Guided by a building concept model, which interprets building into different levels and scales, this paper presents a method to extract buildings in monocular urban aerial images without priori illuminating or orienta...
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ISBN:
(纸本)0780381939
Guided by a building concept model, which interprets building into different levels and scales, this paper presents a method to extract buildings in monocular urban aerial images without priori illuminating or orientation knowledge. By using a shadow context model, a method was proposed to estimate the direction of shadow cast to verify the raw segmentations. Building extractions are refined by context, and a method of partial snake with the aid of the shadow cast direction is proposed, which can sharply reduce the iteration complexity and the influence caused by illumination. The extraction of self-shadow on gable roof with a proposed mathematical roof model is also discussed in this paper.
Sea-ice motion consists of complex non-rigid motions involving continuous, piece-wise continuous and discrete particle motion. Techniques for estimating non-rigid motion of sea ice from pairs of satellite images (gene...
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Sea-ice motion consists of complex non-rigid motions involving continuous, piece-wise continuous and discrete particle motion. Techniques for estimating non-rigid motion of sea ice from pairs of satellite images (generally spaced three days apart) are still in the developmental stages. For interior Arctic and Antarctic pack ice, the continuum assumption begins to fail below the 5 km scale with evidence of discontinuities already revealed in models and remotesensing products in the form of abrupt changes in magnitude and direction of the differential velocity. Using a hierarchical multi-scale phase-correlation method and profiting from known limitations of cross correlation methods, we incorporate the identification of discontinuities into our motion estimation algorithm, thereby descending below the continuum threshold to examine the phenomenon of discontinuous non-rigid sea-ice motion.
This work presents a survey on some artificial neural networks designed to extract relevant features from input images in 3 specific areas: 1) remotesensing, 2) 3D anthropometry, and 3) machine vision.
This work presents a survey on some artificial neural networks designed to extract relevant features from input images in 3 specific areas: 1) remotesensing, 2) 3D anthropometry, and 3) machine vision.
A fundamental challenge in applying texture features to statistical object modeling is recognizing differently oriented spatial patterns. Rows of moored boats in remote sensed images of harbors should be consistently ...
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A fundamental challenge in applying texture features to statistical object modeling is recognizing differently oriented spatial patterns. Rows of moored boats in remote sensed images of harbors should be consistently labeled regardless of the orientation of the harbors, or of the boats within the harbors. This is not straightforward to do, however, when using anisotropic texture features to characterize the spatial patterns. We here propose an elegant solution, termed normalized texture motifs, that uses a parametric statistical model to characterize the patterns regardless of their orientation. The models are learned in an unsupervised fashion from arbitrarily orientated training samples. The proposed approach is general enough to be used with a large category of orientation-selective texture features.
In this paper, we propose a registration method between GIS data and high-resolution satellite images of urban scenes. Our approach consists of two steps: firstly, the urban straight main road features in images are e...
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In this paper, we propose a registration method between GIS data and high-resolution satellite images of urban scenes. Our approach consists of two steps: firstly, the urban straight main road features in images are extracted by combining their spectral information with a geometric constraint. Then, by exploiting the frequency spectrum property of linear stripe regions, we perform matching of the road layer of geographic information data and feature images using a new FFT-based algorithm. The significant advantage of the approach is its capability to match the rotated and scaled images robustly even when they have a large scale change or obvious geometric differences. Experimental results demonstrate the robustness and efficiency of the method.
Improved target detection, reduced false alarm rates, and enhanced timeliness are critical to meeting the requirements of current and future military missions. We present a new approach to target detection, based on a...
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Improved target detection, reduced false alarm rates, and enhanced timeliness are critical to meeting the requirements of current and future military missions. We present a new approach to target detection, based on a suite of imageprocessing and exploitation tools developed under the intelligent searching of images and signals (ISIS) program at Los Alamos National Laboratory. Performance assessment of these algorithms relies on a new metric for scoring target detection that is relevant to the analyst's needs. An object-based loss function is defined by the degree to which the automated processing focuses the analyst's attention on the true targets and avoids false positives. For target detection techniques that produce a pixel-by-pixel classification (and thereby produce not just an identification of the target, but a segmentation as well), standard scoring rules are not appropriate because they unduly penalize partial detections. From a practical standpoint, it is not necessary to identify every single pixel that is on the target; all that is required is that the processing draw the analyst's attention to the target. By employing this scoring metric directly into the target detection algorithm, improved performance in this more practical context can be obtained.
The following topics are dealt with: computational radar imaging; watermarking; video compression standards; biomedical imageprocessing; error resilience/concealment; image segmentation; image filtering and morpholog...
The following topics are dealt with: computational radar imaging; watermarking; video compression standards; biomedical imageprocessing; error resilience/concealment; image segmentation; image filtering and morphological processing; image enhancement; video segmentation; image/video indexing and retrieval; DCT-based video coding; image compression; distributed source coding; media security issues in streaming and mobile applications; face detection, recognition, and classification; video summarization and browsing; biometrics; video object tracking; content-based analysis of multi-modal high dimensional medical images; image forensics; image denoising and deblurring; lossy image coding; wavelet video coding and scalability; motion detection and estimation; feature extraction; geosciences and remotesensing; super-resolution and interpolation; video streaming and networking; shape extraction and analysis; image modeling; image scanning, display, and printing; image formation; image coding; source-channel coding; transcoding; document imageprocessing; image representation, rendering, and quality assessment; stereoscopic imageprocessing and 3D modeling; cryptography.
Automatic warning systems for protection against airborne threats require a high detection probability at long ranges. Infrared Search and Track (IRST) systems which are designed as monocular and passive systems are a...
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