Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications(Institute of Electrical and Electronics Engineers Inc., 2014) Parameshwari, D.S.; Aparna., P.In this paper, we propose an efficient textural feature extraction algorithm (TFEA) based on higher order statistical cumulant namely Kurtosis for a class of brain MR imaging applications. Using a model that represents the wavelet coefficient energies of the sub-bands of multi-level decomposition of the image as a basis, a feature set involving three parameters for each band corresponding to probability density function (PDF) of generalized Gaussian type is derived. The logical correctness and working of the proposed TFEA are first verified based on MATLAB ver.2010a tool. The algorithm is applied in conjunction with one of the popularly used canny edge detection algorithm for segmenting a class of real and synthetic magnetic resonance (MR) images to detect the region of a tumor if present. The use of the proposed approach results in reduced feature set size thus obviating the need for employing specialized feature selection/ reduction algorithms. A detailed look at the experimental results clearly show an improvement in the segmentation quality compared with conventional method. © 2014 IEEE.Item Depth image super-resolution: A review and wavelet perspective(Springer Verlag service@springer.de, 2017) Balure, C.S.; Ramesh Kini, M.We propose an algorithm which utilizes the Discrete Wavelet Transform (DWT) to super-resolve the low-resolution (LR) depth image to a high-resolution (HR) depth image. Commercially available depth cameras capture depth images at a very low-resolution as compared to that of the optical cameras. Having an highresolution depth camera is expensive because of the manufacturing cost of the depth sensor element. In many applications like robot navigation, human-machine interaction (HMI), surveillance, 3D viewing, etc. where depth images are used, the LR images from the depth cameras will restrict these applications, thus there is a need of a method to produce HR depth images from the available LR depth images. This paper addresses this issue using DWT method. This paper also contributes to the compilation of the existing methods for depth image super-resolution with their advantages and disadvantages, along with a proposed method to super-resolve depth image using DWT. Haar basis for DWT has been used as it has an intrinsic relationship with super-resolution (SR) for retaining the edges. The proposed method has been tested on Middlebury and Tsukuba dataset and compared with the conventional interpolation methods using peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance metrics. © Springer Science+Business Media Singapore 2017.Item Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal(Elsevier Ltd, 2018) Madhusudana, C.K.; Kumar, K.; Narendranath, S.The monitoring of machining process can improve the quality of product and economy of production. The monitoring system helps to recognize and monitor the surface roughness, dimensional tolerance and tool condition. In this way, the condition monitoring system provides precise dimensional products, high productivity and enhanced machine tool life. This paper presents the classification of healthy and faulty conditions of the face milling tool using Decision tree (J48 algorithm) technique through machine learning approach. The sound signals of the face milling tool under healthy and faulty conditions are acquired. A set of discrete wavelet features are extracted from the sound signals using discrete wavelet transform (DWT) method. Decision tree technique is used to select prominent features out of all extracted features. The selected features are fed to the same algorithm for classification. Output of the algorithm is used to study and categorize the tool conditions. The decision tree model has provided a good classification accuracy of about 81% for the given sound signals and can be considered for fault diagnosis/condition monitoring. From the experimental results, it is suggested that the proposed method which comprises of decision tree and DWT techniques with sound signals can be recommended for the applications of fault diagnosis of the face milling tool. © 2017 Elsevier Ltd. All rights reserved.Item Application of Wavelet Packet Transform for Detecting Bearing Issues(Springer Science and Business Media Deutschland GmbH, 2024) Bhaumik, D.; Bhaumik, D.Vibration condition monitoring analysis is one of the important methods for detecting defects in motors. The vibration data related to the outer race fault of an induction motor (IM) is collected from public domain. The main motive is to observe and characterize outer race defect. Different wavelet transform is mainly used. Absolute energy of the defective condition is compared with the normal state of the motor by the topology of discrete wavelet-transform (DWT). The proper nodes in wavelet packet transform (WPT) can be determined by the best basis selection (BBS) method. The absolute energy of selected nodes is further compared with the normal condition for detecting fault. The sub-band or the node corresponding to the DWT and the WPT analysis is mainly utilized for identifying the proper frequency components in characterizing the defect severity. In the case of DWT-based analysis, the sub-band D4 (1.5 kHz to 3 kHz) showed a positive energy trend with an increase in defect severity. In WPT, nodes 33 (1.5 kHz to 2.25 kHz) and 34 (2.25 kHz to 3 kHz) showed a positive trend with increased defect severity. Although the change in energy in node 33 is more dominant than in node 34, it can be said that the frequency range corresponding to node 33 (1.5 kHz to 2.25 kHz) can characterize the outer race defect severities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.