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International Journal of Computer Vision & Signal Processing (ISSN: 2186-1390)  


Vol. 2, No. 1:

A Novel Wavelet Based Image Fusion for Brain Tumor Detection

Vivek Angoth, CYN Dwith, Amarjot Singh, National Institute of Technology, Warangal, India

A Real-time Scheme of Video Stabilization for Mobile Surveillance Robot

Saksham Keshri, National Institute of Technology Karnataka, India
S.N. Omkar, IISc, Bangalore, India
Amarjot Singh, National University of Singapore, Singapore
Vinay Jeengar, Maneesh Kumar Yadav, National Institute of Technology Karnataka, India

Wireless Capsule Endoscopy Segmentation using Global-Constrained Hidden Markov Model and Image Registration

Yiwen Wan, Prakash Duraisamy, University of North Texas, USA
Mohammad S Alam, University of South Alabama, USA
Bill Buckles, University of North Texas, USA

Choosing Appropriate Homography Transformation for Building Panoramic Images

Prakash Duraisamy, Yassine Belkhouche, Stephen Jackson, Kamesh Namuduri, Bill Buckles, University of North Texas, USA

A Novel Wavelet Based Image Fusion for Brain Tumor Detection

Abstract:
Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. In this paper, we propose an efficient wavelet based algorithm for tumor detection which utilizes the complementary and redundant information from the Computed Tomography (CT) image and Magnetic Resonance Imaging (MRI) images. Hence this algorithm effectively uses the information provided by the CT image and MRI images there by providing a resultant fused image which increases the eciency of tumor detection. We also evaluate the effectiveness of proposed algorithm on varying the wavelet fusion parameters like number of decompositions, type of wavelet used for the decomposition. The experimental results of the simulation on MRI and CT images show the performance efficiency of the proposed approach.
Full Paper (in PDF)

A Real-time Scheme of Video Stabilization for Mobile Surveillance Robot

Abstract:
The purpose of this research is to develop a mobile surveillance robot capable of capturing and transmitting video on rough terrains. Recorded video is affected by jitters resulting into significant error between the desired and captured video flow. Image registration with a contrario RANSAC variant has been used to minimize the error between present and desired output video as it has proved to be a fast algorithm for video stabilization as compared to the conventional stabilization methods. This is the first paper which makes use of this method to design mobile wireless robot for surveillance applications. The video captured by the robot is stabilized and transmitted to the controller in the control room. Once the video is stabilized the controller moves the objects from one place to another with the help of robotic arm mounted to the robot using a wireless transmitter and receiver. The surveillance capabilities of the system are also tested in low illumination situations as spying in dark is an important requirement of todays advanced surveillance systems.
Full Paper (in PDF)

Wireless Capsule Endoscopy Segmentation using Global-Constrained Hidden Markov Model and Image Registration

Abstract:
This paper describes about analysis of wireless capsule endoscopy (WCE) using pattern recognition and statistical analysis. Specifically, we introduce a novel approach to discriminate between oesophagus, stomach, small intestine, and colon tissue present in WCE. Automatic image analysis can expedite this task by supporting the clinician and speeding up this process. Video segmentation of WCE into the four parts of the gastrointestinal tract is one way to aid the physician. The segmentation approach described in this paper integrates pattern recognition with statistical analysis. Initially, a support vector machine is applied to classify video frames into four classes using a combination of multiple color and texture features as the feature vector. A Poisson cumulative distribution, for which the parameter depends on the length of segments, models a prior knowledge. A priori knowledge together with inter-frame difference serves as the global constraints driven by the underlying observation of each WCE video, which is fitted by Gaussian distribution to constrain the transition probability of hidden Markov model. We also used image registration method to confirm our segmentation results. Experimental results demonstrated effectiveness of the approach.
Full Paper (in PDF)

Choosing Appropriate Homography Transformation for Building Panoramic Images

Abstract:
In building panoramic images, the selection of appropriate homography plays a crucial role in reducing the error and in registering the images accurately. In this paper, we demonstrate a method for selecting the appropriate homography for building the panoramic image based on information extracted from the images. It is shown that using homographies from the appropriate subgroup, the undesirable distortions can be reduced which improves the quality of the panoramic image. We tested our method both on synthetic and real world images. We also discussed and compared several error metrics to evaluate the accuracy of registration.
Full Paper (in PDF)

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