Computational Time Complexity of Image Interpolation Algorithms

Abstract This paper deals with automatic generation of motion of a point under both geometric and non-geometric constraints. Optimal point paths are generated which are not only free of collisions with polygonal obstacles representing geometric constraints but also conform to non-geometric constraints such as speed of the motion, a maximum allowable change in the velocity vector and a minimum clearance from the obstacle boundaries. The concept of passage networks and conforming paths on the network are introduced. These are used to provide a new representation of the free space as well as a motion generation algorithm with a computational time complexity of only O(n3.log(n)), where n designates the total number of obstacle vertices. The algorithm finds the shortest or fastest (curved) path that also conforms with preset constraints on the motion of the point. The point paths generated are proved to be optimal while conforming to the constraints.

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Biclique Cryptanalysis on the Full Crypton-256 and mCrypton-128

Journal of Applied Mathematics ◽

10.1155/2014/529736 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Junghwan Song ◽

Kwanhyung Lee ◽

Hwanjin Lee

Keyword(s):

Computational Complexity ◽

Bipartite Graph ◽

Time Complexity ◽

Computational Time

Biclique cryptanalysis is an attack which reduces the computational complexity by finding a biclique which is a kind of bipartite graph. We show a single-key full-round attack of the Crypton-256 and mCrypton-128 by using biclique cryptanalysis. In this paper, 4-round bicliques are constructed for Crypton-256 and mCrypton-128. And these bicliques are used to recover master key for the full rounds of Crypton-256 and mCrypton-128 with the computational complexities of 2253.78and 2126.5, respectively. This is the first known single-key full-round attack on the Crypton-256. And our result on the mCrypton-128 has superiority over known result of biclique cryptanalysis on the mCrypton-128 which constructs 3-round bicliques in terms of computational time complexity.

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AN IMPROVED EDGE-DIRECTED IMAGE INTERPOLATION ALGORITHM

International Journal of Image and Graphics ◽

10.1142/s0219467812500234 ◽

2012 ◽

Vol 12 (04) ◽

pp. 1250023 ◽

Cited By ~ 1

Author(s):

XINGYUAN WANG ◽

ZHIFENG CHEN ◽

XUEMEI BAO

Keyword(s):

Real Time ◽

Local Structure ◽

Time Complexity ◽

Image Interpolation ◽

Threshold Value ◽

Subjective Quality ◽

Experimental Results ◽

Interpolation Algorithm ◽

Visual Effects ◽

Structure Characteristics

The paper sets forth an improved edge-directed image interpolation algorithm with low time complexity. The algorithm partitions images into homogeneous and edge areas by setting the preset threshold value based on the local structure characteristics. Specified algorithms are assigned to interpolate each classified areas respectively. The proposed method implements strategy in three steps to interpolate after setting the preset threshold value. In this way, it can achieve the goals of real-time interpolation and good subjective quality. Furthermore, the interpolated images have much more explicit edge regions and better visual effects using our proposed method than that of using other algorithms. Experimental results demonstrate that the method proposed by the authors is high-performed in image interpolation.

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Dbscan Assisted by Hybrid Genetic K Means Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8061.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 1973-1979

Keyword(s):

Data Mining ◽

Time Complexity ◽

Clustering Algorithm ◽

Hybrid Approach ◽

Research Direction ◽

Computational Time ◽

Main Concern ◽

Complex Data ◽

Data Mining Algorithms ◽

Mining Algorithms

The data mining algorithms functioning is main concern, when the data becomes to a greater extent. Clustering analysis is a active and dispute research direction in the region of data mining for complex data samples. DBSCAN is a density-based clustering algorithm with several advantages in numerous applications. However, DBSCAN has quadratic time complexity i.e. making it complicated for realistic applications particularly with huge complex data samples. Therefore, this paper recommended a hybrid approach to reduce the time complexity by exploring the core properties of the DBSCAN in the initial stage using genetic based K-means partition algorithm. The technological experiments showed that the proposed hybrid approach obtains competitive results when compared with the usual approach and drastically improves the computational time.

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Performance comparison and evaluation of vari-ous segmentation methods

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.9687 ◽

2018 ◽

Vol 7 (2) ◽

pp. 663

Author(s):

Vikashini Venkatesh ◽

Praveen P U

Keyword(s):

Time Complexity ◽

Performance Comparison ◽

Computational Time ◽

Normalized Cut ◽

Important Method ◽

Common Features ◽

Segmentation Methods ◽

Cut Method ◽

Different Types ◽

Watershed Method

Image segmentation is the most important method in the concept of image processing. It helps in analyzing the image accurately in many applications. It is generally used to assign or name, a label to individual pixels in an image, so that labels with similar name share common features. These related pixels result in same color, texture, or intensity. It also helps in identifying lines, curves and objects. These kinds of results help in different applications in the field of medical imaging, 3D constructions, etc. There are different kinds of segmentation methods already available for such applications. This paper briefs and compares three different types of segmentation methods like multithreshold method, watershed method and normalized cut method. It is compared based on computational time, complexity and number of clusters of the different methods used in the image.

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