Ptu-024 - photometric stereo reconstruction for surface analysis of mucosal tissue

This paper provides a novel approach for real-time detection of polyps. Using a photometric stereo sensor for endoscopy imaging in a porcine model, the 3D surface geometry of a porcine gut is recovered. Shape features are extracted from the 3D surface data and analysed to detect and identify regions that are locally spherical, suggestive of polyps to aid polyp detection

Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification

The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles

Optical Imaging Technology In Colonoscopy - Is There A Role For Photometric Stereo

AbstractColonoscopy screening for the detection and removal of colonic adenomas is central to efforts to reduce the morbidity and mortality of colorectal cancer. However, up to a third of adenomas may be missed at colonoscopy, and the majority of post-colonoscopy colorectal cancers are thought to arise from these. Adenomas have three-dimensional surface topographic features that differentiate them from adjacent normal mucosa. However, these topographic features are not enhanced by white light colonoscopy, and the endoscopist must infer these from two-dimensional cues. This may contribute to the number of missed lesions. A variety of optical imaging technologies have been developed commercially to enhance surface topography. However, existing techniques enhance surface topography indirectly, and in two dimensions, and the evidence does not wholly support their use in routine clinical practice. In this narrative review, co-authored by gastroenterologists and engineers, we summarise the evidence for the impact of established optical imaging technologies on adenoma detection rate, and review the development of photometric stereo (PS) for colonoscopy. PS is a machine vision technique able to capture a dense array of surface normals to render three-dimensional reconstructions of surface topography. This imaging technique has several potential clinical applications in colonoscopy, including adenoma detection, polyp classification, and facilitating polypectomy, an inherently three-dimensional task. However, the development of PS for colonoscopy is at an early stage. We consider the progress that has been made with PS to date and identify the obstacles that need to be overcome prior to clinical application

Perceptual dimensions for surface texture retrieval

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Change in appearance of textures with randomisation of Fourier phase

It is known that humans can discriminate visual textures on the basis of differences in statistics higher than the second order. However, these results have been obtained using artificial textures made up of geometric elements, and the effects of manipulating higher order statistics in textures with a more natural appearance have not been examined quantitatively. We therefore investigate the effect of gradual phase randomisation on the ability of observers to discriminate synthetic textures with a naturalistic appearance. We keep the first and second order statistics of textures constant as phase is randomised, so that any perceived changes are due only to changes in third and higher order statistics. A difference scaling method is used to derive perceptual scales for each observer, and this shows a monotonic effect of the degree of randomisation on appearance. The greatest change is perceived between 20% and 60% randomisation, with little change in appearance above and below this range. We propose a biologically plausible model based on a local measurement derived using phase congruence information to account for the observed effects of phase randomisation on discrimination of texture pairs. We show that the same behaviour can be achieved in both perceptual and feature spaces, which can be related by a linear relationship within a log-log space

A novel photometric stereo imaging sensor for endoscopy imaging: Proof of concept studies on a porcine model

The American Society of Gastroenterology Endoscopy led Preservation and Incorporation of Valuable Endoscopy Innovations initiative has identified real time poly diagnosis as one of the next major technology driven changes in endoscopy (1). A number of imaging techniques are presently being investigated in this area. The complex and demanding nature of the imaging environment, including issues relating to operation in a confined space, the presence of surface fluids and the highly reflective nature of the mucosa areas, renders 3D surface capture and analysis for the purpose of diagnosis an extremely challenging task. A novel Photometric Stereo (PS) imaging sensor has never been previously assessed for mucosal imaging. PS imaging requires the capture of the mucosal regions while illuminated using light from differing known directions and offers the potential for the recovery of high resolution 3D shape and topographic texture data. The captured PS images are then used to recover and analyse the 3D surface geometry

Paper type classification employing a 3D DrCID

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Paper substrate classification based on 3D surface micro-geometry

This paper presents an approach to derive a novel 3D signature based on the micro-geometry of paper surfaces so as to uniquely characterise and classify different paper substrates. This procedure is extremely important to confront different conducts of tampering valuable documents. We use a 4-light source photometric stereo (PS) method to recover dense 3D geometry of paper surfaces captured using an ultra-high resolution sensing device. We derived a unique signature for each paper type based on the shape index (SI) map generated from the surface normals of the 3D data. We show that the proposed signature can robustly and accurately classify paper substrates with different physical properties and different surface textures. Additionally, we present results demonstrating that our classification model using the 3D signature performs significantly better as compared to the use of conventional 2D image based descriptors extracted from both printed and non-printed paper surfaces. Accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non-printed paper types and a measure of classification success of over 92%is achieved in both cases (92.5% for printed surfaces and 96% for the non-printed ones)

Paper type classification based on a new 3D surface texture measure

A novel three-dimensional (3D) surface texture measure (3DSTM) is presented based on the micro-geometry of paper surfaces to classify different paper substrates. This is useful to automatically determine whether a document is printed on the correct paper substrate to help identify fraud. We use a 4-light source photometric stereo (PS) method to recover the dense 3D geometry of paper surfaces captured using a high-resolution sensing device. We derive a unique 3DSTM for each paper type based on the shape index (SI) map generated from the surface normals of the 3D data. We show that the proposed 3DSTM can robustly and accurately classify paper substrates with different physical properties and different surface textures. The accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non-printed paper types and a measure of success over 92% is achieved

Paper substrate classification based on 3D surface micro-geometry

This paper presents an approach to derive a novel 3D signature based on the micro-geometry of paper surfaces so as to uniquely characterise and classify different paper substrates. This procedure is extremely important to confront different conducts of tampering valuable documents. We use a 4-light source photometric stereo (PS) method to recover dense 3D geometry of paper surfaces captured using an ultra-high resolution sensing device. We derived a unique signature for each paper type based on the shape index (SI) map generated from the surface normals of the 3D data. We show that the proposed signature can robustly and accurately classify paper substrates with different physical properties and different surface textures. Additionally, we present results demonstrating that our classification model using the 3D signature performs significantly better as compared to the use of conventional 2D image based descriptors extracted from both printed and non-printed paper surfaces. Accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non-printed paper types and a measure of classification success of over 92%is achieved in both cases (92.5% for printed surfaces and 96% for the non-printed ones).</p