We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. The generated detailed map finds its application in the precise localization and navigation...
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ISBN:
(纸本)9781538680940
We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. The generated detailed map finds its application in the precise localization and navigation of autonomous vehicles. It can also serve as a virtual test bed for various vision and planning algorithms as well as a background map in the computer games. In this paper, we focus on two important issues: (i) incrementally generating a map with coherent 3D surface, in real time and (ii) preserving the quality of color texture. To handle the above issues, firstly, we perform a pose-refinement procedure which leverages camera image information, Delaunay triangulation and existing scan matching techniques to produce high resolution 3D map from the sparse input LIDAR scan. This 3D map is then texturized and accumulated by using a novel technique of ray-filtering which handles occlusion and inconsistencies in pose-refinement. Further, inspired by human fovea, we introduce foveal-processing which significantly reduces the computation time and also assists ray-filtering to maintain consistency in color texture and coherency in 3D surface of the output map. Moreover, we also introduce texture error (TE) and mean texture mapping error (MTME), which provides quantitative measure of texturing and overall quality of the textured maps.
In this paper we propose a clustering technique for the recognition of pigmented skin lesions in dermatological images. It is known that computer vision-based diagnosis systems have been used aiming mostly at the earl...
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ISBN:
(纸本)9783642128417
In this paper we propose a clustering technique for the recognition of pigmented skin lesions in dermatological images. It is known that computer vision-based diagnosis systems have been used aiming mostly at the early detection of skin cancer and more specifically the recognition of malignant melanoma tumour. The feature extraction is performed utilising digital imageprocessing methods, i.e. segmentation, border detection, colour and texture processing. The proposed method belongs to a class of clustering algorithms which are very successful in dealing with high dimensional data, utilising information driven by the Principal Component Analysis. Experimental results show the high performance of the algorithm against other methods of the same class.
In this paper we describe a low complexity image orientation detection algorithm which can be implemented in real-time on embedded devices such as low-cost digital cameras, mobile phone cameras and video surveillance ...
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ISBN:
(纸本)9780819484086
In this paper we describe a low complexity image orientation detection algorithm which can be implemented in real-time on embedded devices such as low-cost digital cameras, mobile phone cameras and video surveillance cameras. Providing orientation information to tamper detection algorithm in surveillance cameras, color enhancement algorithm and various scene classifiers can help improve their performances. Various image orientation detection algorithms have been developed in the last few years for image management systems, as a post processing tool. But, these techniques use certain high-level features and object classification to detect the orientation, thus they are not suitable for implementation on a capturing device in real-time. Our algorithm uses low-level features such as texture, lines and source of illumination to detect orientation. We implemented the algorithm on a mobile phone camera device with a 180 MHz, ARM926 processor. The orientation detection takes 10 ms for each frame which makes it suitable to use in image capture as well as video mode. It can be used efficiently in parallel with the other processes in the imaging pipeline of the device. On hardware, the algorithm achieved an accuracy of 92% with a rejection rate of 4% and a false detection rate of 8% on outdoor images.
Brain tumor segmentation is an important task in medical imageprocessing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. ...
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Brain tumor segmentation is an important task in medical imageprocessing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed. (C) 2016 The Authors. Published by Elsevier B.V.
We propose a framework for Threat image Projection (TIP) in cargo transmission X-ray imagery. The method exploits the approximately multiplicative nature of X-ray imagery to extract a library of threat items. These it...
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ISBN:
(纸本)9781509010721
We propose a framework for Threat image Projection (TIP) in cargo transmission X-ray imagery. The method exploits the approximately multiplicative nature of X-ray imagery to extract a library of threat items. These items can then be projected into real cargo. We show using experimental data that there is no significant qualitative or quantitative difference between real threat images and TIP images. We also describe methods for adding realistic variation to TIP images in order to robustify Machine Learning (ML) based algorithms trained on TIP. These variations are derived from cargo X-ray image formation, and include: (i) translations;(ii) magnification;(iii) rotations;(iv) noise;(v) illumination;(vi) volume and density;and (vii) obscuration. These methods are particularly relevant for representation learning, since it allows the system to learn features that are invariant to these variations. The framework also allows efficient addition of new or emerging threats to a detection system, which is important if time is critical. We have applied the framework to training ML-based cargo algorithms for (i) detection of loads (empty verification), (ii) detection of concealed cars (ii) detection of Small Metallic Threats (SMTs). TIP also enables algorithm testing under controlled conditions, allowing one to gain a deeper understanding of performance. Whilst we have focused on robustifying ML-based threat detectors, our TIP method can also be used to train and robustify human threat detectors as is done in cabin baggage screening.
Two algorithms for medical imageprocessing are discussed: CAVITY DETECTOR, which solves the segmentation problem of regions which are not completely surrounded by walls and EDGMENTATION, which is used for (i) preproc...
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The purpose of this work is to assess the image quality of dental digital systems with computed radiography (phosphor plate) and direct radiography (charge-coupled-device sensor), by developing a specific dental phant...
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The purpose of this work is to assess the image quality of dental digital systems with computed radiography (phosphor plate) and direct radiography (charge-coupled-device sensor), by developing a specific dental phantom named RADEN and software to analyze the phantom image automatically. The phantom developed to evaluate the image quality of dental radiographic equipment has specific test objects of contrast-detail combinations appropriated for the resolution of the dental digital systems. The image quality is evaluated by using contrast detail curves and an image quality index. The phantom was made of a square aluminum block 7.5 x 7.5 cm that has a lodge of size 3.2 x3.2 cm for fixing the sensor of the digital dental system, and it contains specific test objects of contrast-detail combinations that are cylindrical holes. The diameters of the holes ranged from 0.3 to 1.6 min, and the depths ranged from 0.14 to 1.28 mm;these ranges for the diameters and depths are suitable for resolution of the dental digital systems and the contrast attenuation curves of the X-radiation, and the results are sensitive to the operating conditions of the dental radiographic system. We have also developed specific software to analyze the RADEN phantom image automatically obtained by the digital radiographic equipment. The algorithms are based on digital imageprocessing techniques, and they have been implemented in a user-friendly tool with a graphical interface.
The proceedings contains 229 papers from the conference on Proceedings of the 2003 IEEE International Symposium On Circuits and systems: Volume IV. The topics discussed include: analog representation and digital imple...
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The proceedings contains 229 papers from the conference on Proceedings of the 2003 IEEE International Symposium On Circuits and systems: Volume IV. The topics discussed include: analog representation and digital implementation of OFDM systems;fast algorithms for computing full and reduced rank Wiener filters;efficient signal processing in embedded Java systems;an efficient split-radix FFT algorithm;a new class of even length wavelet filters;fuzzy filters for noisy image filtering;and design and implementation of a reconfigurable FIR filter.
Classification of images is a significant step in pattern recognition and digital imageprocessing. It is applied in various domains for authentication, identification, defense, medical diagnosis and so on. Feature ex...
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Light usually discarded in a microscope can be collected in additional channels and used to reduce noise sensitivity. Optimal Fourier-domain processing is used to construct a single superior image from the multi-chann...
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