Image Processing Techniques to Separate Linear and Curvilinear Features in Textures

Varied image processing techniques have been developed to extract or detect linear features from images. However, these techniques are targeted at extracting or detecting linear features, and it has been shown in an existing technique that the Fourier transform can be used in conjunction with the polar transformation to essentially lift or separate linear features from the background image. Extracting or detecting linear features in images involves locating these features in the image while separating or lifting them involves separating them from the background image such that we get two images: one image containing the linear features and the other image containing the background. This thesis presents approaches to separate linear and curvilinear features from textured backgrounds. The problem of separating linear features from a textured background is of importance in applications such as lithography, layout design and pattern recognition. The existing Fourier transform based approach of linear feature separation effectively separates randomly located lines that are spread throughout the entire image and is found to be ineffective when the linear features are of varied lengths and thickness. This thesis presents an approach to overcome this limitation of the Fourier transform based approach. This thesis presents two new window based techniques relying on the Fourier transform and the wavelet transform to lift randomly located lines of varied in lengths and thickness. The proposed techniques are built upon the existing Fourier transform approach. The performances of the proposed techniques are compared to the Fourier Transform approach through application to several images. It is observed that the proposed Fourier based block approach and wavelet based block approach consistently perform better than the existing approach. It is also observed that the proposed techniques effectively lift curvilinear features from textures too. The mathematical analysis and experimental results verifying this claim are presented

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability

Research on a modifeied RANSAC and its applications to ellipse detection from a static image and motion detection from active stereo video sequences

制度:新 ; 報告番号:甲3091号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2010/2/24 ; 早大学位記番号:新535

Automated visual inspection for the quality control of pad printing

Pad printing is used to decorate consumer goods largely because of its unique ability to apply graphics to doubly curved surfaces. The Intelpadrint project was conceived to develop a better understanding of the process and new printing pads, inks and printers. The thesis deals primarily with the research of a printer control system including machine vision. At present printing is manually controlled. Operator knowledge was gathered for use by an expert system to control the process. A novel local corner- matching algorithm was conceived to effect image segmentation, and neuro-fuzzy techniques were used to recognise patterns in printing errors. Non-linear Finite Element Analysis of the rubber printing-pad led to a method for pre-distorting artwork so that it would print undistorted on a curved product. A flexible, more automated printer was developed that achieves a higher printing rate. Ultraviolet-cured inks with improved printability were developed. The image normalisation/ error-signalling stage in inspection was proven in isolation, as was the pattern recognition system