Automatic target recognition(ATR) is the key of the image guidance technology, yet it is difficult to recognize the target by merely depending on the real-time image acquired by flying vehicle cameras, moreover, the t...
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ISBN:
(纸本)9780819469526
Automatic target recognition(ATR) is the key of the image guidance technology, yet it is difficult to recognize the target by merely depending on the real-time image acquired by flying vehicle cameras, moreover, the task of recognizing the target from the real-time images by the vehicle-carrying image processing system is a hard work itself The main trend of the ATR nowadays is to make utilization of the images produced by high-resolution remote sensing satellite to retrieve the front elevation of the interested region before hand. These front elevations are loaded upon the flying vehicles and are matched with the real-time images acquired by vehicle-carrying cameras to recognize the interested target. Obviously, the key step of this method is to recover the 3D information from 21) images. This paper proposed a framework to produce multi-scale and multi-viewpoint projection images based on remote sensing satellite stereopair by means of photogrammetry and computervision. First we proposed a algorithm for reconstructing the 3D structure of the target by digital photogrammetric techniques and establishing the 3D model of the target using the OpenGL visualization toolkit. Then the conversion relationship between the world coordinate system and the simulation space coordinate system is provided to produce the front elevation in the simulation space.
The development of the remote sensing technology makes us obtain very abundant information of nature, especially with the appearance of high resolution remote sensing image it extends the visual field of the nature. H...
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ISBN:
(纸本)9780819469526
The development of the remote sensing technology makes us obtain very abundant information of nature, especially with the appearance of high resolution remote sensing image it extends the visual field of the nature. High-resolution satellite images such as Quickbird and IKONOS have been applied into many fields. But the challenge that faces us is how to make use of the data effectively and obtain more useful information through some processing. Because in the target recognition, the mutual-complementarity among the different results obtained by the different classifier making using of the same features usually is very strong and high resolution remote sensing data have a lot of characteristics such as spectral, texture and context and so on compared to the other lower resolution remote sensing data, the Multiple Classifiers making use of multi-characteristic was proposed to improve the high resolution remote sensing image classification accuracy in this paper. The experiments show that the approach can obtain higher classification accuracy and better classification result than single classifier.
In this paper, we propose a human action recognition system suitable for embedded computervision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for e...
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ISBN:
(纸本)9781424411795
In this paper, we propose a human action recognition system suitable for embedded computervision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for embedded computervision application based on three reasons. Firstly, the system was based on a linear Support Vector Machine (SVM) classifier where classification progress can be implemented easily and quickly in embedded hardware. Secondly, we use compacted motion features easily obtained from videos. We address the limitations of the well known Motion History Image (MHI) and propose a new Hierarchical Motion History Histogram (HMHH) feature to represent the motion information. HMHH not only provides rich motion information, but also remains computationally inexpensive. Finally, we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers. Experimental results show that our system achieves significant improvement on the recognition performance.
This paper presents an object-based image retrieval using a method based on visual-pattern matching. A visual pattern is obtained by detecting the line edge from a square block using the moment-preserving edge detecto...
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This paper presents an object-based image retrieval using a method based on visual-pattern matching. A visual pattern is obtained by detecting the line edge from a square block using the moment-preserving edge detector. It is desirable and yet terrains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computervision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computervision methods that explore the structure of an image object by visual-pattern detection to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness. (c) 2006 patternrecognition Society. Published by Elsevier Ltd. All rights reserved.
A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representi...
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ISBN:
(纸本)9781424411795
A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well. The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computervision applications.
A major challenge in developing advanced thermal processess based on electromagnetic heating is to determine the location of cold spots in foods. A rapid and reliable method was developed in this study with the aim to...
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A major challenge in developing advanced thermal processess based on electromagnetic heating is to determine the location of cold spots in foods. A rapid and reliable method was developed in this study with the aim to effectively locate the cold spot in model food sterilized in microwave systems. The developed method involved application of chemical marker M-2 yield to a model food, mashed potatoes, using computervision system and an image processing software IMAQ vision Builder to capture and analyze color patterns after thermal processes. A systematic study was conducted to establish relationships among M-2 yields, color values from captured images of cut food samples, and thermal lethality (F-0). Several factors including consistency of imaging background and positions of lights over the diffuser box were considered to standardize the method. To facilitate the comparative study of heating characteristic for different combinations of power levels and F0, a mapping scale using unheated and saturated mashed potato samples was developed by fixing the lowest and upper most gray-scale values. Color values equivalent to gray-level values were positively correlated to F-0 and M-2 yield. The specified cold spot location determined by computervision method was validated in a 915 MHz single-mode microwave sterilization system. The results showed that the computervision method can potentially be used as an effective tool in microwave sterilization process development for regulatory acceptance and industrial applications. (c) 2007patternrecognition Society. Published by Elsevier Ltd. All rights reserved.
Confidence evaluation is an important technique in image matching process. This paper proposes a confidence level evaluation method for image matching result based on support vector machine (SVM). We divide the matchi...
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ISBN:
(纸本)9780819469526
Confidence evaluation is an important technique in image matching process. This paper proposes a confidence level evaluation method for image matching result based on support vector machine (SVM). We divide the matching result into two different types: the correct result and the wrong result. So we translate the match result's confidence evaluation problem into the matching result's classification. This paper firstly provides a method of how to prepare the character parameters which can accurately reflect the matching performance. And then the SVM based on Gaussian kernel is used as a classifier to classify the match result and discriminate the match result's type. The experiments show that this method is effective. Compared with the Dempster-Shafer (D-S) evidence reasoning fusion method it has much higher accuracy.
Recently, we developed NIR based face recognition for highly accurate face recognition under illumination variations [10]. In this paper, we present a part-based method for improving its robustness with respect to pos...
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ISBN:
(纸本)9781424411795
Recently, we developed NIR based face recognition for highly accurate face recognition under illumination variations [10]. In this paper, we present a part-based method for improving its robustness with respect to pose variations. An NIR face is decomposed into parts. A part classifier is built for each part, using the most discriminative LBP histogram features selected by AdaBoost learning. The outputs of part classifiers are fused to give the final score. Experiments show that the present method outperforms the whole face-based method [10] by 4.53%.
The inhibitory interaction has long been observed in the lateral eye of the Limulus and been integrated into mechanism of enhancing contrast. When applying to the enhancement of low-contrast image for segmenting inter...
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ISBN:
(纸本)9780819469526
The inhibitory interaction has long been observed in the lateral eye of the Limulus and been integrated into mechanism of enhancing contrast. When applying to the enhancement of low-contrast image for segmenting interested objects, the original lateral inhibition model will simultaneously amplify noises while enhancing edges contrast, This paper presents a new lateral inhibition model, which is called Stick-Guided Lateral Inhibition, for enhancement of low-contrast image so that week edges may exert a stronger force to catch the boundary of targets in the latter segmentation. First, the guided inhibition term is introduced as a general framework for improving the performance of lateral inhibition models in the presence of noises. Then, by using asymmetric sticks to guide the inhibiting process, we are able to accentuate the intensity gradients of image-edges and contours while suppressing the amplification of noises. Experiments on synthetic images and remote sensor images show that our model significantly enhances low-contrast images and improves the performance of latter segmentation.
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