Unsupervised construction of fuzzy measures through self-organizing feature maps and its application in color image segmentation

AbstractThe paper presents a framework for the segmentation of multi-dimensional images, e.g., color, satellite, multi-sensory images, based on the employment of the fuzzy integral, which undertakes the classification of the input features. The framework makes use of a self-organizing feature map, whereby the coefficients of the fuzzy measure are determined. This process is unsupervised and therefore constitutes one of the main contributions of the paper.The performance of the framework is shown by successfully realizing the segmentation of color images in two different applications. First, the features of the framework and its parameterization are analyzed by segmenting different images used as benchmark in image processing. Finally, the framework is applied in the segmentation of different images taken under difficult illumination conditions. The images serve the development of an automated cashier system, where the weak segmentation constitutes the first step for the identification of different market items. The presented framework succeeds in the segmentation of all these color images

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Fuzzy machine vision based inspection

Machine vision system has been fostered to solve many realistic problems in various fields. Its role in achieving superior quality and productivity is of paramount importance. But, for such system to be attractive, it needs to be fast, accurate and cost-effective. This dissertation is based on a number of practical machine vision based inspection projects obtained from the automotive industry. It presents a collection of developed efficient fuzzy machine vision approaches endorsed with experimental results. It also covers the conceptual design, development and testing of various fuzzy machine vision based inspection approaches for different industrial applications. To assist in developing and evaluating the performance of the proposed approaches, several parts are tested under varying lighting conditions. This research deals with two important aspects of machine vision based inspection. In the first part, it concentrates on the topics of component detection and component orientation identification. The components used in this part are metal clips mounted on a dash panel frame that is installed in the door of trucks. Therefore, we propose a fuzzy machine vision based clip detection model and a fuzzy machine vision based clip orientation identification model to inspect the proper placement of clips on dash panels. Both models are efficient and fast in terms of accuracy and processing time. In the second part of the research, we are dealing with machined part defects such as broken edge, porosity and tool marks. The se defects occur on the surface of die cast aluminum automotive pump housings. As a result, an automated fuzzy machine vision based broken edge detection method, an efficient fuzzy machine vision based porosity detection technique and a neuro-fuzzy part classification model based on tool marks are developed. Computational results show that the proposed approaches are effective in yielding satisfactory results to the tested image databases. There are four main contributions to this work. The first contribution is the development of the concept of composite matrices in conjunction with XOR feature extractor using fuzzy subtractive clustering for clip detection. The second contribution is about a proposed model based on grouping and counting pixels in pre-selective areas which tracks pixel colors in separated RGB channels to determine whether the orientation of the clip is acceptable or not. The construction of three novel edge based features embedded in fuzzy C-means clustering for broken edge detection marks the third contribution. At last, the fourth contribution presents the core of porosity candidates concept and its correlation with twelve developed matrices. This, in turn, results in the development of five different features used in our fuzzy machine vision based porosity detection approach

The analysis and discrimination of pyrolysis products from biological and non-biological sources

This work involves the limited use of human tissue samples. These samples were obtained through body donation and under full ethical approval from the University of Strathclyde ethics committee. Products generated through pyrolysis of common materials can act as background compounds, interfering with the analysis and identification of potential human remains. The development of a robust methodology for the generation and analysis of volatile products from biological (porcine and human tissues) and non-biological (textile materials) sources stands at the core of this study, combined with examining various factors that causes these profiles to deviate. This process began with the validation of porcine samples as a substitute of human samples through the identification of similar key indicators, characteristic to both tissues. Interestingly, different temperature ranges (pre- and post-ignition) and type of porcine tissues utilised were found to effect the type of key indicators detected; and as such, has convincingly resolved key indicators reported in previous research literature. In addition, key indicators of pure and blended textiles were also established and the effects of blended fibres towards the overall thermal properties of the textile, highlighted. Alterations to the key indicators of individual porcine and textile samples were examined, subjectively and objectively, when both samples were burnt together (combined samples). Subjective analysis involved the scrutiny of the chromatographic output, revealing the dominance of key indicators of porcine samples over textiles for majority of the combined samples. EIC and EIP proved to be a beneficial tool in extracting key indicators of porcine samples in the presence of contamination (textiles). At 70% presence, SOFM provided an objective and successful classification and discrimination of pyrolytic data according to the type of pyrolysis product detected across textiles, porcine bones and also in the combined textile-bone samples while underlining meaningful associations amongst similar groups. Overall, although this work suggests that pyrolytic data can be unpredictable, such as its dependence on various factors, with suitable analytical and statistical techniques, it has revealed pertinent information on the key indicators of porcine, human and textiles samples and the inter- and intra-molecular changes that occur to them during pyrolysis.This work involves the limited use of human tissue samples. These samples were obtained through body donation and under full ethical approval from the University of Strathclyde ethics committee. Products generated through pyrolysis of common materials can act as background compounds, interfering with the analysis and identification of potential human remains. The development of a robust methodology for the generation and analysis of volatile products from biological (porcine and human tissues) and non-biological (textile materials) sources stands at the core of this study, combined with examining various factors that causes these profiles to deviate. This process began with the validation of porcine samples as a substitute of human samples through the identification of similar key indicators, characteristic to both tissues. Interestingly, different temperature ranges (pre- and post-ignition) and type of porcine tissues utilised were found to effect the type of key indicators detected; and as such, has convincingly resolved key indicators reported in previous research literature. In addition, key indicators of pure and blended textiles were also established and the effects of blended fibres towards the overall thermal properties of the textile, highlighted. Alterations to the key indicators of individual porcine and textile samples were examined, subjectively and objectively, when both samples were burnt together (combined samples). Subjective analysis involved the scrutiny of the chromatographic output, revealing the dominance of key indicators of porcine samples over textiles for majority of the combined samples. EIC and EIP proved to be a beneficial tool in extracting key indicators of porcine samples in the presence of contamination (textiles). At 70% presence, SOFM provided an objective and successful classification and discrimination of pyrolytic data according to the type of pyrolysis product detected across textiles, porcine bones and also in the combined textile-bone samples while underlining meaningful associations amongst similar groups. Overall, although this work suggests that pyrolytic data can be unpredictable, such as its dependence on various factors, with suitable analytical and statistical techniques, it has revealed pertinent information on the key indicators of porcine, human and textiles samples and the inter- and intra-molecular changes that occur to them during pyrolysis