This paper presents our initial research on calibrating developed Multi-vision Oblique Photogrammetry System (MOPS). First, 2D point control from so-called Radial Alignment Constrain (RAC) by Tsai's two-stage cali...
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ISBN:
(纸本)9780819485809
This paper presents our initial research on calibrating developed Multi-vision Oblique Photogrammetry System (MOPS). First, 2D point control from so-called Radial Alignment Constrain (RAC) by Tsai's two-stage calibration is outlined and its potential issues to camera calibration accuracy are discussed as well. Second, we make well study on image radial distortion when principle point parameters are ignored and with perspective invariant of space line, one new radial distortion correctness algorithm is proposed based on 2D line control. Third, procedure of Tsai's two-stage calibration is modified to corporate with proposed radial distortion correctness algorithm and new steps to calibrate digital cameras are summarized in detail. Finally, calibration test was implemented on selected digital camera Nikon P5100 by means of 2D chessboard as reference object. The comparison of proposed approach with Tsai's two-stage calibration is also given in this paper and valuable conclusions are conducted as well.
Checker pattern detection in the presence of miscellaneous background objects is a challenging task in computervision systems. In practical applications, the direct detection of corner points fails due to the illumin...
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ISBN:
(纸本)9781601321916
Checker pattern detection in the presence of miscellaneous background objects is a challenging task in computervision systems. In practical applications, the direct detection of corner points fails due to the illumination variation in the lighting system. A computationally efficient checker pattern detection method using morphological operations and contour tree validation is proposed. The proposed method is tolerant to illumination, rotation and scaling variation. Comparative analysis with corner detection demonstrates the viability of the proposed algorithm as a valuable tool for automatic camera calibration and 3D measurements.
The patterns in collections of real world objects are often not based on a limited set of isolated properties such as features. Instead, the totality of their appearance constitutes the basis of the human recognition ...
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ISBN:
(纸本)9783642250842
The patterns in collections of real world objects are often not based on a limited set of isolated properties such as features. Instead, the totality of their appearance constitutes the basis of the human recognition of patterns. Structural patternrecognition aims to find explicit procedures that mimic the learning and classification made by human experts in well-defined and restricted areas of application. This is often done by defining dissimilarity measures between objects and measuring them between training examples and new objects to be recognized. The dissimilarity representation offers the possibility to apply the tools developed in machine learning and statistical patternrecognition to learn from structural object representations such as graphs and strings. These procedures are also applicable to the recognition of histograms, spectra, images and time sequences taking into account. the connectivity of samples (bins, wavelengths, pixels or time samples). The topic of dissimilarity representation is related to the field of non-Mercer kernels in machine learning but it covers a wider set of classifiers and applications. Recently much progress has been made in this area and many interesting applications have been studied in medical diagnosis, seismic and hyperspectral imaging, chemometrics and computervision. This review paper offers an introduction to this field and presents a number of real world applications(1).
In this paper, machine learning and geometric computervision are combined for the purpose of automatic reading bus line numbers with a. smart phone. This can prove very useful to improve the autonomy of visually impa...
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ISBN:
(纸本)9783642240874;9783642240881
In this paper, machine learning and geometric computervision are combined for the purpose of automatic reading bus line numbers with a. smart phone. This can prove very useful to improve the autonomy of visually impaired people in urban scenarios. The problem is a challenging one, since standard geometric image matching methods fail clue to the abundance of distractors, occlusions, illumination changes, highlights arid specularities, shadows, and perspective distortions. The problem is solved by locating the main geometric entities of the bus facade through a cascade of classifiers, and then refining the matching with robust geometric matching. The method works in real time and, as experimental results show, has a good performance in terms of recognition rate and reliability.
Photobooks are comfortable and attractive solution for personal photo printing and storing. Photobook generation requires a lot of manual operations and takes a lot of time. Automation process will involve new users a...
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Wii Remote is the standard controller of the game console Nintendo Wii®. Despite its low cost, it has a very high performance and high resolution infrared camera. It also has a built-in chip for tracking up to 4 ...
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Content-based image retrieval(CBIR) is an application of computervision techniques to the image retrieval *** is,the problem of searching for digital images in large *** this paper,we apply an image segmentation tech...
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Content-based image retrieval(CBIR) is an application of computervision techniques to the image retrieval *** is,the problem of searching for digital images in large *** this paper,we apply an image segmentation technique to an image retrieval system which is designed for the use on mobile *** an image captured by the mobile devices,edge detection and region merging mechanisms are used in this segmentation technique to extract the ROI from a complex background *** proposed method automatically merges the regions that are initially segmented by mean shift segmentation,and then effectively extracts the object contour by the labeled regions as either background or *** no users interaction,the experimental results show the method is more effective than other automatic segmentation methods.
Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such ...
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ISBN:
(纸本)9781601321916
Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such as patternrecognition, object tracking, and shape recognition. So far, there have been so many methods offered to solve this problem. Near all these methods have tried to extract and define features to separate different labels of textures really well. This article has offered an approach which has an overall process on the images of textures based on Local binary pattern and Gray Level Co-occurrence matrix and then by edge detection, and finally, extracting the statistical features from the images would classify them. Although, this approach is a general one and is could be used in different applications, the method has been tested on the stone texture and the results have been compared with some of the previous approaches to prove the quality of proposed approach.
This paper presents inexpensive computervision techniques allowing to measure the texture characteristics of woven fabric, such as weave repeat and yarn counts, and the surface roughness. First, we discuss the automa...
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This paper presents inexpensive computervision techniques allowing to measure the texture characteristics of woven fabric, such as weave repeat and yarn counts, and the surface roughness. First, we discuss the automatic recognition of weave pattern and the accurate measurement of yarn counts by analyzing fabric sample images. We propose a surface roughness indicator FDFFT, which is the 3-D surface fractal dimension measurement calculated from the 2-D fast Fourier transform of high-resolution 3-D surface scan. The proposed weave patternrecognition method was validated by using computer-simulated woven samples and real woven fabric images. All weave patterns of the tested fabric samples were successfully recognized, and computed yarn counts were consistent with the manual counts. The rotation invariance and scale invariance of FDFFT were validated with fractal Brownian images. Moreover, to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from the 3-D fabric surface. According to the test results, we demonstrated that FDFFT is a fast and reliable parameter for fabric roughness measurement based on 3-D surface data.
The rise of cellphones and other mobile devices as an interface platform for end users has spurred the demand for applications with richer feature sets with each new generation. This demand for functionality has drive...
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ISBN:
(纸本)9781601321916
The rise of cellphones and other mobile devices as an interface platform for end users has spurred the demand for applications with richer feature sets with each new generation. This demand for functionality has driven an increase in the power and complexity of the underlying hardware. The combination of ever-advancing hardware and rich software environments provides possibilities for functionality that have not existed in the past. This project explores a framework for providing true on-device computervision and image analysis functionality as a means of further enhancing the functionality of evolving mobile devices. The paper describes the development and evaluation of a prototype computervision application for the Android platform that provides native image generation and analysis functionality.
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