Design of automatic vision-based inspection system for solder joint segmentation

Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions.

Robust real time multi-layer foreground segmentation

Many surveillance applications (object tracking, abandoned object detection) rely on detecting changes in a scene. Foreground segmentation is an effective way to extract the foreground from the scene, but these techniques cannot discriminate between objects that have temporarily stopped and those that are moving. We propose a series of modifications to an existing foreground segmentation system\cite{Butler2003} so that the foreground is further segmented into two or more layers. This yields an active layer of objects currently in motion and a passive layer of objects that have temporarily ceased motion which can itself be decomposed into multiple static layers. We also propose a variable threshold to cope with variable illumination, a feedback mechanism that allows an external process (i.e. surveillance system) to alter the motion detectors state, and a lighting compensation process and a shadow detector to reduce errors caused by lighting inconsistencies. The technique is demonstrated using outdoor surveillance footage, and is shown to be able to effectively deal with real world lighting conditions and overlapping objects.

Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

Accurate silhouette segmentation using motion detection and graph cuts

Acquiring accurate silhouettes has many applications in computer vision. This is usually done through motion detection, or a simple background subtraction under highly controlled environments (i.e. chroma-key backgrounds). Lighting and contrast issues in typical outdoor or office environments make accurate segmentation very difficult in these scenes. In this paper, gradients are used in conjunction with intensity and colour to provide a robust segmentation of motion, after which graph cuts are utilised to refine the segmentation. The results presented using the ETISEO database demonstrate that an improved segmentation is achieved through the combined use of motion detection and graph cuts, particularly in complex scenes.

Fusing shrinking and expanding active contour models for robust IRIS segmentation

The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non- ideal iris images. This paper proposes an improved method to localise non-circular iris images quickly and accurately. Shrinking and expanding active contour methods are consolidated when localising inner and outer iris boundaries. First, the pupil region is roughly estimated based on histogram thresholding and morphological operations. There- after, a shrinking active contour model is used to precisely locate the inner iris boundary. Finally, the estimated inner iris boundary is used as an initial contour for an expanding active contour scheme to find the outer iris boundary. The proposed scheme is robust in finding exact the iris boundaries of non-circular and off-angle irises. In addition, occlusions of the iris images from eyelids and eyelashes are automatically excluded from the detected iris region. Experimental results on CASIA v3.0 iris databases indicate the accuracy of proposed technique.

Smart grids : better performance through communication and algorithms

There has been a developing interest in smart grids, the possibility of significantly enhanced performance from remote measurements and intelligent controls. For transmission the use of PMU signals from remote sites and direct load shed controls can give significant enhancement for large system disturbances rather than relying on local measurements and linear controls. This lecture will emphasize what can be found from remote measurements and the mechanisms to get a smarter response to major disturbances. For distribution systems there has been a significant history in the area of distribution reconfiguration automation. This lecture will emphasize the incorporation of Distributed Generation into distribution networks and the impact on voltage/frequency control and protection. Overall the performance of both transmission and distribution will be impacted by demand side management and the capabilities built into the system. In particular, we consider different time scales of load communication and response and look to the benefits for system, energy and lines.

Channel tracking algorithms for highly efficient wireless broadband communications in rural areas

High-speed broadband internet access is widely recognised as a catalyst to social and economic development, having a significant impact on global economy. Rural Australia’s inherent dispersed population over a large geographical area make the delivery of efficient, well-maintained and cost-effective internet a challenging task. The novel and highly-efficient Multi-User-Single-Antenna for MIMO (MUSA-MIMO) broadband wireless communication technology can effectively be used to deliver wireless broadband access to rural areas. This research aims to develop for the first time, an efficient and accurate algorithm for the tracking and prediction of Channel State Information (CSI) at the transmitter, by characterising time variation effects of the wireless communication channel on the performance of a highly-efficient MUSA-MIMO technology particularly suited for rural communities, improving their quality of life and economic prosperity.

Evolutionary algorithms for sustainable building design

This approach to sustainable design explores the possibility of creating an architectural design process which can iteratively produce optimised and sustainable design solutions. Driven by an evolution process based on genetic algorithms, the system allows the designer to “design the building design generator” rather than to “designs the building”. The design concept is abstracted into a digital design schema, which allows transfer of the human creative vision into the rational language of a computer. The schema is then elaborated into the use of genetic algorithms to evolve innovative, performative and sustainable design solutions. The prioritisation of the project’s constraints and the subsequent design solutions synthesised during design generation are expected to resolve most of the major conflicts in the evaluation and optimisation phases. Mosques are used as the example building typology to ground the research activity. The spatial organisations of various mosque typologies are graphically represented by adjacency constraints between spaces. Each configuration is represented by a planar graph which is then translated into a non-orthogonal dual graph and fed into the genetic algorithm system with fixed constraints and expected performance criteria set to govern evolution. The resultant Hierarchical Evolutionary Algorithmic Design System is developed by linking the evaluation process with environmental assessment tools to rank the candidate designs. The proposed system generates the concept, the seed, and the schema, and has environmental performance as one of the main criteria in driving optimisation.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

The Machine Vision Toolbox : A MATLAB Toolbox for vision and vision-based control

The article described an open-source toolbox for machine vision called Machine Vision Toolbox (MVT). MVT includes more than 60 functions including image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration, and color space conversion. MVT can be used for research into machine vision but is also versatile enough to be usable for real-time work and even control. MVT, combined with MATLAB and a model workstation computer, is a useful and convenient environment for the investigation of machine vision algorithms. The article illustrated the use of a subset of toolbox functions for some typical problems and described MVT operations including the simulation of a complete image-based visual servo system.