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Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation
Arunkumar Rajendran,
Thamarai Muthusamy
Issue:
Volume 3, Issue 6-1, December 2014
Pages:
1-5
Received:
17 September 2014
Accepted:
22 September 2014
Published:
20 October 2014
Abstract: In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.
Abstract: In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an uns...
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Intelligent Traffic Light Controller Based on MCA Associative Memory
Emad I. Abdul Kareem,
Safana H. Abbas,
Salman Mahmood Salman
Issue:
Volume 3, Issue 6-1, December 2014
Pages:
6-16
Received:
22 September 2014
Accepted:
31 October 2014
Published:
6 November 2014
Abstract: Traffic in urban areas is mainly regularized by traffic lights, which may lead to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas where most of the traffic lights are based on a ‘fixed cycle’ protocol. This paper aims to design an intelligent controller of an intersection in a specific city using associative memory with multi-connect architecture via using this structure of neural network the intelligent controller can adapt to all street cases, which may be faced during its work. Not like other controllers, this work uses small associative memory. It will learn all street traffic conditions. The controller uses virtual data about the traffic condition of each street in the intersection. Thus, in an image processing module this video camera will provide visual information. This information will be processed to extract data about the traffic jam. This data will be represented in a look- up table, then smart decisions are taken when the intersection management determines the street case of each street at the intersection based on this look- up table.
Abstract: Traffic in urban areas is mainly regularized by traffic lights, which may lead to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas where most of the traffic lights are based on a ‘fixed cycle’ protocol. This paper aims to design an i...
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Analysis of Particle Swarm Optimization in Block Matching Algorithms for Video Coding
Kakalakannan Damodharan,
Thamarai Muthusamy
Issue:
Volume 3, Issue 6-1, December 2014
Pages:
17-23
Received:
1 November 2014
Accepted:
5 November 2014
Published:
12 November 2014
Abstract: Particle Swarm Optimization (PSO) is global optimization technique based on swarm intelligence. It simulates the behavior of bird flocking. It is widely accepted and focused by researchers due to its profound intelligence and simple algorithm structure. Currently PSO has been implemented in a wide range of research areas such as functional optimization, pattern recognition, neural network training and fuzzy system control etc.,. In video processing PSO is used to find the best matching block in Block matching algorithm, bit rate optimization for MPEG 1/2, object tracking and data clustering. In this paper the usage of PSO in Block matching algorithms for video compression is analyzed and the results are compared with the existing techniques.
Abstract: Particle Swarm Optimization (PSO) is global optimization technique based on swarm intelligence. It simulates the behavior of bird flocking. It is widely accepted and focused by researchers due to its profound intelligence and simple algorithm structure. Currently PSO has been implemented in a wide range of research areas such as functional optimiza...
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Super-Resolution Method Based on Edge Feature for High Resolution Imaging
Ryohei Yamaguchi,
Teppei Sato,
Atsushi Koike,
Naoya Wada,
Hitomi Murakami
Issue:
Volume 3, Issue 6-1, December 2014
Pages:
24-29
Received:
29 November 2014
Accepted:
12 December 2014
Published:
27 December 2014
Abstract: Super-resolution is recently one of the most attractive research themes. An image interpolation method using co-variance between neighboring pixels, so called New Edge-Directed Interpolation (NEDI), was proposed. It enables to interpolate pixels quantitatively regardless of edge features. However, in estimation of predictive coefficients and determination of NEDI’s local window size, edge features are not made consideration. So, NEDI cannot necessarily satisfy quality of picture. In order to overcome this problem, we propose a new intra-frame super-resolution method that window sizes and configurations are adaptively determined depending on edge strengths and orientations. Experimental simulation shows that the proposed method can interpolate pixels more clearly than NEDI for many kinds of edges.
Abstract: Super-resolution is recently one of the most attractive research themes. An image interpolation method using co-variance between neighboring pixels, so called New Edge-Directed Interpolation (NEDI), was proposed. It enables to interpolate pixels quantitatively regardless of edge features. However, in estimation of predictive coefficients and determ...
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Stream Flow Painting from Real Images Using Anisotropic Band-Pass Filter
Xiaohua Zhang,
Ning Xie,
Yuelan Xin,
Heming Huang
Issue:
Volume 3, Issue 6-1, December 2014
Pages:
30-38
Received:
20 September 2014
Accepted:
28 January 2015
Published:
27 February 2015
Abstract: A new non-photorealistic rendering algorithm is proposed for creating artistic painting with soft stream flow from natural color images. The algorithm consists mainly of two stages, that is, a revised bilateral filter called trilateral filter is firstly applied to original color image for creating drawings using gradient information and then a DoG-like band-pass filter is adapted for generating soft stream flow along the eigenvectors and therefore the image is smoothed along curved stream lines. The proposed trilateral filter is an extension of bilateral filter by incorporating gradient space. On the other hand, DoG-like band-pass filter is designed by applying eigenvectors and eigenvalues of a structure tensor matrix calculated at each pixel. Our approach effectively preserves image main structures while smoothing image regions in an anisotropic way. Even in regions with lower contrast, stream flow-like potential structures are also well produced due to a gradient relaxation. The experiments demonstrate that the proposed algorithm works well and produces good and pleasant visual results.
Abstract: A new non-photorealistic rendering algorithm is proposed for creating artistic painting with soft stream flow from natural color images. The algorithm consists mainly of two stages, that is, a revised bilateral filter called trilateral filter is firstly applied to original color image for creating drawings using gradient information and then a DoG-...
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