Object-based approaches to image analysis have achieved considerable prominence in the last decade and are now widely considered superior to pixel-based approaches, particularly when extracting features from high-reso...
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ISBN:
(纸本)9781618390288
Object-based approaches to image analysis have achieved considerable prominence in the last decade and are now widely considered superior to pixel-based approaches, particularly when extracting features from high-resolution remotely sensed data. The oft-cited advantage of the object-based approach is the ability to simultaneously incorporate spectral, geometric, textural, and contextual information into the classification process. However, context has been ignored in many applications of object-based techniques, despite its importance to human cognition and the current technical capacity to accommodate it. We attribute this oversight to reliance on linear approaches to image analysis and argue that iterative approaches, while more complex, can produce more stable classifications and lead to improved accuracy. We provide examples from four recent land-cover mapping projects that show how context - the relative position of individual objects to neighbor objects - was used to improve feature discrimination in heterogeneous landscapes. We also show how this key factor in patternrecognition was combined with data fusion techniques to maximize object discrimination and to exploit existing investments in remote-sensing data (e.g., imagery, LiDAR, and vector GIS datasets). Although inclusion of contextual information in object-based image analysis presents both analytical and processing challenges, we found that the benefits of improved accuracy and landscape representation far outweigh potential costs.
In the process of dealing with traffic accidents, the contour of the car body parts is very important to the information of the vehicle body. By using imageprocessing techniques and methods, this paper proposed a new...
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In the process of dealing with traffic accidents, the contour of the car body parts is very important to the information of the vehicle body. By using imageprocessing techniques and methods, this paper proposed a new type recognition system of rear-view mirror outline. We also designed the way of active and passive vision measurement in the image acquisition phase; discussed the way of using the Harsdorff distance of the fragments and the standard parts' profile curve for image matching; and designed a database to achieve the query and comparison of fragments and standard parts.
There is advancement in every day for image classification starting from object classification to remotesensingimage. Plant classification from their part is one of the most current research works going in the area ...
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There is advancement in every day for image classification starting from object classification to remotesensingimage. Plant classification from their part is one of the most current research works going in the area of imageprocessing. The proposed work is a new approach for bamboo species classification from their Culm sheath by using Central moment. Automated recognition of bamboo has not yet been well established mainly due to lack of research in this area, non-availability and difficulty in obtaining the database. Therefore need of recognition of bamboo species is required by the user. The proposed work is an automated classification of bamboo species system based on shape features of bamboo Culm sheath by using the central moment classifier. Four different bamboo species are taking for experiment in the proposed work. The results obtained shows considerable recognition accuracy proving that the techniques used is suitable to be implemented for commercial purposes.
In the process of dealing with traffic accidents, the contour of the car body parts is very important to the information of the vehicle body. By using imageprocessing techniques and methods, this paper proposed a new...
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The multi-source information holds a great importance in processing complex and imprecise data. Unfortunately, it requires an adequate formalism capable to modelize and to fuse several information. The evidence theory...
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The multi-source information holds a great importance in processing complex and imprecise data. Unfortunately, it requires an adequate formalism capable to modelize and to fuse several information. The evidence theory distinguishes from all formalism by its capacity to modelize and treat imprecise and imperfect data. In this context, the high resolution images represent a huge amount of data and needs multi-source information to perform patternrecognition. In this paper, we present an adaption of the distance operator introduced by Denoeux for estimating belief functions. This proposed approach will be used to classify forest imageremotesensing by identifying the tree crown classes.
Edge of image is one of the most fundamental and significant features. Edge detection is always one of the classical studying projects of computer vision and imageprocessing field. It is the first step of image analy...
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Edge of image is one of the most fundamental and significant features. Edge detection is always one of the classical studying projects of computer vision and imageprocessing field. It is the first step of image analysis and understanding. With the continuous improvement of remotesensingimage, especially the appearance of Digital Aerial image, edge detection is necessary step to extract information from the Digital Aerial images..The purpose of edge detection is to discover the information about the shapes and the reflectance or transmittance in an image. The correctness and reliability of its results affect directly the comprehension machine system made for objective world. In this paper FPGA-based architecture for edge detection algorithms has been proposed. The implementation of edge detection algorithms on a field programmable gate array (FPGA) is having advantage of using large memory and embedded multipliers. FPGAs are providing a platform for processing real time algorithms on application-specific hardware with substantially higher performance than programmable digital signal processors (DSPs). The proposed architecture can be used as a building block of a aerial imaging systems for navigation and for the patternrecognition. The hardware implementation results are presented for the Sobel and Prewitt operator.
Hough transform has been applied abroad in object shape detection. However, the traditional generalized Hough transform may not make the vote focus to one point when the image has a high-level noise. As a result, the ...
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Hough transform has been applied abroad in object shape detection. However, the traditional generalized Hough transform may not make the vote focus to one point when the image has a high-level noise. As a result, the object positioning is not very precise, or even wrong. It makes the Hough Transform can't be used in strong noisy image or complex object background on this condition. In this paper, we apply fuzzy set theory to generalized Hough transform and use a new method to process strong noisy image. The method regards the unfocused area not just as some simple point but a "fuzzy voting point" - a fuzzy area. Consequently, the fuzzy set theory can be used to describe the "fuzzy voting point". By constructing a new subjection function, we can calculate a cut set and use it as weight to optimize the position of the reference points. The experiments show that this method can get more accurate and robust object position than traditional method in shape detection from high-level noise image.
We present a parallel image classification approach, referred to as the parallel positive Boolean function (PPBF), to multisource remotesensingimages. PPBF is originally from the positive Boolean function (PBF) clas...
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remote human identification using iris biometrics requires the development of automated algorithm of the robust segmentation of iris region pixels from visible face images. This paper presents a new automated iris seg...
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remote human identification using iris biometrics requires the development of automated algorithm of the robust segmentation of iris region pixels from visible face images. This paper presents a new automated iris segmentation framework for iris images acquired at-a-distance using visible imaging. The proposed approach achieves the segmentation of iris region pixels in two stages, i.e. (i) iris and sclera classification, and (ii) post-classification processing. Unlike the traditional edge-based segmentation approaches, the proposed approach simultaneously exploits the discriminative color features and localized Zernike moments to perform pixel-based classification. Rigorous experimental results presented in this paper confirm the usefulness of the proposed approach and achieve improvement of 42.4% in the average segmentation errors, on UBIRIS.v2 dataset, as compared to the previous approach.
A new approach for object extraction from high-resolution satellite images is presented in this paper. The new approach integrates image fusion, multi-spectral classification, feature extraction and feature segmentati...
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