A Micropower Interface ASIC for a Capacitive 3-Axis Micro-Accelerometer

In this paper, a micropower interface IC for a capacitive 3-axis micro-accelerometer implemented in a 0.13- BiCMOS process is presented. The sensor interface consists of a front-end that converts the acceleration signal to voltage, two algorithmic ADCs, two frequency references, and a voltage, current, and temperature reference circuit. Die area and power dissipation are reduced by using time-multiplexed sampling and varying duty cycles down to 0.3%. The chip with a 0.51 active area draws 62 from a 1.8 V supply while sampling temperature at 100 Hz, and four proof masses, each at 1.04 kHz. With a 4-g capacitive 3-axis accelerometer, the measured noise floors in the x-, y-, and z-directions are 482 , 639 , and 662 , respectively.     

Local and global Gabor features for object recognition

Invariant object recognition is one of the most central problems in computer vision. To be successful when occlusion and distortions are present, object recognition has to be based on local features. The features should express the significant information while being robust in the presence of noise and distortions, and stable in terms of feature parameters. In this study, Gabor filtering based features is analyzed in terms of the above requirements. Two classes of Gabor features are introduced: global Gabor features and fundamental frequency Gabor features. The Gabor filter response and stability issues are analyzed in terms of the filtering parameters. The robustness of the proposed features is examined through experiments. Both analytical and experimental results indicate that when certain conditions on the filter parameters are met, Gabor filtering can be reliably used in low-level feature extraction in image processing, and the filter responses can be used to construct robust invariant recognition systems.     

Detection and decision-support diagnosis of diabetic retinopathy using machine vision

This invited paper considers the results of the IMAGERET project. The goal of the project is to demonstrate how lesions in a retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. The project consists of the following results: an image annotation tool for medical expert annotation, diabetic retinopathy databases, an evaluation framework for development and comparison of methods, image-based and pixel-based methods, and new imaging solutions. The automated diagnosis can be seen in two steps: in the first step, it is decided whether an eye needs further analysis (too many lesions visible) or not (the eye is healthy enough). In the second step, fundus images selected for further analysis are automatically diagnosed. The developed system saves both resources of medical experts and costs in healthcare. It will further offer a tool for the health care providers to improve the quality of the life of diabetes patients. This is important since the number of diabetes patients is increasing, especially rapidly in the developed countries.     

Diagnosis tool for motor condition monitoring

In the modern industrial environment there is increasing demand for automatic condition monitoring. With reliable condition monitoring, faults such as mechanical motor failures could be identified in their early stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories. A generic condition diagnosis tool is introduced in this paper. The tool is based on discriminative energy functions which reveal discriminative frequency-domain regions where failures can be identified. The tool was applied to induction motor bearing fault detection and succeeded in finding characteristic frequencies which allow accurate detection of bearing faults.     

Thresholding-based detection of fine and sparse details

Fine and sparse details appear in many quality inspection applications requiring machine vision. Especially on flat surfaces, such as paper or board, the details can be made detectable by oblique illumination. In this study, a general definition of such details is given by defining sufficient statistical properties from histograms. The statistical model allows simulation of data and comparison of methods designed for detail detection. Based on the definition, utilization of the existing thresholding methods is shown to be well motivated. The comparison shows that minimum error thresholding outperforms the other standard methods. Finally, the results are successfully applied to a paper printability inspection application, and the IGT picking assessment, in which small surface defects must be detected. The provided method and measurement system prototype provide automated assessment with results comparable to manual expert evaluations in this laborious task.     

Differential Evolution Training Algorithm for Feed-Forward Neural Networks

An evolutionary optimization method over continuous search spaces, differential evolution, has recently been successfully applied to real world and artificial optimization problems and proposed also for neural network training. However, differential evolution has not been comprehensively studied in the context of training neural network weights, i.e., how useful is differential evolution in finding the global optimum for expense of convergence speed. In this study, differential evolution has been analyzed as a candidate global optimization method for feed-forward neural networks. In comparison to gradient based methods, differential evolution seems not to provide any distinct advantage in terms of learning rate or solution quality. Differential evolution can rather be used in validation of reached optima and in the development of regularization terms and non-conventional transfer functions that do not necessarily provide gradient information. This revised version was published online in June 2006 with corrections to the Cover Date.     

Detection of irregularities in regular patterns

Regular patterns, as defined in this study, are found in areas of industry and science, for example, halftone raster patterns used in the printing industry and crystal lattice structures in solid state physics. The need for quality inspection of products containing regular patterns has aroused interest in the application of machine vision for automatic inspection. Quality inspection typically corresponds to detecting abnormalities, defined as irregularities in this case. In this study, the problem of irregularity detection is described in analytical form and three different detection methods are proposed. All the methods are based on characteristics of the Fourier transform to compactly represent regular information. The Fourier transform enables the separation of regular and irregular parts of an input image. The three methods presented are shown to differ in their generality and computational complexities.     

Feature-based affine-Invariant localization of faces

We present a novel method for localizing faces in person identification scenarios. Such scenarios involve high resolution images of frontal faces. The proposed algorithm does not require color, copes well in cluttered backgrounds, and accurately localizes faces including eye centers. An extensive analysis and a performance evaluation on the XM2VTS database and on the realistic BioID and BANCA face databases is presented. We show that the algorithm has precision superior to reference methods.     

Invariance properties of gabor filter-based features--overview and applications.

For almost three decades the use of features based on Gabor filters has been promoted for their useful properties in image processing. The most important properties are related to invariance to illumination, rotation, scale, and translation. These properties are based on the fact that they are all parameters of Gabor filters themselves. This is especially useful in feature extraction, where Gabor filters have succeeded in many applications, from texture analysis to iris and face recognition. This study provides an overview of Gabor filters in image processing, a short literature survey of the most significant results, and establishes invariance properties and restrictions to the use of Gabor filters in feature extraction. Results are demonstrated by application examples.