Studies on rotation invariant texture in remotesensingimageprocessing are relatively rare. Local Binary pattern (LBP) is a relatively new rotation invariant texture measure which is theoretically simply but powerfu...
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ISBN:
(纸本)9781424463886;9780769539874
Studies on rotation invariant texture in remotesensingimageprocessing are relatively rare. Local Binary pattern (LBP) is a relatively new rotation invariant texture measure which is theoretically simply but powerful. In this paper, the LBP operator was proposed to calculate texture features of the stimulant image derived from high-resolution remotesensingimage. The produced texture image was combined with the spectral data in image classification to evaluate the performance of the rotation invariant texture measure. The result was compared to classifications using spectral data alone and plus traditional rotation variant texture images. Experiments demonstrate that compared to spectral classification, the classification overall accuracy can be significantly improved when the rotation invariant texture is included. The results also show that the rotation invariant texture result show a more than four percentage increase in overall accuracy, compared with the classification result with traditional Grey-Level Co-occurrence Matrix texture.
In this paper, we propose an active contour with threshold value to detect objects and at the same time get rid of unimportant parts rather than extract all information. The basic ideal of our model is to introduce a ...
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ISBN:
(纸本)9781424475421
In this paper, we propose an active contour with threshold value to detect objects and at the same time get rid of unimportant parts rather than extract all information. The basic ideal of our model is to introduce a weight matrix into region-based active contours, which can enhance the weight for the main parts while filter the weak intensity, such as shadows, illumination and so on. Moreover, we can choose threshold value to set weight matrix manually for accurate image segmentation. Thus, the proposed method can extract objects of interest in practice. Coupled partial differential equations are used to implement this method with level set algorithms. Experimental results show the advantages of our method in terms of accuracy for image segmentation.
This paper describes a lane recognition method for the lane departure warning system of smart vehicles and the algorithm implemented in a dual core ADI-BF561 600MHz DSP embedded system to verify the functionality. Sin...
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This paper describes a lane recognition method for the lane departure warning system of smart vehicles and the algorithm implemented in a dual core ADI-BF561 600MHz DSP embedded system to verify the functionality. Since the computing power and memory size of the embedded system are not as good as those on a personal computer, special techniques have been applied in the algorithm to enhance the performance of lane recognition while maintaining the reliability of the results. The applied median filter can obtain the median element of a 4-by-3 array using only 19 comparison operations. Furthermore, the edge enhancing filter can washout the foreign objects in the region of interest and keep the lane marks with tilt/slope pattern. Using these two tools as the basis, the applied algorithm can detect the event of lane-departure and alarm the warning to assist drivers for driving safety on the road. Besides the basic techniques, the lane mark enhancement is implemented to improve the accuracy of detection and double-line detection is also added to alert the driver for eventual traffic rule violation. The system has been implemented and tested on a DSP-based embedded platform. It can perform the required processes in real-time under various weather conditions.
Hidden Markov Models (HMMs) have been widely used in landmine detection with Ground Penetrating Radar (GPR) data; however, to the best of our knowledge, there are no other studies that investigated the simultaneous le...
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Hidden Markov Models (HMMs) have been widely used in landmine detection with Ground Penetrating Radar (GPR) data; however, to the best of our knowledge, there are no other studies that investigated the simultaneous learning of the features and the HMM parameters. In this paper, we present a novel method based on Gibbs sampling that both learns a feature extraction model as well as an HMM model. The new system allows for the training of new features when the sensor systems are different. Experiments show that our algorithm is more robust to initialization and can find better solutions.
image matching is a fundamental task in the analysis of the remotesensing data. The precision of the image matching have played an important role in the application of remotesensingimages, such as three-dimensional...
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image matching is a fundamental task in the analysis of the remotesensing data. The precision of the image matching have played an important role in the application of remotesensingimages, such as three-dimensional modeling, pattern reorganization, panoramic reconstruction, super resolution, phasic analysis, etc. At present, the development of image matching technology and later use in China and abroad is mainly focused on the robust key point, which means that the extracted feature should been invariant to the translation, scale and rotation etc. In recent years, the sift algorithm has been the most popular for extracting interest points. But the calculation procedure is too slow. In this paper, our approach starts with a matching algorithm that combine Harris detector together with sift descriptor method. Our approaches start with the procedure of counting Harris method on the remotesensingimage and discard the key point with low contrast. Moreover, we perform an accurate key point location. At last, we use SIFT descriptor as a feature to perform matching from the descriptor point vector in the reference image to the sets of point vector in the querying image.
The INTERSAFE-2 project aims to develop and demonstrate a Cooperative Intersection Safety System that is able to significantly reduce injury and fatal accidents at intersections. The cooperative sensor data fusion is ...
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The goal of this paper is the presentation of a method and results for artificial neural networks crops classification based on HyMap hyperspectral data. The method that uses an ANNs does not only depend on statistica...
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The goal of this paper is the presentation of a method and results for artificial neural networks crops classification based on HyMap hyperspectral data. The method that uses an ANNs does not only depend on statistical parameters of particular class and hence makes it possible to include texture information. To experiment with variable pattern size two data sets were chosen with 10 bands obtained after MNF and 5 hyperspectral vegetation indicies. Next to post classification crops maps, additional quality layers were generated to check which classes are “problematic” because of spectral similarity or errors in the training/reference data. The best accuracy was achieved using the 10 MNF bands with the 3×3 pixel sub pattern size -94,8 %.
Active contour methods like snakes, have become a basic tool in computer vision and image analysis over the last years. They have proven to be adequate for the task of finding boundary features like broken edges in an...
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ISBN:
(纸本)9781424475421
Active contour methods like snakes, have become a basic tool in computer vision and image analysis over the last years. They have proven to be adequate for the task of finding boundary features like broken edges in an image. However, when applying the basic snake technique to synthetic aperture radar (SAR) remotesensingimages, the detection of varying-contrast edges may not be satisfying. This is caused by the special imaging technique of SAR and the commonly known specklenoise. In this paper we propose the use of asymmetric external energy terms to cope with this problem. We show first results of the method for the detection of edges of tidal creeks using an ENVISAT ASAR image. These creeks can be found in the World Heritage Site ”Wadden Sea” located at the German Bight (North Sea).
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