BACKGROUND The speed and precision with which objects are moved by hand or hand-tool interaction under image guidance depend on a specific type of visual and spatial sensorimotor learning. Novices have to learn to optimally control what their hands are doing in a real-world environment while looking at an image representation of the scene on a video monitor. Previous research has shown slower t...

The explosive growth of image data on the web leads to the research and development of visual information retrieval systems. However, these visual contents do not allow user to query images using semantic meanings. To resolve this problem, automatically annotating images with a list of semantic concepts is an essential and beneficial task. In this paper, we describe our approach for annotating ...

 Fast Single Image Super-resolution by Self-trained Filtering Dalong Li, Steven Simske HP Laboratories HPL-2011-94 Super-resolution; PSNR; filter; image restoration; image enhancement This paper introduces an algorithm to super-resolve an image based on a self-training filter (STF). As in other methods, we first increase the resolution by interpolation. The interpolated image has higher resolu...

Estimating the pose (position and orientation) of a camera with respect to a xed 3D coor-dinate system from an image of the surrounding environment captured in that pose has manyapplications. In our work we try to establish 2D-to-3D point correspondences between the im-age and the environment in order to apply Perspective-n-Point (PnP) algorithm to compute thepose. Each 2D-to-3D...

Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervised learning approaches, in this paper we propose learning intrinsic image decomposition by explaining the input image. Our model, the Rendered Intrinsics Network (RIN), joins together an image decomposition pipeline, ...

For the task of assigning labels to an image to summarize its contents, many early attempts use segment-level information and try to determine which parts of the images correspond to which labels. Best performing methods use global image similarity and nearest neighbor techniques to transfer labels from training images to test images. However, global methods cannot localize the labels in the im...

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular ...

background evaluation of the accuracy and performance of sonography units needs tissue-mimicking phantoms. these phantoms play an important role by simulating soft tissues, obviating the need to experiment on humans or animals. objectives to present a simple sonographic phantom for quality control and training purposes. materials and methods the presented phantom consists of a two-part plexigla...

Image inpainting consists in recovering missing parts of an image. Since a color image is a D array, tensor completion methods are applicable to this problem. Tensor completion approach based on trace norm minimization can be useful when the fraction of missing pixels is not large, with the advantage that the training set is not required. Here, we demonstrate that the dictionary for sparse repr...