3D movies/videos have become increasingly popular in the market; however, they are usually produced by professionals. This paper presents a new technique for the automatic conversion of 2D to 3D video based on RGB-D sensors, which can be easily conducted by ordinary users. To generate a 3D image, one approach is to combine the original 2D color image and its corresponding depth map together to perform depth image-based rendering (DIBR). An RGB-D sensor is one of the inexpensive ways to capture an image and its corresponding depth map. The quality of the depth map and the DIBR algorithm are crucial to this process. Our approach is twofold. First, the depth maps captured directly by RGB-D sensors are generally of poor quality because there are many regions missing depth information, especially near the edges of objects. This paper proposes a new RGB-D sensor based depth map inpainting method that divides the regions with missing depths into interior holes and border holes. Different schemes are used to inpaint the different types of holes. Second, an improved hole filling approach for DIBR is proposed to synthesize the 3D images by using the corresponding color images and the inpainted depth maps. Extensive experiments were conducted on different evaluation datasets. The results show the effectiveness of our method.
A novel three-dimensional fuzzy logic controller (3D FLC) was developed recently for spatially distributed systems. In this study, the inherent spatial structure feature of a 3D FLC with two spatial inputs (also called as 3D two-term FLC) is first exposed via an analytical model. Then, the global bounded-input/bounded-output (BIBO) stability of the 3D fuzzy two-term control system is discussed. A sufficient condition is derived and provided as a useful criterion for the controller design of the 3D two-term FLC. Finally, a catalytic packed-bed reactor is presented as an example of spatially distributed process to demonstrate the effectiveness of the controller. 相似文献
Similarity search is important in information retrieval applications where objects are usually represented as vectors of high
dimensionality. This leads to the increasing need for supporting the indexing of high-dimensional data. On the other hand,
indexing structures based on space partitioning are powerless because of the well-known “curse of dimensionality”. Linear
scan of the data with approximation is more efficient in the high-dimensional similarity search. However, approaches so far
have concentrated on reducing I/O, and ignored the computation cost. For an expensive distance function such as Lp norm with fractional p, the computation cost becomes the bottleneck. We propose a new technique to address expensive distance functions by “indexing
the function” by pre-computing some key values of the function once. Then, the values are used to develop the upper/lower
bounds of the distance between a data vector and the query vector. The technique is extremely efficient since it avoids most
of the distance function computations; moreover, it does not involve any extra secondary storage because no index is constructed
and stored. The efficiency is confirmed by cost analysis, as well as experiments on synthetic and real data. 相似文献
