Celine Kavida - Academia.edu (original) (raw)

Papers by Celine Kavida

International Journal of Biomedical Engineering and Technology

Computer Systems Science and Engineering

Digitization has created an abundance of new information sources by altering how pictures are cap... more Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes). This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively. Thus, retrieval becomes straightforward and rapid. This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost. We focus on the schema design on a non-clustered index solution, especially cover queries. This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing. Finally, we include non-key columns in addition to the key columns. Experiments on two image data sets 'with and without' filtered indexing show low query cost. We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing. The results show that retrieval by using deep root indexing is simple and fast.

Multidimensional Systems and Signal Processing

Detecting and classifying the target objects in the underwater videos is the primary and essentia... more Detecting and classifying the target objects in the underwater videos is the primary and essential operation in this modern era. The present works proposed the pattern extraction, segmentation and classification techniques for target object detection in underwater images. These techniques were found to be lacking with the following limitations, such as inefficient computation, inaccurate results, and increased cost-efficiency. Thus, this work targets to introduce a new pattern extraction based classification system for processing underwater images. As the first and foremost thing, the input underwater picture is preprocessed for eliminating the noise by applying the Laplacian Bellman filtering technique. After that, the histogram equalization is utilized to enhance the quality of the source picture. Once the image is noise-free, the patterns are extracted from it with the help of the likelihood gradient pattern technique. Subsequently, the label formation and blob extraction processes are performed in an orderly manner to track the target object accurately. In this work, the novelty is seen implemented in both the preprocessing and the feature analysis stages by developing a novel technique. The significant advantages of this work are: yielding the improved image quality, by being the efficient texture pattern extractor, and they also do not require any additional information and adjustments. While under simulation, the fulfilment attained in the proposed techniques and the appropriate literature methodologies are evaluated and validated with the performance measures like accuracy, sensitivity, specificity, average time, precision, F1-measure, and the filtering features like entropy, contrast, and the correlation.

Journal of Ambient Intelligence and Humanized Computing

The use of remote sensing and geographic information system (GIS) technologies grow drastically i... more The use of remote sensing and geographic information system (GIS) technologies grow drastically in recent years by ecologists around the globe. At present, using sophisticated sensors, there is a massive challenge in handling the high dense remotely captured information with spatial, spectral, temporal, and radiometric resolutions. This article addresses how to handle such large volume remotely sensed data using R programming with the aid of RStudio. We aim broadly two categories, such as image preprocessing and classification techniques on remotely sensed data. Image preprocessing methods such as false-color composite, pan-sharpening, single event upset error mitigation, and a view on the hyper spectral image. The other category comes with a focus on landscape spatial features analysis and unsupervised classification to analyze vegetation land. The CLARA (Clustering LARge Application) algorithm is used in this study which exploits k-medoids approaches for the unsupervised classification. Also, for results comparison, different vegetation indices such as normalized difference water index (NDWI), modified NDWI (MNDWI), and soil adjusted vegetation index are used for vegetation (land) analysis. Also, for the unsupervised classification, the Silhouette index is used to compare the clustering algorithms.

Soft Computing

In remote sensing, land cover classification of vegetation and water area from satellite image pl... more In remote sensing, land cover classification of vegetation and water area from satellite image play a vital role for rural and urban planning and development. Existing algorithms of land cover classification require more sample image datasets for training. For existing algorithms, land cover classification of vegetation and water area is a challenging task because of mixed pixel and geometric distortion over boundary and curvature region. Mixed pixel affects the precise classification and measurement of land cover. Geometric distortion arises due to frame of isotropic and angular selectivity during image acquisition and affects the contour of land cover. In this paper, the proposed transverse dyadic wavelet transform (TDyWT) enhances and classifies vegetation and water area in land cover from LANDSAT image without training datasets. The proposed TDyWT uses Haar wavelet for decomposition and Burt 5 × 7 wavelet for reconstruction. The TDyWT enhances the contour, curvature, and boundary of vegetation and water area in LANDSAT image due to reversible and lifting properties of wavelet. TDyWT removes geometric distortion and spatial scale error of mixed pixel. In traditional land surveying spatial scale error reduction eliminates through total station and error modeling techniques. From the results, the proposed TDyWT algorithm classifies the area of subclass of vegetation and water with the 95% of accuracy with respect to ground truth survey methods.

Cluster Computing

In the proposed work introduced a pose-invariant face recognition process for the videos so as to... more In the proposed work introduced a pose-invariant face recognition process for the videos so as todiminish the calculation intricacy of the traditional technique. The intended approach includes four phases, namely (i) preprocessing, (ii) object detection, (iii) feature extraction along with (iv) dimensionality reduction. The initial phase is segmenting the database video clips into the frames where the preprocessing is done using an adaptive median filtering such that to eradicate the noise. The subsequent phase is detecting the preprocessed image through the viola–jones technique. With this technique, the mouth, left eye, face, nose, right eye were identified. After that, the training image attributes like the texture, edge, and wavelet are extorted with the assistance of dissimilar feature extortion approaches. The extorted attributes will then be inputted to the PCA approach. The similar process is replicated for query images. At last, perform the similarity measurement amid the trained database images and query images such that to attain the distinguished image. In addition, the genuine general performance assesses of the projected technique is assessed with that of the traditional ICA and also LDA techniques.

International Journal of Biomedical Engineering and Technology

Computer Systems Science and Engineering

Digitization has created an abundance of new information sources by altering how pictures are cap... more Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes). This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively. Thus, retrieval becomes straightforward and rapid. This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost. We focus on the schema design on a non-clustered index solution, especially cover queries. This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing. Finally, we include non-key columns in addition to the key columns. Experiments on two image data sets 'with and without' filtered indexing show low query cost. We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing. The results show that retrieval by using deep root indexing is simple and fast.

Multidimensional Systems and Signal Processing

Detecting and classifying the target objects in the underwater videos is the primary and essentia... more Detecting and classifying the target objects in the underwater videos is the primary and essential operation in this modern era. The present works proposed the pattern extraction, segmentation and classification techniques for target object detection in underwater images. These techniques were found to be lacking with the following limitations, such as inefficient computation, inaccurate results, and increased cost-efficiency. Thus, this work targets to introduce a new pattern extraction based classification system for processing underwater images. As the first and foremost thing, the input underwater picture is preprocessed for eliminating the noise by applying the Laplacian Bellman filtering technique. After that, the histogram equalization is utilized to enhance the quality of the source picture. Once the image is noise-free, the patterns are extracted from it with the help of the likelihood gradient pattern technique. Subsequently, the label formation and blob extraction processes are performed in an orderly manner to track the target object accurately. In this work, the novelty is seen implemented in both the preprocessing and the feature analysis stages by developing a novel technique. The significant advantages of this work are: yielding the improved image quality, by being the efficient texture pattern extractor, and they also do not require any additional information and adjustments. While under simulation, the fulfilment attained in the proposed techniques and the appropriate literature methodologies are evaluated and validated with the performance measures like accuracy, sensitivity, specificity, average time, precision, F1-measure, and the filtering features like entropy, contrast, and the correlation.

Journal of Ambient Intelligence and Humanized Computing

The use of remote sensing and geographic information system (GIS) technologies grow drastically i... more The use of remote sensing and geographic information system (GIS) technologies grow drastically in recent years by ecologists around the globe. At present, using sophisticated sensors, there is a massive challenge in handling the high dense remotely captured information with spatial, spectral, temporal, and radiometric resolutions. This article addresses how to handle such large volume remotely sensed data using R programming with the aid of RStudio. We aim broadly two categories, such as image preprocessing and classification techniques on remotely sensed data. Image preprocessing methods such as false-color composite, pan-sharpening, single event upset error mitigation, and a view on the hyper spectral image. The other category comes with a focus on landscape spatial features analysis and unsupervised classification to analyze vegetation land. The CLARA (Clustering LARge Application) algorithm is used in this study which exploits k-medoids approaches for the unsupervised classification. Also, for results comparison, different vegetation indices such as normalized difference water index (NDWI), modified NDWI (MNDWI), and soil adjusted vegetation index are used for vegetation (land) analysis. Also, for the unsupervised classification, the Silhouette index is used to compare the clustering algorithms.

Soft Computing

In remote sensing, land cover classification of vegetation and water area from satellite image pl... more In remote sensing, land cover classification of vegetation and water area from satellite image play a vital role for rural and urban planning and development. Existing algorithms of land cover classification require more sample image datasets for training. For existing algorithms, land cover classification of vegetation and water area is a challenging task because of mixed pixel and geometric distortion over boundary and curvature region. Mixed pixel affects the precise classification and measurement of land cover. Geometric distortion arises due to frame of isotropic and angular selectivity during image acquisition and affects the contour of land cover. In this paper, the proposed transverse dyadic wavelet transform (TDyWT) enhances and classifies vegetation and water area in land cover from LANDSAT image without training datasets. The proposed TDyWT uses Haar wavelet for decomposition and Burt 5 × 7 wavelet for reconstruction. The TDyWT enhances the contour, curvature, and boundary of vegetation and water area in LANDSAT image due to reversible and lifting properties of wavelet. TDyWT removes geometric distortion and spatial scale error of mixed pixel. In traditional land surveying spatial scale error reduction eliminates through total station and error modeling techniques. From the results, the proposed TDyWT algorithm classifies the area of subclass of vegetation and water with the 95% of accuracy with respect to ground truth survey methods.

Cluster Computing

In the proposed work introduced a pose-invariant face recognition process for the videos so as to... more In the proposed work introduced a pose-invariant face recognition process for the videos so as todiminish the calculation intricacy of the traditional technique. The intended approach includes four phases, namely (i) preprocessing, (ii) object detection, (iii) feature extraction along with (iv) dimensionality reduction. The initial phase is segmenting the database video clips into the frames where the preprocessing is done using an adaptive median filtering such that to eradicate the noise. The subsequent phase is detecting the preprocessed image through the viola–jones technique. With this technique, the mouth, left eye, face, nose, right eye were identified. After that, the training image attributes like the texture, edge, and wavelet are extorted with the assistance of dissimilar feature extortion approaches. The extorted attributes will then be inputted to the PCA approach. The similar process is replicated for query images. At last, perform the similarity measurement amid the trained database images and query images such that to attain the distinguished image. In addition, the genuine general performance assesses of the projected technique is assessed with that of the traditional ICA and also LDA techniques.