Subpixel rendering

About: Subpixel rendering is a research topic. Over the lifetime, 3885 publications have been published within this topic receiving 82789 citations.

A Subpixel Target Detection Approach to Hyperspectral Image Classification

Abstract: Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral–spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu’s method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates.

Corner detection and curve representation using cubic B-splines

Abstract: In this paper, we propose to use B-Splines to represent digital curves. We have developed an efficient algorithm to locate corners and at the same time encode curve segments between them using B-Splines. Used in conjunction with our subpixel edge detector, [1], it allows us to obtain accurate position of the corners, as needed in many registration problems such as stereo matching and motion parameter estimation. In addition to corners, we detect points of significant curvature between them. The resulting representation is a good approximation of the original, in the sense that it makes interesting points explicit, and achieves significant data compression.

HAIRIS: A Method for Automatic Image Registration Through Histogram-Based Image Segmentation

Abstract: Automatic image registration is still an actual challenge in several fields. Although several methods for automatic image registration have been proposed in the last few years, it is still far from a broad use in several applications, such as in remote sensing. In this paper, a method for automatic image registration through histogram-based image segmentation (HAIRIS) is proposed. This new approach mainly consists in combining several segmentations of the pair of images to be registered, according to a relaxation parameter on the histogram modes delineation (which itself is a new approach), followed by a consistent characterization of the extracted objects-through the objects area, ratio between the axis of the adjust ellipse, perimeter and fractal dimension-and a robust statistical based procedure for objects matching. The application of the proposed methodology is illustrated to simulated rotation and translation. The first dataset consists in a photograph and a rotated and shifted version of the same photograph, with different levels of added noise. It was also applied to a pair of satellite images with different spectral content and simulated translation, and to real remote sensing examples comprising different viewing angles, different acquisition dates and different sensors. An accuracy below 1° for rotation and at the subpixel level for translation were obtained, for the most part of the considered situations. HAIRIS allows for the registration of pairs of images (multitemporal and multisensor) with differences in rotation and translation, with small differences in the spectral content, leading to a subpixel accuracy.

Generating exact ray-traced animation frames by reprojection

Abstract: Reprojection techniques can create approximate ray traced animation frames. Extending an existing algorithm yields exact frames and full ray tracing, with up to 92 percent savings in rendering time. We present an algorithm that exploits spatiotemporal coherence between frames to significantly decrease the rendering time of ray traced animations. This method produces inferred ray traced images of any scene that can be ray traced using a point sampled method. The images created by the algorithm are not approximated frames created from weighted averages of other frames, nor are they frames patched together from near frame pixel values. The algorithm guarantees that a color seen in a subpixel would be returned by a ray passing somewhere through that subpixel, but not necessarily though the center. This algorithm efficiently creates frames of any view that can be ray traced. While the savings increase with the complexity of the rendered objects and the preponderance of diffuse objects, significant savings occur with reflective and refractive objects. However, the technique requires the ray tracing method to be point sample oriented. >

Absolute depth estimate in stereopsis

Abstract: An analysis of decrease of accuracy in passive stereoscopic vision shows that in order to obtain absolute depth estimates useful for robotics (Δz < 1%) it is necessary to measure all mechanical parameters with an extremely high precision, to reach subpixel accuracy, and to match features between two rather different images.