Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

MCL-3D: A Database for Stereoscopic Image Quality Assessment using 2D-Image-Plus-Depth Source

Abstract: A new stereoscopic image quality assessment database rendered using the 2D-image- plus-depth source, called MCL-3D, is described and the performance benchmarking of several known 2D and 3D image quality metrics using the MCL-3D database is presented in this work. Nine image-plus-depth sources are first selected, and a depth image- based rendering (DIBR) technique is used to render stereoscopic image pairs. Distortions applied to either the texture image or the depth image before stereoscopic image rendering include: Gaussian blur, additive white noise, down-sampling blur, JPEG and JPEG-2000 (JP2K) compression and transmission error. Furthermore, the distortion caused by imperfect rendering is also examined. The MCL-3D database contains 693 stereoscopic image pairs, where one third of them are of resolution 1024*768 and two thirds are of resolution 1920*1080. The pair-wise comparison was adopted in the subjective test for user friendliness, and the Mean Opinion Score (MOS) were computed accordingly. Finally, we evaluate the performance of several 2D and 3D image quality metrics applied to MCL-3D. All texture images, depth images, rendered image pairs in MCL- 3D and their MOS values obtained in the subjective test are available to the public (http: //mcl.usc.edu/mcl-3d-database/) for future research and development.

Image analysis for all.

Abstract: The availability of user-friendly scientific image analysis software for the Macintosh II has made the application of digital imaging techniques both practical and cost-effective in many areas of research.

System and method for generating an image from an image dataset and a video image

Abstract: In a system and a method for generating an image that contains superimposed or fused image data, a first system acquires an image dataset from a subject and a second system obtains a video image of the subject. The positions of the first, the second systems in the acquisition of the image datasets are determined with the an arrangement for position determination, such as a navigation system, with reference to which the position of the image dataset acquired with the first system and the position of the video image dataset can be determined in space, so that the two image datasets can be superimposed or fused with one another. An arrangement also can be provided for generating a video image expanded with image data of the image dataset acquired with the first system.

3-D face structure extraction and recognition from images using 3-D morphing and distance mapping

Abstract: We describe a novel approach for creating a three-dimensional (3-D) face structure from multiple image views of a human face taken at a priori unknown poses by appropriately morphing a generic 3-D face. A cubic explicit polynomial in 3-D is used to morph a generic face into the specific face structure. The 3-D face structure allows for accurate pose estimation as well as the synthesis of virtual images to be matched with a test image for face identification. The estimation of a 3-D person's face and pose estimation is achieved through the use of a distance map metric. This distance map residual error (geometric-based face classifier) and the image intensity residual error are fused in identifying a person in the database from one or more arbitrary image view(s). Experimental results are shown on simulated data in the presence of noise, as well as for images of real faces, and promising results are obtained.