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The detection and extraction of buildings using high resolution synthetic aperture radar (SAR) images has been the topic of recent discussions. In this paper, a framework for building extraction and classification is proposed. Buildings are classified into three kinds: commercial architecture, residential building and Industrial building, by the fusion of structure features (point-like, linear and...
The grammar of facade structures is often related to regularly distributed signature patterns in high-resolution synthetic aperture radar (SAR) images. Given the structural details in the SAR image, algorithms for building extraction/monitoring should exploit this contextual information for an object-based analysis. In this paper we propose a framework to extract detailed high rise building features...
Previous polarimetric synthetic aperture radar (PolSAR) images change detection methods are generally undertaken in the pixel scale, resulting in overlooking the semantic information. To solve this problem, this paper presents a superpixel-based PolSAR images change detection methods. Different from some previous methods, an improved SLIC superpixel segmentation method is introduced in polarimetric...
Accurate recognition of burning state is critical in sintering process control of rotary kiln. Recently, flame image-based burning state recognition has received much attention. However, most of the existing methods demand accurate image segmentation, which is quite challenging due to poor image quality caused by smoke and dust inside the kiln. In this study, we develop a more reliable method for...
Recognition of burning states is a critical issue in sintering process control of rotary kiln, and the recognition is usually based on the temperatures of burning zone. Recently, flame image-based burning states recognition has received much attention. However, most of the methods reported involve image segmentation, which is a very challenging problem due to the poor quality of flame images of rotary...
In this paper, a novel technique is proposed to identify computer graphics by employing second-order statistics to capture the significant statistical difference between computer graphics and photographic images. Due to the wide availability of JPEG images, a JPEG 2-D array formed from the magnitudes of quantized block DCT coefficients is deemed a feasible input; however, a difference JPEG 2-D array...
A new method is proposed to get the segmentation threshold and detect the dark spot in oil-spill images. The method is inspired from the ??-distribution model of sea background, which is widely accepted to describe the ocean clutter. By comparing the histograms of oil-spill region and the sea background, it is found that the oil slicks break the ??-distribution model, but there is still some information...
We consider here a change detection problem: to find regions of change on a test image with respect to a reference image. Unlike the state-of-the-art change detection and background subtraction algorithms that compute only local (pixel location-based) changes, we propose to minimize a novel region-based energy functional based on Bhattacharya coefficient involving histograms of image features. The...
We propose a blood cell classification method with the aim of designing an automatic differential blood count system, which can help cancer diagnosis. The proposed system contains two automated steps: an active contour-based segmentation of blood cells from microscopy images and their classification. For classification we investigate several joint histogram-based features extracted from the segmented...
This paper proposes a data driven image segmentation algorithm, based on decomposing the target output (ground truth). Classical pixel labeling methods utilize machine learning algorithms that induce a mapping from pixel features to individual pixel labels. In contrast we propose to first extract features from both images and labels. Subsequently we induce a mapping from pixel features to label features...
Background subtraction is an effective technique for motion detection. A traditional background subtraction algorithm assumes a moving object (or objects) with respect to a static background, and segments the moving object(s) by classifying pixels into foreground and background with trained statistical models. Because classical background subtraction algorithms work with intensity images, they cannot...
Accurate segmentation of moving objects in an image sequence is a crucial task in many computer vision and image analysis applications such as the mineral processing industry and automated visual surveillance. In this paper, we introduce a novel algorithm for spatio-temporal segmentation of image sequences to achieve accurate extraction of the boundary of moving objects from noisy background. Our...
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