Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP.
Data depth is a statistical method which models data distribution in terms of centeroutward ranking rather than density or linear ranking. While there are a lot of academic interests, its applications are hampered by the lack of a method which is both robust and efficient. This paper introduces Half-Space Mass which is a significantly improved version of half-space data depth. Half-Space Mass is the only data depth method which is both robust and efficient, as far as we know. We also reveal four theoretical properties of Half-Space Mass: (i) its resultant mass distribution is concave regardless of the underlying density distribution, (ii) its maximum point is unique which can be considered as median, (iii) the median is maximally robust, and (iv) its estimation extends to a higher dimensional space in which the convex hull of the dataset occupies zero volume. We demonstrate the power of Half-Space Mass through its applications in two tasks. In anomaly detection, being a maximally robust location estimator leads directly to a robust anomaly detector that yields a better detection accuracy than half-space depth; and it runs orders of magnitude faster than L 2 depth, an existing maximally robust location estimator. In clustering, the Half-Space Mass version of K-means overcomes three weaknesses of K-means.
A triangle mesh compression algorithm based on reverse subdivision is introduced. By improving reverse Butterfly simplification algorithm, a mesh simplification algorithm based on reverse Modified Loop scheme is proposed. The dense triangle mesh is decomposed into progressive meshes which consist of a base mesh and a series of displacement wavelets. The progressive meshes are compressed with embedded zerotree coding by constructing displacement wavelets tree structure. The experiments show that the proposed approach is faster and more efficient than previous related techniques. The proposed algorithm can be used for progressive transmission over wireless networks and 3D graphics real-time rendering on mobile terminals.
Main defects of infrared touch screen are low resolution and sensors to be easily damage, which makes it can't realize the accurate positioning. To overcome infrared screen principle inherent defects, we propose a new solution by the principle of innovation, with a laser tube instead of the infrared emission tube, plastic optical fiber instead of the infrared receiver, CMOS sensor instead of complex circuits, which resulting a design of touch-screen with high resolution infrared based on plastic optical fiber and image processing. Currently, it does effectively handle the problem of the low revolution, poor stability and the complex maintenance of infrared touch screen, to make it a great business prospects.
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