Change Point Estimation of Bilevel Functions

Reconstruction of a bilevel function such as a bar code signal in a partially blind deconvolution problem is an important task in industrial processes. Existing methods are based on either the local approach or the regularization approach with a total variation penalty. This article reformulated the problem explicitly in terms of change points of the 0-1 step function. The bilevel function is then reconstructed by solving the nonlinear least squares problem subject to linear inequality constraints, with starting values provided by the local extremas of the derivative of the convolved signal from discrete noisy data. Simulation results show a considerable improvement of the quality of the bilevel function using the proposed hybrid approach over the local approach. The hybrid approach extends the workable range of the standard deviation of the Gaussian kernel significantly

Smart imaging using laser targeting: a multiple barcodes application

To the best of our knowledge, proposed is a novel variable depth of field smart imager design using intelligent laser targeting for high productivity multiple barcodes reading applications. System smartness comes via the use of an Electronically Controlled Variable Focal-Length Lens (ECVFL) to provide an agile pixel (and/or pixel set) within the laser transmitter and optical imaging receiver. The ECVFL in the receiver gives a flexible depth of field that allows clear image capture over a range of barcode locations. Imaging of a 660 nm wavelength laser line illuminated 95-bit one dimensional barcode is experimentally demonstrated via the smart imager for barcode target distances ranging from 10 cm to 54 cm. The smart system captured barcode images are evaluated using a proposed barcode reading algorithm. Experimental results after computer-based post-processing show a nine-fold increase in barcode target distance variation range (i.e., range variation increased from 2.5 cm to 24.5 cm) when compared to a conventional fixed lens imager. Applications for the smart imager include industrial multiple product tracking, marking, and inspection system

Peculiarities, classification and application area for 2d barcodes

У статті проаналізовано основні види сучасних двомірних штрихових кодів, визначено поняття «стековий штриховий код» і «матричний штриховий код», проведено порівняльну характеристику зарубіжного досвіду застосування стекових та матричних штрих-кодів, а також встановлено характер подальшого розвитку двомірних бар-кодів.В статье проанализированы основные виды современных двухмерных штрих-кодов, определено понятия «стековый штрих-код» и «матричный штрих-код», проведена сравнительная характеристика зарубежного опыта применения стековых и матричных штрих-кодов, а также установлен характер дальнейшего развития двухмерных бар-кодов.The article analyzes major types of the current 2D barcodes and defines the notions of "stack barcode" and "matrix barcode"; the comparative description of foreign experience in application of stack barcodes and matrix barcodes is carried out, and the patterns for further development of 2D barcodes are presented

Objects extraction and recognition for camera-based interaction : heuristic and statistical approaches

In this thesis, heuristic and probabilistic methods are applied to a number of problems for camera-based interactions. The goal is to provide solutions for a vision based system that is able to extract and analyze interested objects in camera images and to use that information for various interactions for mobile usage. New methods and new attempts of combination of existing methods are developed for different applications, including text extraction from complex scene images, bar code reading performed by camera phones, and face/facial feature detection and facial expression manipulation. The application-driven problems of camera-based interaction can not be modeled by a uniform and straightforward model that has very strong simplifications of reality. The solutions we learned to be efficient were to apply heuristic but easy of implementation approaches at first to reduce the complexity of the problems and search for possible means, then use developed statistical learning approaches to deal with the remaining difficult but well-defined problems and get much better accuracy. The process can be evolved in some or all of the stages, and the combination of the approaches is problem-dependent. Contribution of this thesis resides in two aspects: firstly, new features and approaches are proposed either as heuristics or statistical means for concrete applications; secondly engineering design combining seveal methods for system optimization is studied. Geometrical characteristics and the alignment of text, texture features of bar codes, and structures of faces can all be extracted as heuristics for object extraction and further recognition. The boosting algorithm is one of the proper choices to perform probabilistic learning and to achieve desired accuracy. New feature selection techniques are proposed for constructing the weak learner and applying the boosting output in concrete applications. Subspace methods such as manifold learning algorithms are introduced and tailored for facial expression analysis and synthesis. A modified generalized learning vector quantization method is proposed to deal with the blurring of bar code images. Efficient implementations that combine the approaches in a rational joint point are presented and the results are illustrated.reviewe