Topic
Channel (digital image)
About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.
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07 Jul 2008
TL;DR: This work proposes to extract pixel-based evolutions from SITS data by using two different symbolic techniques based on data mining techniques that aim at extracting frequent sequential patterns.
Abstract: Nowadays, there is a growing need for processing huge volumes of observation data due to the increase in size, in resolution, in spectral channel number and in acquisition frequency of remote sensing images. When data is gathered over time for a same geographical zone, this data is said to be a Satellite Image Time Series (SITS). The informational content of SITS is rich because the observed scene is described both in time and in space. In order to exhibit potential interesting spatio-temporal patterns, we propose to extract pixel-based evolutions from SITS data by using two different symbolic techniques. The first one is based on data mining techniques that aim at extracting frequent sequential patterns (e.g.,). The second one relies on the use of tries (e.g.,) for classifying pixels according to their evolution in time. Encouraging experiments on a SPOT SITS are detailed.
21 citations
TL;DR: This paper presents a novel approach for OD localization and segmentation which is fast as well as robust and much higher than the existing state-of-the-art methods.
Abstract: Automatic optic disc (OD) localization and segmentation is not a simple process as the OD appearance and size may significantly vary from person to person. This paper presents a novel approach for OD localization and segmentation which is fast as well as robust. In the proposed method, the image is first enhanced by de-hazing and then cropped around the OD region. The cropped image is converted to HSV domain and then V channel is used for OD detection. The vessels are extracted from the Green channel in the cropped region by multi-scale line detector and then removed by the Laplace Transform. Local adaptive thresholding and region growing are applied for binarization. Furthermore, two region properties, eccentricity, and area are then used to detect the true OD region. Finally, ellipse fitting is used to fill the region. Several datasets are used for testing the proposed method. Test results show that the accuracy and sensitivity of the proposed method are much higher than the existing state-of-the-art methods.
21 citations
01 Jan 2013
TL;DR: The methodology was evaluated on 1200 fundus images from the publicly-available MESSIDOR database, 229 of which present signs of macular edema, and results outperform the re- viewed methodologies available in literature.
Abstract: A methodology for locating the optic disc (OD) in digital retinal images is presented in this paper. Input images are intensity images (I channel of the HSI color space), resized to have a retinal diameter of 300 pixels. A shade-correction method for homogenizing the background, as well as, a set of morphological opening and clos- ing operations for enhancing bright structures, are applied to nd a pixel within OD. The methodology was evaluated on 1200 fundus images from the publicly-available MESSIDOR database, 229 of which present signs of macular edema. In 1190 of these images the distance between the methodology-provided pixel and actual OD center position remained below one standard optic disc radius. These results outperform the re- viewed methodologies available in literature that were tested on this same database.
21 citations
TL;DR: Wang et al. as discussed by the authors proposed a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations, which can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results.
Abstract: In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.
21 citations
TL;DR: This paper proposes an autoencoders based deep learning model for image denoising that achieves higher result compared to the conventional models for PSNR.
21 citations