A real-time segmentation of images requires features which are fast to calculate and a segmentation procedure which can fastly classify pixels or regions with respect to these features. Automatic surveillance of a hon...
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A real-time segmentation of images requires features which are fast to calculate and a segmentation procedure which can fastly classify pixels or regions with respect to these features. Automatic surveillance of a honeybee comb requires the segmentation of homogeneous image regions. Such regions correspond to visible brood cells while the regions which are covered by a crowd of bees appear non-homogeneous. In this paper a novel and efficient way to calculate an existing homogeneity feature is presented. Second, it is extended to a novel feature and empirical results are provided which show improvements over the basic homogeneity value. Third, a simple but effective segmentation procedure is presented and segmentation results are provided.
Several areas of land degradation are present in the northeastern part of Brazil, resulting primarily from old practices of vegetation clearing and burning for subsistence farming and grazing. However, no reliable inf...
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Several areas of land degradation are present in the northeastern part of Brazil, resulting primarily from old practices of vegetation clearing and burning for subsistence farming and grazing. However, no reliable information is currently available on the extent of these areas and whether they are expanding over time. Taking the Gilbue's region as an example, this study shows the potential for obtaining this information using multitemporal dual-season Landsat Thematic Mapper (TM) imagery. We used digital imageprocessing, which involved the enhancement of soil-fraction images by segmentation/region classification and Normalized Difference Index (NDI) techniques. The results show highly degraded soils covering an almost continuous area of approximately 600 km(2). The data also show that, over the past two decades, land degradation has been concentrated mainly along and in the heads of drainage systems, a pattern that indicates removal of riparian forests, thereby compounding the serious problem of sediment loading in the streams in the area. We show that satellite remotely sensed data can provide valuable information in support of environmental policies for northeast Brazil.
This study presents the first experimental results on the integration of discrete wavelet transform (DWT) derived contexture (macro-texture) and grey-level co-occurrence matrices (GLCM) (micro-texture) in the recognit...
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This study presents the first experimental results on the integration of discrete wavelet transform (DWT) derived contexture (macro-texture) and grey-level co-occurrence matrices (GLCM) (micro-texture) in the recognition and extraction of the following selected urban land cover information from very-high spatial resolution Quickbird imagery: residential buildings, commercial buildings, roads/parking and green vegetation. The DWT filters capture the lower and mid-frequency texture information, whereas the GLCM captures the high-frequency textural components, for the same scene features. Besides the commonly used micro-texture (GLCM), the macro-texture (DWT) is modelled here to take care of the contextual information defined as feature edge (size and shape). This edge information is arguably derived from the multi-scale and multidirectional components of the DWT. From the statistical significance testing of the per-pixel classification accuracy results with the z-score, it was found that the integrated feature sets comprising the Quickbird spectral bands, 3 x 3 mean-GLCM and the first level of the vertical-DWT sub-band outperformed all the other tested input primitives, with a z-score value of 2.25. The accuracy results showed that all the three feature primitives were essential in improving the recognition and extraction of tested urban land cover in very-high spatial resolution Quickbird imagery.
This work aims to present an assessment of a modified version of the standard EM clustering algorithm for remotesensing data classification. As observing clusters with very similar mean vectors but differing only on ...
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A semifragile watermarking scheme for remotesensingimages is presented. The suggested scheme uses the signature of the multispectral or hyperspectral image to embed the mark and detects a forgery of the marked image...
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This paper represents a survey of the state of art reached in 3D Face recognition frameworks and show some different approaches developed and tested by its authors. We have designed a strong algorithm that is based on...
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This paper represents a survey of the state of art reached in 3D Face recognition frameworks and show some different approaches developed and tested by its authors. We have designed a strong algorithm that is based on genetic algorithms, Principal Component Analysis (PCA) and face geometry assumptions, for head pose normalization of 3D scanned face models. Experiments conducted on the GavaDB database show a 100% success rate in correctly those models that "stare" at the camera (with a perfect alignment in 83% of the cases). A previously developed algorithm for nose-tip detection based on an adapted Khoshelham GHT has been used to create an automatic repere's points detection system with the purpose of obtaining a biometric system for AFR (Automatic Face recognition) using 3D Face templates. Subsequently two different methodologies, based respectively on an unsupervised self-organizing neural network (SOM) and upon a graph matching, have been implemented to validate the performance of the new 3D facial feature identification and localization algorithm. Experiments have been performed on a dataset of 23 3D faces acquired by a 3D laser camera at eBIS lab with pose and expression variations. Then an optimization of the search of the points ALS and ALD of the nose and a new graph approach for the recognition base on several new points has been implemented. Experiments have been performed on a dataset of 44 faces, acquired by a 3D laser camera at eBIS lab with pose and expression variations, scoring a result of 100% of recognition.
In the field of crop identification with remotesensing technology, current multi-temporal methods usually do not made full use of target crop's temporal features and spectral features. An improved multi-temporal ...
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An automated registration system named AISIR (Automated inter-sensor/inter-band satellite image registration) has been designed and implemented for the purpose of registering satellite images acquired using different ...
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An automated registration system named AISIR (Automated inter-sensor/inter-band satellite image registration) has been designed and implemented for the purpose of registering satellite images acquired using different sensors and spectral bands. Sensor and environmental noise, contrast non-uniformities, and inter-sensor and inter-band intensity mapping differences are addressed in the AISIR system. First, a novel modified Geman-McClure M-estimation scheme using a robust phase-adaptive complex wavelet feature representation is introduced for robust control point matching. Second, an iterative refinement scheme is introduced in the AISIR system for improved control point pair localization. Finally, the AISIR system introduces a robust mapping function estimation scheme based on the proposed modified Geman-McClure M-estimation scheme. The AISIR system was tested using various multi-spectral optical, LIDAR, and SAR images and was shown to achieve better registration accuracy than state-of-the-art M-SSD and ARRSI registration algorithms for all of the test sets. (C) 2009 Elsevier B.V. All rights reserved.
Forest fires are serious natural disasters around the world, which will devastate forests heavily and cause enormous financial losses. It has great significance for the application research and development of forest f...
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ISBN:
(纸本)9781424473021
Forest fires are serious natural disasters around the world, which will devastate forests heavily and cause enormous financial losses. It has great significance for the application research and development of forest fire monitoring, which improve forecast accuracy of forest fire warning, prepare for forest fire prevention, detect fire point in time, and extinguish the fire fast and efficient. The method mentioned in this paper integrates remotesensing, imagerecognition, GIS, and remote monitoring technology together, based on the video monitoring images and the MODIS remotesensing data to design a real-time, effective forest fire monitoring system. The coordinates are sent to forest fireproof GIS when the fire point are detected, and then the forest fireproof GIS can search and calculate all the information necessary for fireproof decision-making.
image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smooth...
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image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.
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