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

A novel clustering algorithm based on mathematical morphology for wind power generation prediction

Wind power has the characteristic of daily similarity. Furthermore, days with wind power variation trends reflect similar meteorological phenomena. Therefore, wind power prediction accuracy can be improved and computational complexity during model simulation reduced by choosing the historical days whose numerical weather prediction information is similar to that of the predicted day as training samples. This paper proposes a new prediction model based on a novel dilation and erosion (DE) clustering algorithm for wind power generation. In the proposed model, the days with similar numerical weather prediction (NWP) information to the predicted day are selected via the proposed DE clustering algorithm, which is based on the basic operations in mathematical morphology. And the proposed DE clustering algorithm can cluster automatically without supervision. Case study conducted using data from Yilan wind farm in northeast China indicate that the performance of the new generalized regression neural network (GRNN) prediction model based on the proposed DE clustering algorithm (DE clustering-GRNN) is better than that of the DPK-medoids clustering-GRNN, the K-means clustering-GRNN, and the AM-GRNN in terms of day-ahead wind power prediction. Further, the proposed DE clustering-GRNN model is adaptive

A Time Efficient Approach for Decision-Making Style Recognition in Lane-Change Behavior

Fast recognizing driver's decision-making style of changing lanes plays a pivotal role in safety-oriented and personalized vehicle control system design. This paper presents a time-efficient recognition method by integrating k-means clustering (k-MC) with K-nearest neighbor (KNN), called kMC-KNN. The mathematical morphology is implemented to automatically label the decision-making data into three styles (moderate, vague, and aggressive), while the integration of kMC and KNN helps to improve the recognition speed and accuracy. Our developed mathematical morphology-based clustering algorithm is then validated by comparing to agglomerative hierarchical clustering. Experimental results demonstrate that the developed kMC-KNN method, in comparison to the traditional KNN, can shorten the recognition time by over 72.67% with recognition accuracy of 90%-98%. In addition, our developed kMC-KNN method also outperforms the support vector machine (SVM) in recognition accuracy and stability. The developed time-efficient recognition approach would have great application potential to the in-vehicle embedded solutions with restricted design specifications

Character-based Automated Human Perception Quality Assessment In Document Images

Large degradations in document images impede their readability and deteriorate the performance of automated document processing systems. Document image quality (IQ) metrics have been defined through optical character recognition (OCR) accuracy. Such metrics, however, do not always correlate with human perception of IQ. When enhancing document images with the goal of improving readability, e.g., in historical documents where OCR performance is low and/or where it is necessary to preserve the original context, it is important to understand human perception of quality. The goal of this paper is to design a system that enables the learning and estimation of human perception of document IQ. Such a metric can be used to compare existing document enhancement methods and guide automated document enhancement. Moreover, the proposed methodology is designed as a general framework that can be applied in a wide range of applications. © 2012 IEEE

The segmentation of nonsolid pulmonary nodules in CT images

Nonsolid nodules are a common radiographical finding in high resolution CT images of the lung. A main factor in determining a nodules malignancy status is the change in the nodule size over time. A method for automatically segmenting a nonsolid nodule from CT images is presented in this thesis. Precise image segmentation is a prerequisite for determining the volumetric growth rate from multiple image scans and the corresponding nodule malignancy status. There has been limited previous work on a segmentation technique for nonsolid nodules. The methods that have been proposed have lacked clinical validation with a radiologist ground truth and often include smaller datasets. The method in this thesis directly compares radiologist ground truth with our automated method and examines the consistency of growth measurement for further validation. The segmentation method consists of three stages; bilateral noise reduction, a probability based voxel classifier and geometric vessel removal. Parameter optimization and validation of the segmentation algorithm is facilitated with a dataset of 20 nonsolid nodule images in which a radiologist has established ground truth by outlining the boundary of the nodule in each image that it is visible. The optimal parameters were determined using the overlap metric and a training/testing methodology. The automated method achieved an average overlap of 0.43 with the radiologist ground truth. An experiment was conducted to determine whether the radiologist manual boundaries or the automated segmentations were more consistent at measuring the volumetric growth between three time scans of the same nodule. Results were determined for two different growth models (exponential and linear) on a dataset of 25 nonsolid nodules. The growth variation of the automated method was found to be 1.87 compared to the radiologist growth variation of 3.00. This suggests that, if the assumption of consistent nodule growth holds for nonsolid nodules, then the automated method provides a more precise growth rate estimate than the radiologist markings

非等方的拡散法による自然画像の領域分割に関する研究

室蘭工業大学 (Muroran Institute of Technology)博士（工学

Regulated morphological operations

In this paper regulated morphological operations are de"ned by extending the "tting interpretation of the ordinary morphological operations. The de"ned operations have a controllable strictness, and so they are less sensitive to noise and small intrusions or protrusions on the boundaries of shapes. The properties of the de"ned operations are described, and the relations between them and some other non-linear operations are discussed. Given an existing morphological algorithm, it is possible to try and improve the results obtained by it by using the regulated operations instead of the ordinary operations with strictness that may be optimized according to some optimization criteria. Several examples of the propose