2,879 research outputs found
Map online system using internet-based image catalogue
Digital maps carry along its geodata information such as coordinate that is important in one particular topographic and thematic map. These geodatas are meaningful especially in military field. Since the maps carry along this information, its makes the size of the images is too big. The bigger size, the bigger storage is required to allocate the image file. It also can cause longer loading time. These conditions make it did not suitable to be applied in image catalogue approach via internet environment. With compression techniques, the image size can be reduced and the quality of the image is still guaranteed without much changes. This report is paying attention to one of the image compression technique using wavelet technology. Wavelet technology is much batter than any other image compression technique nowadays. As a result, the compressed images applied to a system called Map Online that used Internet-based Image Catalogue approach. This system allowed user to buy map online. User also can download the maps that had been bought besides using the searching the map. Map searching is based on several meaningful keywords. As a result, this system is expected to be used by Jabatan Ukur dan Pemetaan Malaysia (JUPEM) in order to make the organization vision is implemented
Model for Estimation of Bounds in Digital Coding of Seabed Images
This paper proposes the novel model for estimation of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density function of the quantizer input signal, SQNR gains are calculated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution
Auxiliary Guided Autoregressive Variational Autoencoders
Generative modeling of high-dimensional data is a key problem in machine
learning. Successful approaches include latent variable models and
autoregressive models. The complementary strengths of these approaches, to
model global and local image statistics respectively, suggest hybrid models
that encode global image structure into latent variables while autoregressively
modeling low level detail. Previous approaches to such hybrid models restrict
the capacity of the autoregressive decoder to prevent degenerate models that
ignore the latent variables and only rely on autoregressive modeling. Our
contribution is a training procedure relying on an auxiliary loss function that
controls which information is captured by the latent variables and what is left
to the autoregressive decoder. Our approach can leverage arbitrarily powerful
autoregressive decoders, achieves state-of-the art quantitative performance
among models with latent variables, and generates qualitatively convincing
samples.Comment: Published as a conference paper at ECML-PKDD 201
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