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Visual object tracking is a fundamental research topic in computer vision. In this paper, we proposed a novel hybrid tracking method based on Pulse Coupled Neural Network (PCNN) and Multiple Instance Learning (MIL). Most modern trackers may be inaccurate when the training samples are imprecise which causes drift. To resolve these problems, MIL method is introduced into the tracking task, which can...
Object-based image analysis (OBIA) in combination with domain knowledge is an important methodology for remote sensing image interpretation. It can deal with semantic gap effectively. In this paper, a knowledge-based procedure for remote sensing image classification is presented. Domain knowledge is represented with semantic network, which guides the initial classification of the image. A quantitative...
A watershed transform segmentation method based on hybrid gradient that combines intensity and texture visual cues is proposed. Firstly a bilateral filtering method derived from robust statistics is used to extract the intensity gradient. Secondly a Gabor filter bank is applied to extract texture features. With a smoothing post process, the texture gradient is extracted. Then by morphological dilation...
The presence of cavities in the upper lung zones is an important indicator of highly infectious Tuberculosis (TB). Diagnoses performed by the radiologists are labor intensive and of high inter-reader variation. After analyzing the existing computer-aided detection techniques, we propose an fully automated TB cavity detection system which combines a 2D Gaussian-model-based template matching (GTM) for...
Fluid vector flow (FVF) is a recently developed edge-based parametric active contour model for segmentation. By keeping its merits of large capture range and ability to handle acute concave shapes, we improved the model from two aspects: edge leakage and control point selection. Experimental results of cavity segmentation in chest radiographs show that the proposed method provides at least 8% improvement...
Most image segmentation algorithms extract regions satisfying visual uniformity criteria. Unfortunately, because of the semantic gap between low-level features and high-level semantics, such regions usually do not correspond to meaningful parts. This has motivated researchers to develop methods that, by introducing high-level knowledge into the segmentation process, can break through the performance...
A new multi-scale image segmentation algorithm based on nonsubsampled contourlet transform (NSCT) and simplified plus coupled neural network (SPCNN) has been discussed in this paper. Comparing with plus coupled neural network (PCNN), the SPCNN algorithm can decrease the complexity of adjusting parameters significantly. First we combine susan edge detector with SPCNN, more accurate result can be obtained...
An improved segmentation method is proposed in this paper for metallographic images, especially those objects surrounded with complex texture. If only watershed algorithm is used for the segmentation of an image, the over-segmentation problem will be serious. To solve this, we proposed a new approach. In the method, we take the curvelet transform to denoise initial images by thresholding the different...
Accurate lung field segmentation is crucial to computer-aided diagnosis (CAD) of lung diseases such as lung cancer and tuberculosis (TB). In this paper, we propose a modified gradient vector flow based active shape model (GVF-ASM) for lung field extraction from chest radiographs. Experimental results show that the proposed technique provides around 3-5% improvement over the ASM techniques.
Mean shift clustering tends to generate accurate segmentations of color images, but choosing the scale parameters remains a difficult problem which has a strong impact on its performance. We present an adaptive image segmentation framework that achieves a task-dependent top-down adaption of the scale parameters. The proposed method can be used under the context of a relevance feedback-based content-based...
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