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The Kinect provides an opportunity to collect large quantities of training data for visual learning algorithms relatively effortlessly. To this end we investigate learning to automatically segment humans from cluttered images (without depth information) given a bounding box. For this algorithm, obtaining a large dataset of images with segmented humans is crucial as it enables the possible variations...
This paper presents a novel solution toward the accurate and automatic cartilage segmentation with multi-contrast MR images based on pixel classification. The previous pixel classification based works for cartilage segmentation only rely on the labeling by a trained classifier, such as support vector machines (SVM) or k-nearest neighbors. However, these frameworks do not consider the spatial information...
In this work, we propose a framework to help in the design of the ground-truth for the classification of remote sensing images. It consists first to segment the considered image by means of a level set method and then to extract the segments characterized by the largest numbers of pixels. Afterward, the selected segments are labeled by a human user. Experimental results obtained on a very high resolution...
Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale...
This paper presents a two-stage machine learning method by simulating visual system for segmentation of marrow cell image. Firstly, the scale space clustering is employed to simulate primary visual system to separate image into series regions with similar colours. Different from traditional methods, we focus on a few significant clusters rather than all of them. Priori knowledge is considered to group...
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