ELCVIA Electronic Letters on Computer Vision and Image Analysis https://elcvia.cvc.uab.es/ Electronic Journal on Computer Vision and Image Analysis CVC Press en-US ELCVIA Electronic Letters on Computer Vision and Image Analysis 1577-5097 Authors who publish with this journal agree to the following terms:<br /><ol type="a"><li>Authors retain copyright.</li><li>The texts published in this journal are – unless indicated otherwise – covered by the Creative Commons Spain <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">Attribution-NonComercial-NoDerivatives 4.0</a> licence. You may copy, distribute, transmit and adapt the work, provided you attribute it (authorship, journal name, publisher) in the manner specified by the author(s) or licensor(s). The full text of the licence can be consulted here: <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">http://creativecommons.org/licenses/by-nc-nd/4.0</a>.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li></ol> Saliency-Based Image Retrieval as a Refinement to Content-Based Image Retrieval https://elcvia.cvc.uab.es/article/view/v20-n1-Alazawi <p class="AbstractBodytext">Searching for an image in a database is important in different applications; hence, many algorithms have been proposed to identify the contents of the image. In some applications, but not all, identifying the content of the image as a whole can offer good results. Searching for an object inside the image is more important in most applications than identifying the image as a whole. Therefore, studies focused on segmenting the image into small sub-images and identified their contents. In view of the concepts of human attention, various literature defined saliency as a computer representation of it, where different algorithms were developed to extract the salient regions. These salient regions, which are the regions that attract human attention, are used to identify the most important regions that contain important objects in the image. In this paper, we introduce a new algorithm that utilises the saliency principles to identify the contents of an image and search for similar objects in the images stored in a database. We also demonstrate that the use of salient objects produces better and more accurate results in the image retrieval process. A new retrieval algorithm is therefore presented here, focused on identifying the objects extracted from the salient regions. To assess the efficiency of the proposed algorithm, a new evaluation method is also proposed which considers the order of the retrieved image in assessing the efficiency of the algorithm.</p> Mohammad A. N. Al-Azawi Copyright (c) 2021 Mohammad A. N. Al-Azawi https://creativecommons.org/licenses/by-nc-nd/4.0 2021-01-13 2021-01-13 20 1 1 15 10.5565/rev/elcvia.1325 A comparison of an RGB-D cameras performance and a stereo camera in relation to object recognition and spatial position determination https://elcvia.cvc.uab.es/article/view/v20-n1-Rodriguez <p>Results of using an RGB-D camera (Kinect sensor) and a stereo camera, separately, in order to determine the 3D real position of characteristic points of a predetermined object in a scene are presented. KAZE algorithm was used to make the recognition, that algorithm exploits the nonlinear scale space through nonlinear diffusion filtering; 3D coordinates of the centroid of a predetermined object were calculated employing the camera calibration information and the depth parameter provided by a Kinect sensor and a stereo camera. Experimental results show it is possible to get the required coordinates with both cameras in order to locate a robot, although a balance in the distance where the sensor is placed must be guaranteed: no fewer than 0.8 m from the object to guarantee the real depth information, it is due to Kinect operating range; 0.5 m to stereo camera, but it must not be 1 m away to have a suitable rate of object recognition, besides, Kinect sensor has more precision with distance measures regarding a stereo camera.</p> Julian Severiano Rodriguez Copyright (c) 2021 Julian Severiano Rodriguez https://creativecommons.org/licenses/by-nc-nd/4.0 2021-01-26 2021-01-26 20 1 16 27 10.5565/rev/elcvia.1238 Investigation of Solar Flare Classification to Identify Optimal Performance https://elcvia.cvc.uab.es/article/view/v20-n1-Kakde When an intense brightness for a small amount of time is seen in the sun, then we can say that a solar flare emerged. As solar flares are made up of high energy photons and particles, thus causing the production of high electric fields and currents and therefore results in the disruption in space-borne or ground-based technological system. It also becomes a challenging task to extract its important features for prediction. Convolutional Neural Networks have gain a significant amount of popularity in the classification and localization tasks. This paper has given stress on the classification of the solar flares emerged on different years by stacking different convolutional layers followed by max pooling layers. From the reference of Alexnet, the pooling layer employed in this paper is the overlapping pooling. Also two different activation functions that are ELU and CReLU have been used to investigate how many number of convolutional layers with a particular activation function provides with the best results on this dataset as the size of the dataset in this domain is always small. The proposed investigation can be further used in a novel solar prediction systems. Aditya Kakde Durgansh Sharma Bhavana Kaushik Nitin Arora Copyright (c) 2021 Aditya Kakde, Durgansh Sharma, Bhavana Kaushik, Nitin Arora https://creativecommons.org/licenses/by-nc-nd/4.0 2021-01-19 2021-01-19 20 1 28 41 10.5565/rev/elcvia.1274 Adaptive Window Selection for Non-uniform Lighting Image Thresholding https://elcvia.cvc.uab.es/article/view/v20-n1-Pattnaik Selection of appropriate size of windows or subimages is the most important step for thresholding images with non-uniform lighting. In this paper, a novel criteria function is developed to partition images into different size of sub images appropriate for thresholding. After the partitioning, each subimage is segmented by Otsu’s thresholding approaches. The performance of the proposed method is validated on benchmark test images with different degree of uneven lighting. Based on the qualitative and quantitative measures, the proposed method is fully automatic, fast and efficient in comparison to many landmark approaches. Tapaswini Pattnaik Priyadarshi Kanungo Copyright (c) 2021 Tapaswini Pattnaik, Priyadarshi Kanungo https://creativecommons.org/licenses/by-nc-nd/4.0 2021-01-19 2021-01-19 20 1 42 54 10.5565/rev/elcvia.1301 Recognition of Devanagari Scene Text Using Autoencoder CNN https://elcvia.cvc.uab.es/article/view/v20-n1-Sannakki <p class="AbstractBodytext">Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique.</p> Sankirti Sandeep Shiravale Jayadevan R Sanjeev S Sannakki Copyright (c) 2021 Sankirti Sandeep Shiravale, Jayadevan R, Sanjeev S Sannakki https://creativecommons.org/licenses/by-nc-nd/4.0 2021-02-02 2021-02-02 20 1 55 69 10.5565/rev/elcvia.1344 Edge detection algorithm for omnidirectional images, based on superposition laws on Blach’s sphere and quantum entropy https://elcvia.cvc.uab.es/article/view/v20-n1-Ezzai <p><span class="fontstyle0">This paper presents an edge detection algorithm for omnidirectional images based on superposition law on Bloch’s sphere and quantum local entropy. Omnidirectional vision system has become an essential tool in computer vision, duo to its large field of view. However, classical image processing algorithms are not suitable to be applied directly in this type of images without taking into account the spatial information around each pixel. To show the performance of the proposed method, a set of experimentation was done on synthetic and real images devoted to agriculture applications. Later, Fram &amp; Deutsh criterion has been adopted to evaluate its performance against three algorithms proposed on the literature and developed for omnidirectional images. The results show a good performance of the proposed method in term of edge quality, edge community and sensibility to noise.</span></p> Ayoub Ezzaki Dirar Benkhedra Mohamed El Ansari Lhoussaine Masmoudi Copyright (c) 2021 Ayoub Ezzaki, Dirar Benkhedra, Mohamed El Ansari, Lhoussaine Masmoudi https://creativecommons.org/licenses/by-nc-nd/4.0 2021-02-25 2021-02-25 20 1 70 83 10.5565/rev/elcvia.1338