self-commanding. Due to its onboard computing capabilities and extended battery life, CYCLOPS can perform complex and numerically intensive calculations, such as image processing and autonomous navigation algorithms, in addition to interfacing to additional sensors. Its Internet connectivity renders CYCLOPS a worldwide accessible testbed for researchers in the field of artificial vision systems. CYCLOPS enables subject-independent evaluation and validation of image processing and autonomous navigation systems with respect to the utility and efficiency of supporting and enhancing visual prostheses, while potentially reducing to a necessary minimum the need for valuable testing time with actual visual prosthesis carriers.