Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
The JPEG committee (formally, ISO SC29 WG1) is currently standardizing a lightweight mezzanine codec for video over IP transport under the name JPEG XS. A particular challenging design constraint of this codec is multi-generation robustness, that is the necessity to minimize the error built-up under multiple re-compression cycles. In this paper, we discuss the sources of such errors, how they are...
This paper develops a general framework of image retrieval, named A3, by introducing an auxiliary set of samples (object references), each of which is annotated with semantic attributes (tags). Given a query image (without tags), we first map it into the references by a non-convex sparse coding formulation, which jointly optimizes appearance reconstruction of the query and semantics consistency among...
We use a quantum annealing D-Wave 2X (1,152-qubit) computer to generate sparse representations of Canny-filtered, center-cropped 30x30 CIFAR-10 images. Each binary neuron (qubit) represents a feature kernel obtained initially by imprinting on a randomly chosen 5x5 image patch and then adapted via an off-line Hebbian learning protocol using the sparse solutions generated by the D-Wave. When using binary...
The deep learning neural network is a recent development that has become the subject of research in the computer vision and remote sensing disciplines. Super resolution (SR) images can be obtained using deep neural network methods that achieve a higher performance than all previous traditional methods. Here, in this study, the objective is to describe existing deep learning methods for SR satellite...
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme...
Convolutional sparse coding (CSC) plays an essential role in many computer vision applications ranging from image compression to deep learning. In this work, we spot the light on a new application where CSC can effectively serve, namely line drawing analysis. The process of drawing a line drawing can be approximated as the sparse spatial localization of a number of typical basic strokes, which in...
Virtual view synthesis is a key component of multi-view imaging systems that enable visual immersion environments for emerging applications, e.g., virtual reality and 360-degree video. Using a small collection of captured reference view-points, this technique reconstructs any view of a remote scene of interest navigated by a user, to enhance the perceived immersion experience. We carry out a convexity...
Existing methods for layer-based backward compatible high dynamic range (HDR) image and video coding mostly focus on the rate-distortion optimization of base layer while neglecting the encoding of the residue signal in the enhancement layer. Although some recent studies handle residue coding by designing function based fixed global mapping curves for 8-bit conversion and exploiting standard codecs...
Imaging with random sensing functions may afford novel measurement geometries that circumvent constraints of conventional point-by-point imaging architectures. Here we demonstrate imaging of axial reflectivity profiles using random temporal-spatial encoding created by modal interference in a multimode fiber.
Block-wise super-resolution methods, and in particular sparse representation based approaches, often focus on spatial upsampling of still images. Applying such models to videos is an extremely time consuming process due to the expensive sparse coding process for every block of each frame, and the conventional exhaustive overlapping blocks processing for reducing the blocking artifacts. In this paper,...
Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially,...
Compressed sensing (CS) has drawn many interest in the field ultrasound (US) image recovery. It has demonstrated promising results in the recovery of radio-frequency element raw-data [Liebgott et. al. ULTRAS13, Besson et. al. SPARS17]. The objective of such approaches is to recover the raw-data from undersampled random measurements. It is achieved by means of convex optimization or greedy methods...
Recent research in computed tomographic imaging has focused on developing techniques that enable reduction of the X-ray radiation dose without loss of quality of the reconstructed images or volumes. While penalized weighted-least squares (PWLS) approaches have been popular for CT image reconstruction, their performance degrades for very low dose levels due to the inaccuracy of the underlying WLS statistical...
In this paper, a method for reducing coding artifacts introduced by lossy image compression is proposed. The method is similar to sample adaptive offset (SAO) which is adopted in the H.265/HEVC video coding standard as one of in-loop filtering tools. In the SAO, samples of the reconstructed image are classified into several categories based on some simple algorithms, and an optimum offset value is...
Recently, RGB-D cameras have been widely used to perform 6-D pose estimation of target objects for robotic manipulation. Such applications require accuracy shape measurements for 3-D modeling of the objects. In this work, we develop an RGB-D camera based on the structured light (SL) techniques with gray-code encoding and decoding. The intrinsic and extrinsic parameters of the camera system are determined...
Learning based hashing has become increasingly popular because of its high efficiency in handling the large scale image retrieval. Preserving the pairwise similarities of data points in the Hamming space is critical in state-of-the-art hashing techniques. However, most previous methods ignore to capture the local geometric structure residing on original data, which is essential for similarity search...
Lenslet images that record both spatial and angular light radiance in a super high definition with distinct macropixel structures desire efficient compression methods for promoting the applications of handheld plenoptic cameras urgently. In this paper, a lenslet image compression method is proposed. First, a reversible image reshaping and adaptive interpolation is proposed to align the macropixel...
The proposed framework, called Steered Mixture-of-Experts (SMoE), enables a multitude of processing tasks on light fields using a single unified Bayesian model. The underlying assumption is that light field rays are instantiations of a non-linear or non-stationary random process that can be modeled by piecewise stationary processes in the spatial domain. As such, it is modeled as a space-continuous...
Compression of multispectal images is of great importance in an environment where resources such as computational power and memory are scarce. To that end, we propose a new extremely low-complexity encoding approach for compression of multispectral images, that shifts the complexity to the decoding. Our method combines principles from compressed sensing and distributed source coding. Specifically,...
It is necessary to transmit the image from the ammunition platform to the terrestrial system in real time when the TV guided weapon strikes the ground target. For the large amount of HDTV images, due to the limited bandwidth of signal transmission, real-time transmission can't be realized. As an emerging theory, compressive sensing provides the possibility of real-time transmission of HDTV video signals...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.