A New 2D Corner Detector for Extracting Landmarks from Brain MR Images

Point-based registration of images strongly depends on the extraction of suitable landmarks. Recently, various 2D operators have been proposed for the detection of corner points but most of them are not effective for medical images that need a high accuracy. In this paper we have proposed a new automatic corner detector based on the covariance between the small region of support around a central pixel and its rotated one. The main goal of this paper is medical images so we especially focus on extracting brain MR image’s control points which play an important role in accuracy of registration. This approach has been improved by refined localization through a differential edge intersection approach proposed by Karl Rohr. This method is robust to rotation, transition and scaling and in comparison with other grayscale methods has better results particularly for the brain MR images and also has acceptable robustness to distortion which is a common incident in brain surgeries. In the first part of this paper we describe the algorithm and in the second part we investigate the results of this algorithm on different MR images and its ability to detect corresponding points under elastic deformation and noise. It turns out that this method: 1)detect larger number of corresponding points that the other operators, 2)its performance on the basis of the statistical measures is better, and 3)by choosing a suitable region of support, it can significantly decrease the number of false detection

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

3D Neuron Tip Detection in Volumetric Microscopy Images

Abstract-This paper addresses the problem of 3D neuron tips detection in volumetric microscopy image stacks. We focus particularly on neuron tracing applications, where the detected 3D tips could be used as the seeding points. Most of the existing neuron tracing methods require a good choice of seeding points. In this paper, we propose an automated neuron tips detection method for volumetric microscopy image stacks. Our method is based on first detecting 2D tips using curvature information and a ray-shooting intensity distribution model, and then extending it to the 3D stack by rejecting false positives. We tested this method based on the V3D platform, which can reconstruct a neuron based on automated searching of the optimal 'paths' connecting those detected 3D tips. The experiments demonstrate the effectiveness of the proposed method in building a fully automatic neuron tracing system

Improved clustering approach for junction detection of multiple edges with modified freeman chain code

Image processing framework of two-dimensional line drawing involves three phases that are detecting junction and corner that exist in the drawing, representing the lines, and extracting features to be used in recognizing the line drawing based on the representation scheme used. As an alternative to the existing frameworks, this thesis proposed a framework that consists of improvement in the clustering approach for junction detection of multiple edges, modified Freeman chain code scheme and provide new features and its extraction, and recognition algorithm. This thesis concerns with problem in clustering line drawing for junction detection of multiple edges in the first phase. Major problems in cluster analysis such as time taken and particularly number of accurate clusters contained in the line drawing when performing junction detection are crucial to be addressed. Two clustering approaches are used to compare with the result obtained from the proposed algorithm: self-organising map (SOM) and affinity propagation (AP). These approaches are chosen based on their similarity as unsupervised learning class and do not require initial cluster count to execute. In the second phase, a new chain code scheme is proposed to be used in representing the direction of lines and it consists of series of directional codes and corner labels found in the drawing. In the third phase, namely feature extraction algorithm, three features proposed are length of lines, angle of corners, and number of branches at each corner. These features are then used in the proposed recognition algorithm to match the line drawing, involving only mean and variance in the calculation. Comparison with SOM and AP clustering approaches resulting in up to 31% reduction for cluster count and 57 times faster. The results on corner detection algorithm shows that it is capable to detect junction and corner of the given thinned binary image by producing a new thinned binary image containing markers at their locations

Image morphological processing

Mathematical Morphology with applications in image processing and analysis has been becoming increasingly important in today\u27s technology. Mathematical Morphological operations, which are based on set theory, can extract object features by suitably shaped structuring elements. Mathematical Morphological filters are combinations of morphological operations that transform an image into a quantitative description of its geometrical structure based on structuring elements. Important applications of morphological operations are shape description, shape recognition, nonlinear filtering, industrial parts inspection, and medical image processing. In this dissertation, basic morphological operations, properties and fuzzy morphology are reviewed. Existing techniques for solving corner and edge detection are presented. A new approach to solve corner detection using regulated mathematical morphology is presented and is shown that it is more efficient in binary images than the existing mathematical morphology based asymmetric closing for corner detection. A new class of morphological operations called sweep mathematical morphological operations is developed. The theoretical framework for representation, computation and analysis of sweep morphology is presented. The basic sweep morphological operations, sweep dilation and sweep erosion, are defined and their properties are studied. It is shown that considering only the boundaries and performing operations on the boundaries can substantially reduce the computation. Various applications of this new class of morphological operations are discussed, including the blending of swept surfaces with deformations, image enhancement, edge linking and shortest path planning for rotating objects. Sweep mathematical morphology is an efficient tool for geometric modeling and representation. The sweep dilation/erosion provides a natural representation of sweep motion in the manufacturing processes. A set of grammatical rules that govern the generation of objects belonging to the same group are defined. Earley\u27s parser serves in the screening process to determine whether a pattern is a part of the language. Finally, summary and future research of this dissertation are provided

Trademark image retrieval by local features

The challenge of abstract trademark image retrieval as a test of machine vision algorithms has attracted considerable research interest in the past decade. Current operational trademark retrieval systems involve manual annotation of the images (the current ‘gold standard’). Accordingly, current systems require a substantial amount of time and labour to access, and are therefore expensive to operate. This thesis focuses on the development of algorithms that mimic aspects of human visual perception in order to retrieve similar abstract trademark images automatically. A significant category of trademark images are typically highly stylised, comprising a collection of distinctive graphical elements that often include geometric shapes. Therefore, in order to compare the similarity of such images the principal aim of this research has been to develop a method for solving the partial matching and shape perception problem. There are few useful techniques for partial shape matching in the context of trademark retrieval, because those existing techniques tend not to support multicomponent retrieval. When this work was initiated most trademark image retrieval systems represented images by means of global features, which are not suited to solving the partial matching problem. Instead, the author has investigated the use of local image features as a means to finding similarities between trademark images that only partially match in terms of their subcomponents. During the course of this work, it has been established that the Harris and Chabat detectors could potentially perform sufficiently well to serve as the basis for local feature extraction in trademark image retrieval. Early findings in this investigation indicated that the well established SIFT (Scale Invariant Feature Transform) local features, based on the Harris detector, could potentially serve as an adequate underlying local representation for matching trademark images. There are few researchers who have used mechanisms based on human perception for trademark image retrieval, implying that the shape representations utilised in the past to solve this problem do not necessarily reflect the shapes contained in these image, as characterised by human perception. In response, a ii practical approach to trademark image retrieval by perceptual grouping has been developed based on defining meta-features that are calculated from the spatial configurations of SIFT local image features. This new technique measures certain visual properties of the appearance of images containing multiple graphical elements and supports perceptual grouping by exploiting the non-accidental properties of their configuration. Our validation experiments indicated that we were indeed able to capture and quantify the differences in the global arrangement of sub-components evident when comparing stylised images in terms of their visual appearance properties. Such visual appearance properties, measured using 17 of the proposed metafeatures, include relative sub-component proximity, similarity, rotation and symmetry. Similar work on meta-features, based on the above Gestalt proximity, similarity, and simplicity groupings of local features, had not been reported in the current computer vision literature at the time of undertaking this work. We decided to adopted relevance feedback to allow the visual appearance properties of relevant and non-relevant images returned in response to a query to be determined by example. Since limited training data is available when constructing a relevance classifier by means of user supplied relevance feedback, the intrinsically non-parametric machine learning algorithm ID3 (Iterative Dichotomiser 3) was selected to construct decision trees by means of dynamic rule induction. We believe that the above approach to capturing high-level visual concepts, encoded by means of meta-features specified by example through relevance feedback and decision tree classification, to support flexible trademark image retrieval and to be wholly novel. The retrieval performance the above system was compared with two other state-of-the-art image trademark retrieval systems: Artisan developed by Eakins (Eakins et al., 1998) and a system developed by Jiang (Jiang et al., 2006). Using relevance feedback, our system achieves higher average normalised precision than either of the systems developed by Eakins’ or Jiang. However, while our trademark image query and database set is based on an image dataset used by Eakins, we employed different numbers of images. It was not possible to access to the same query set and image database used in the evaluation of Jiang’s trademark iii image retrieval system evaluation. Despite these differences in evaluation methodology, our approach would appear to have the potential to improve retrieval effectiveness

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study