2,216 research outputs found

    AN ADAPTIVE BACKGROUND UPDATION AND GRADIENT BASED SHADOW REMOVAL METHOD

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    Moving object segmentation has its own niche as an important topic in computer vision. It has avidly being pursued by researchers. Background subtraction method is generally used for segmenting moving objects. This method may also classify shadows as part of detected moving objects. Therefore, shadow detection and removal is an important step employed after moving object segmentation. However, these methods are adversely affected by changing environmental conditions. They are vulnerable to sudden illumination changes, and shadowing effects. Therefore, in this work we propose a faster, efficient and adaptive background subtraction method, which periodically updates the background frame and gives better results, and a shadow elimination method which removes shadows from the segmented objects with good discriminative power. Keywords- Moving object segmentation

    Sub-convexity bound for GL(3)Γ—GL(2)GL(3) \times GL(2) LL-functions: GL(3)GL(3)-spectral aspect

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    Let Ο•\phi be a Hecke-Maass cusp form for SL(3,Z)SL(3, \mathbb{Z}) with Langlands parameters (ti)i=13({\bf t}_{i})_{i=1}^{3} satisfying ∣t3βˆ’t2βˆ£β‰€T1βˆ’ΞΎβˆ’Ο΅, tiβ‰ˆT,  i=1,2,3|{\bf t}_{3} - {\bf t}_{2}| \leq T^{1-\xi -\epsilon}, \quad \, {\bf t}_{i} \approx T, \quad \, \, i=1,2,3 with 1/201/2 0. Let ff be a holomorphic or Maass Hecke eigenform for SL(2,Z)SL(2,\mathbb{Z}). In this article, we prove a sub-convexity bound L(ϕ×f,12)β‰ͺmax⁑{T32βˆ’ΞΎ4+Ο΅,T32βˆ’1βˆ’2ΞΎ4+Ο΅}L(\phi \times f, \frac{1}{2}) \ll \max \{ T^{\frac{3}{2}-\frac{\xi}{4}+\epsilon} , T^{\frac{3}{2}-\frac{1-2 \xi}{4}+\epsilon} \} for the central values L(ϕ×f,12)L(\phi \times f, \frac{1}{2}) of the Rankin-Selberg LL-function of Ο•\phi and ff, where the implied constants may depend on ff and Ο΅\epsilon. Conditionally, we also obtain a subconvexity bound for L(ϕ×f,12)L(\phi \times f, \frac{1}{2}) when the spectral parameters of Ο•\phi are in generic position, that is tiβˆ’tjβ‰ˆT, for iβ‰ j, tiβ‰ˆT,  i=1,2,3.{\bf t}_{i} - {\bf t}_{j} \approx T, \quad \, \text{for} \, i \neq j, \quad \, {\bf t}_{i} \approx T , \, \, i=1,2,3.Comment: First draf

    Hybrid subconvexity bound for GL(3)Γ—GL(2)GL(3)\times GL(2) LL-functions: t and level aspect

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    \begin{abstract} In this article, we will get non-trivial estimates for the central values of degree six Rankin-Selberg LL-functions L(1/2+it,π×f)L(1/2+it, \pi \times f) associated with a GL(3){GL(3)} form Ο€\pi and a GL(2){GL(2)} form ff using the delta symbol approach in the hybrid settings i.e. in the level of GL(2){GL(2)} form and tt-aspect. \end{abstract}Comment: 30 pages, comments are welcom
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