Conference Papers

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Now showing 1 - 9 of 9
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    Sub-pixel mineral mapping using EO-1 hyperion hyperspectral data
    (International Society for Photogrammetry and Remote Sensing, 2014) Kumar, C.; Shetty, A.; Raval, S.; Champatiray, P.K.; Sharma, R.
    This study describes the utility of Earth Observation (EO)-1 Hyperion data for sub-pixel mineral investigation using Mixture Tuned Target Constrained Interference Minimized Filter (MTTCIMF) algorithm in hostile mountainous terrain of Rajsamand district of Rajasthan, which hosts economic mineralization such as lead, zinc, and copper etc. The study encompasses pre-processing, data reduction, Pixel Purity Index (PPI) and endmember extraction from reflectance image of surface minerals such as illite, montmorillonite, phlogopite, dolomite and chlorite. These endmembers were then assessed with USGS mineral spectral library and lab spectra of rock samples collected from field for spectral inspection. Subsequently, MTTCIMF algorithm was implemented on processed image to obtain mineral distribution map of each detected mineral. A virtual verification method has been adopted to evaluate the classified image, which uses directly image information to evaluate the result and confirm the overall accuracy and kappa coefficient of 68% and 0.6 respectively. The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, the study demonstrates the feasibility of Hyperion data for sub-pixel mineral mapping using MTTCIMF algorithm with cost and time effective approach.
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    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.
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    What makes a video memorable?
    (Institute of Electrical and Electronics Engineers Inc., 2017) Kar, A.; Prashasthi, P.; Ghaturle, Y.; Vani, M.
    Humans are exposed to many pictures and videos on a daily basis, but they have this exceptional ability to remember the details, even though many of them look very similar. This Video Memorability (VM) is mainly due to distinguishable and a fine representation of the frames in human mind that people tend to remember. Videos have an abundance data contained in the frames which can be used for feature extraction purposes. Each feature from each frame has to be carefully considered to determine the intrinsic property of the video i.e. memorability. Using Convolutional Neural Network (CNN), we propose a solution to the problem of predicting VM, by estimating its memorability. A model has been developed to predict VM using algorithmically extracted features. Two types of features (i) semantic features (ii) visual features have been considered. The effectiveness of the model has been tested using publicly available image and video data. The results confirm that the CNN model can predict memorability with a acceptable performance. © 2017 IEEE.
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    Currency recognition system using image processing
    (Institute of Electrical and Electronics Engineers Inc., 2017) Abburu, V.; Gupta, S.; Rimitha, S.R.; Mulimani, M.; Koolagudi, S.G.
    In this paper, we propose a system for automated currency recognition using image processing techniques. The proposed method can be used for recognizing both the country or origin as well as the denomination or value of a given banknote. Only paper currencies have been considered. This method works by first identifying the country of origin using certain predefined areas of interest, and then extracting the denomination value using characteristics such as size, color, or text on the note, depending on how much the notes within the same country differ. We have considered 20 of the most traded currencies, as well as their denominations. Our system is able to accurately and quickly identify test notes. © 2017 IEEE.
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    On Human Identification Using Running Patterns: A Straightforward Approach
    (Springer Verlag service@springer.de, 2020) Anusha, R.; Jaidhar, C.D.
    Gait is a promising biometric for which various methods have been developed to recognize individuals by the pattern of their walking. Nevertheless, the possibility of identifying individuals by using their running video remains largely unexplored. This paper proposes a new and simple method that extends the feature based approach to recognize people by the way they run. In this work, 12 features were extracted from each image of a gait cycle. These are statistical, texture based and area based features. The Relief feature selection method is employed to select the most relevant features. These selected features are classified using k-NN (k-Nearest Neighbor) classifier. The experiments are carried out on KTH and Weizmann database. The obtained experimental results demonstrate the efficiency of the proposed method. © 2020, Springer Nature Switzerland AG.
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    Video surveillance for the crime detection using features
    (Springer, 2021) Chowdhary, A.; Rudra, B.
    This paper aims at extending the comparison between two images and locating the query image in the source image by matching the features in the videos by presenting a method for the recognition of a particular person or an object. The frames matching the feature (not feature its query) object in a given video will be the output. We describe a method to find unique feature points in an image or a frame using SIFT, i.e., scale-invariant feature transform method. SIFT is used for extracting distinctive feature points which are invariant to image scaling or rotation, presence of noise, changes in image lighting, etc. After the feature points are recognized in an image, the image is tracked for comparison with the feature points found in the frames. The feature points are compared using homography estimation search to find the required query image in the frame. In case the object is not present in the frame, then it will not present any output. © Springer Nature Singapore Pte Ltd 2021.
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    Classification of Medicinal Plants Using Machine Learning
    (Springer Science and Business Media Deutschland GmbH, 2022) Meshram, R.S.; Patil, N.
    Nowadays, peoples are not having information about the surrounding plants and their medicinal values. If some person wants to know about the medicinal plants, they have to contact the person who is having deep knowledge about the medicinal plants and its uses. In order to solve this problem we can use the current technology to give a tool which will help the common people to know more about the medicinal plants. For doing this we can use many machine learning techniques for classifying the medicinal plants with more accuracy. Different kind of medicinal plant species are available on the planet earth but classification of the Particular medicinal plant is very difficult without knowing about the plants first. The information about the medicinal plants is collected by the scientists and urban people. Generally this kind of knowledge is passed through generation to generation and sometimes there might be some changes in the information and its contents. So according to the current situation we can use the machine learning technology to make the tool which will be helpful to solve the medicinal plant classification problem. Machine learning model can easily classify the medicinal plants after the feature extraction and applying the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Attention-Based Bitemporal Image Deep Feature-Level Change Detection for High Resolution Imagery
    (Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Venkatesan, M.; Prabhavathy, P.
    To understand the intricacy of changes on the surface of the land, change detection is an important field in the area of remote sensing. Bitemporal remote sensing images are resourceful information to perform the analysis related to classification and change detection. Most of the architectures proposed for improving the performance of change detection in high resolution images pose a challenge due to composite texture features and finer image details. In this paper, we propose a change detection approach for bitemporal images using supervised learning. Firstly, extraction of the features is performed using a pretrained neural network. Then, the extracted features are provided to a (DSDEN) deep supervised-based difference evaluation network. Then, channel and spatial-based attention components are incorporated for fusing the difference image features with the deep features of raw images for the reconstruction of the final change map. The experimental evaluation on public “LEVIR-CD†dataset demonstrates the effectiveness and superiority over traditional methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Feature Selection and Ranking in EMG Analysis for Hand Movement Classification
    (Institute of Electrical and Electronics Engineers Inc., 2023) Chandrika, P.R.; Powar, O.S.; Chemmangat, K.
    Surface Electromyography has gained tremendous significance in the recent years due to its suitability and reliability in a wide range of applications like automatic prosthetic control, diagnosis of neuromuscular disorders, in robotics and many such fields. Considering such applications, identification of various muscular movements is necessary and hence, EMG pattern recognition is needed. This paper focusses on a generalised EMG pattern recognition of various hand movements. The data from Ninapro Database - 4 has been used for pattern recognition. The database has Surface Electromyogram (sEMG) data of 52 various hand movements. The data was subjected to pre-processing, feature extraction and classification. An average accuracy of 64.87% was obtained for a combination of seven features in the time (temporal) domain, using Linear Discriminant Analysis (LDA) as the classification model. The obtained classification accuracies are compared and discussed with respect to the state-of-the-art literature. © 2023 IEEE.