The internet’s rapid growth has resulted in an increase the number of malicious files. Recently, powershell scripts and Windows portable executable (PE) files have been used behaviors. To solve these problems, artificial intelligence (AI) based malware detection methods widely studied. Among AI techniques, graph convolution network (GCN) was recently introduced. Here, we propose a method using ...

This thesis studies theoretical and practical aspects of the computation of planar polygonal Minkowski sums via convolution methods. In particular we prove the “Convolution Theorem”, which is fundamental to convolution based methods, for the case of simple polygons. To the best of our knowledge this is the first complete proof for this case. Moreover, we describe a complete, exact and efficient...

Dermoscopy image detection stays a tough task due to the weak distinguishable property of the object.Although the deep convolution neural network signifigantly boosted the performance on prevelance computer vision tasks in recent years,there remains a room to explore more robust and precise models to the problem of low contrast image segmentation.Towards the challenge of Lesion Segmentation in ...

This article presents a unified approach to the abstract notions of partial convolution and involution in $L^p$-function spaces over semi-direct product of locally compact groups. Let $H$ and $K$ be locally compact groups and $tau:Hto Aut(K)$ be a continuous homomorphism.  Let $G_tau=Hltimes_tau K$ be the semi-direct product of $H$ and $K$ with respect to $tau$. We define left and right $tau$-c...

Convolution and correlation are very basic image processing operations with numerous applications ranging from image restoration to target detection to image resampling and geometrical transformation. In real time applications, the crucial issue is the processing speed, which implies mandatory use of algorithms with the lowest possible computational complexity. Fast image convolution and correl...

The Logarithmic Image Processing model (LIP) is a robust mathematical framework for the processing of transmitted and reflected images. It follows many visual, physical and psychophysical laws. This works presents a new formulation of a 2D–convolution of separable kernels using the LIP paradigm. A previously stated LIP–Sobel edge detector is redefined with the new proposed formulation, and the ...

Proposed here is a new approach of edge detectors using edge-detecting masks generated from the basic edge detecting masks and convolution matrices of base lengths 2 and 3. The convolution matrices can be generated from a base unity matrix. These matrices behave like a low pass filter, which are effective in reducing noise in the edge detection process. The size of the convolution matrices incr...