Recognition of QR codes on cylindrical surface based on 3D perspective transformation

传统的Qr码识别算法只适用于打印在平面上的条码,提出了一种有效识别打印在饮料瓶等圆柱面上的Qr码。通过对图像轮廓进行角点检测确定回字定位图形,在此基础上筛选条码关键轮廓并对其进行霍夫变换提取圆柱面上的透视椭圆信息,同时结合透视椭圆的参数和三维透视变换,有效构建了圆柱面条码像素从二维图像平面直接映射到三维图像空间的变换矩阵,重构打印在平面或圆柱面上的Qr码目标。实验结果表明,该算法对平面或圆柱面Qr条码的识别有较高的准确率。Traditional QR code recognition algorithm is usually applied to the barcode printed on flat surface only.A lowcost approach to recognize the curved QR codes printed on bottles or cans is proposed in this paper.The width proportion and corners of image contours are extracted to confirm the positioning patterns and an efficient Hough transformation ellipse fitting method is employed to extract the elliptic information.In combination with the parameters of perspective ellipse and 3D perspective transformation,the transformation matrix of barcode pixels on a cylindrical surface is constructed by direct mapping from the 2D image plane to 3D image space.The experiment result proves the algorithm has the high-accuracy recognition ability of barcodes no matter on the flat or the cylindrical surface.福建省自然科学基金资助项目(2010H6026

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