Advances in Image and Video Processing <p>Advances in Image and Video Processing is peer-reviewed open access online journal that provides a medium of the rapid publication of original research papers, review articles, book reviews and short communications covering all aspects of image processing and computer vision from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation.</p> ScholarPublishing on behalf of Services for Science and Education, United Kingdom en-US Advances in Image and Video Processing 2054-7412 <p>Authors wishing to include figures, tables, or text passages that have already been published elsewhere are required to obtain permission from the copyright owner(s) for both the print and online format and to include evidence that such permission has been granted when submitting their papers. Any material received without such evidence will be assumed to originate from the authors.</p><p>All authors of manuscripts accepted for publication in the journal <em>Transactions on Networks and Communications </em>are required to license the <em>Scholar Publishing</em> to publish the manuscript. Each author should sign one of the following forms, as appropriate:</p><p><strong>License to publish</strong>; to be used by most authors. This grants the publisher a license of copyright. Download forms (MS Word formats) - (<a title="SSE Copyright Manuscript" href="/Repository/Forms/SSECopyright-Manuscript.docx" target="_blank">doc</a>)</p><p><strong>Publication agreement</strong> — Crown copyright; to be used by authors who are public servants in a Commonwealth country, such as Canada, U.K., Australia. Download forms (Adobe or MS Word formats) - (<a title="SSE Copyright Manuscript Crown" href="/Repository/Forms/SSECopyright-Manuscript-CrownAuthor.docx" target="_blank">doc</a>)</p><p><strong>License to publish</strong> — U.S. official; to be used by authors who are officials of the U.S. government. Download forms (Adobe or MS Word formats) – (<a title="SSECopyright Manuscript US Official" href="/Repository/Forms/SSECopyright-Manuscript-US-Official.docx" target="_blank">doc</a>)</p><p>The preferred method to submit a completed, signed copyright form is to upload it within the task assigned to you in the Manuscript submission system, after the submission of your manuscript. Alternatively, you can submit it by email <a href=" : International Journal of Medical Imaging and Graphics!"></a></p> A MASK-RCNN Based Approach Using Scale Invariant Feature Transform Key points for Object Detection from Uniform Background Scene <p class="western" align="justify"><span style="color: #00000a;"><span style="font-family: Times New Roman, serif;"><span style="font-size: small;">Object identification using deep learning in known environment gives a new dimension to the research area of computer vision based automation system. As it uses supervised learning technique using Convolution Neural Network (RCNN) it helps automation software tools and machines to detect and identify objects using vision based systems. One of RCNN technique known as Mask-RCNN has been applied in this proposed design and this paper presents a novel approach to object detection problem using Big Data storage for large set of features based data. Earlier work Faster Region-based CNN has led to the development of a state-of-the-art object detector termed as Mask R-CNN. Some samples of solid material objects used in refractory industry have been taken as input images. In our experiment the SIFT based features have been implemented and trained using filter and convolution operation. In addition to improved accuracy, pixel-level annotation (annotating bounding boxes is approximately an order of magnitude which is quicker to perform). The model is retrained to perform the detection of four types of metal objects, with the entire process of annotation and training for the new model per solid block. A key benefit of feature based Mask-RCNN approach is high precision (~94%) in classification and minimized feature points with SIFT key points.</span></span></span></p> RADHAMADHAB DALAI Kishore Kumar Senapati Copyright (c) 2019 Radhamadhab Dalai, Kishore Kumar Senapati 2019-11-08 2019-11-08 7 5 01 08 10.14738/aivp.75.6946 Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques <p>This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.</p> Oladimeji Adeyemi Martins Irhebhude Adeola Kolawole Copyright (c) 2019 Martins E. Irhebhude, Oladimeji A. Adeyemi, Adeola Kolawole 2019-11-08 2019-11-08 7 5 30 42 10.14738/aivp.75.7205 Qualitative SAR Dark Spot Analysis for Oil Anomaly Characterization <p>The characterization of the morphology of a dark spot event in a SAR image is crucial to determining the nature, fate and classification of a potential oil on water anomaly. Hence the need to adequately analyze SAR backscatter values from an amplitude power image were darks spots have been identified and extracted. The dark spots are potential areas of oil on water events and it is imperative that they are understood sufficiently for the purpose of assigning the level of confidence in the processing of oil analysis, understanding the type of oil on water event, classifying the extent of weathering impacting the detected anomaly and relating the oil anomaly to a potential source. In order to generate high quality datasets for anomaly characterization, it is imperative to transform satellite image amplitude data into power values in units of dB and represented on a logarithmic scale. This new power dataset is corrected radiometrically and requisite speckle to noise filter applied for data cleaning and noise suppression. Oil anomaly or darks spot extraction, representative of a potential oil on water is event which is the primary objective of the pre-processing is done. Gamma enhancement was applied on the dark spots in order to characterize their radiometric, textural and geometric properties. This is predominantly for estimating the confidence level of each anomaly detected, and their properties which are indicative of the type, fate, age and physical processes relating to each anomaly.</p> Chituru Dike Obowu Tamunoene Kingdom Abam Sabastian Ngah Copyright (c) 2019 Chituru Dike Obowu, Tamunoene Kingdom Abam, Sabastian Ngah 2019-11-08 2019-11-08 7 5 22 29 10.14738/aivp.75.7240 Retrieval of Images using Combination of Features as Color, Color Moments and Hu Moments <p>In today’s digital era, several of the image retrieval systems focus on retrieving images using features from images themselves such as color, shape and textures and are referred as low-level features. In this proposed work, the features like color with HSV color space, color moments and Hu moments are employed to retrieve similar images. Various experimentations were conducted on Wang’s database images to test the combination of features for higher performance using precision, recall, accuracy and f-score. The results obtained are compared with one another and also with existing works. The retrieval performance is found to be high for proposed system against existing works.</p> RAJKUMAR RAJ Dr. M V Sudhamani Copyright (c) 2019 R Rajkumar, Dr. M V Sudhamani 2019-11-08 2019-11-08 7 5 09 21 10.14738/aivp.75.7208