2366018
Constrained texture synthesis by scalable sub-patch algorithm
In this paper, we propose a novel scalable sub-patch algorithm for texture synthesis. Texture synthesis is an important research topic in computer graphics and image processing. We present an efficient algorithm which can paste a patch each time without boundary processing technique. In particular, the proposed sub-patch algorithm can be used in constrained texture synthesis to fill the large lost region. On the other hand, the searching neighborhood pixels can be changed due to different patch size in image reconstruction. The comparison of previous algorithms is also provided in this paper. We present a first algorithm used in constrained texture synthesis by regular synthesizing order
8736717
Skew detection and reconstruction based on maximization of variance of transition-counts
Abstract The input document images with skew can be a serious problem in the optical character recognition system. A robust method is proposed in this paper for skew detection and reconstruction in document images which can contain less text areas, high noises, tables, figures, flow-chart, and photos. The basic idea of our approach is the maximization of variance of transition counts for the skew detection and text-orientation determination. Once the skew angle is determined, the scanning-line model is applied to reconstruct the skew images. 103 documents with great varieties have been tested and successfully processed. The average detection time of A4 size image is 4.86 s and the reconstruction time is 5.52 s. The proposed approach is also compared with the existing algorithms published in the literature and our method gets some significant improvements in skew detection and reconstruction.
21513395
VLSI architecture and implementation for FS1016 CELP decoder with reduced power and memory requirements
Abstract The code-excited linear predictive (CELP) coder is the most effective technique among various linear predictive coding methods for speech compression. Hence, designing a relatively low-cost and low-power CELP decoder chip for the portable systems and wireless digital communication environment becomes increasingly important. This paper presents the VLSI architecture and chip implementation for the FS1016 CELP decoder with reduced power and memory requirements. A single-chip implementation of the CELP decoder drastically reduces the cost and the size of many CELP vocoder systems. The decoder chip can achieve the following: (1) excellent accuracy results due to the accuracy studies for the finite word length, (2) power savings and high-speed operations owing to the combined advantages of pipeline and concurrent processing structures, (3) table size reducing by applying the memoryless realization for stochastic codebook and partial sums technique and (4) specification satisfying the FS1016 CELP coder. Fabricated with 0.8 μm double-metal CMOS technology, the chip contains approximately 13 000 transistors occupying a 6.1 × 6.2 mm2 area. It has been tested to be fully functional at IMS XL-60 tester and is the first working FS1016 CELP 4.8 K decoder chip. In particular, the fixed-point accuracy studies for the CELP decoder are also addressed herein.
33293924
Intelligent Object Extraction Algorithm Based on Foreground/Background Classification
In this paper, we propose an intelligent object extraction algorithm based on foreground/background classification. The proposed algorithm can offer the users more friendly interface for object extraction from image without unnecessary steps. After the interactive steps from user (marking the foreground and background parts), the wanted object is extracted from the background automatically. The proposed algorithm processes the input image by watershed to produce the regions. Then, the regions are labeled after marking parts of regions. We also introduce an implementation of hierarchical queues to store the unlabeled regions. The classification of foreground and background will generate the final image with selected object. In our experimental results, the proposed algorithm provides the output image with high efficiency. The wanted object is generated after user marking the foreground and background parts less than one second. In addition, the application of this work also can be used in image synthesis or object removal in other fields of image processing.
17964420
A learning process to the identification of feature points on Chinese characters
The paper describes a novel stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on convolution operations with a set of one-dimensional (1D) Gabor templates efficiently extracts the stroke segments from noisy and degraded characters. Furthermore, the relationship between endpoints of stroke segments is modeled as junction structure during a learning process. Finally, each endpoint is identified as a feature point to determine the junction structure by the learning-based technique, rather than rule-based techniques with manual rule creation. Experimental results indicate that the learning-based technique can generalize learning knowledge to identify 1200 feature points with an average identification rate of 93.58% for test set, using k-fold cross-validation testing.
198169485
A robust Stroke Extraction method for Handwritten Chinese Characters
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
8673951
An iterative optimization approach for unified image segmentation and matting
Separating a foreground object from the background in a static image involves determining both full and partial pixel coverages, also known as extracting a matte. Previous approaches require the input image to be presegmented into three regions: foreground, background and unknown, which are called a trimap. Partial opacity values are then computed only for pixels inside the unknown region. This presegmentation based approach fails for images with large portions of semitransparent foreground where the trimap is difficult to create even manually. In this paper, we combine the segmentation and matting problem together and propose a unified optimization approach based on belief propagation. We iteratively estimate the opacity value for every pixel in the image, based on a small sample of foreground and background pixels marked by the user. Experimental results show that compared with previous approaches, our method is more efficient to extract high quality mattes for foregrounds with significant semitransparent regions
34068607
Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control
Presents a systematic approach for developing a concise self-adaptive neuro-fuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for online learning the control knowledge for autonomous underwater vehicles (AUV). The multi-layered structure of SANFIS incorporates fuzzy basis functions for better function approximations. Based on the need of different applications, we investigate three SANFIS structures with three different types of fuzzy IF-THEN-rule-based models and cast the rule formation problem as a clustering problem. A recursive least squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the learning process. Thus, incorporating an online clustering technique, a fast hybrid learning procedure and rule examination, the SANFIS is capable of self-organizing and self-adapting its internal structure for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.
34164276
Absolute results concerning one-way functions and their applications
A new approach to one-way functions is considered. A one-way function is defined to be a function which is easy to compute but hard to invert in the sense that its inverse is not inDTIME(nk) for fixed largek. While this is weaker than the usual definition of one-way functions it requires no complexity-theoretic assumptions. Some of these functions are proved to exist, while the existence of other, stronger functions is shown to bear upon open problems in complexity theory. An application to public-key cryptosystems is given.
14138215
Multiresolution object-of-interest detection for images with low depth of field
This paper describes a novel multiresolution image segmentation algorithm for separating sharply focused objects-of-interest from other foreground or background objects in low depth-of-field (DOF) images, such as sports, telephoto, macro, and microscopic images. The algorithm takes a multiscale context-dependent approach to segment images based on features extracted from wavelet coefficients in high-frequency bands. The algorithm is fully automatic in that all parameters are image-independent. Experiments with the algorithm on more than 100 low DOF images have shown results close to the human segmentation of these images. Besides high accuracy, the algorithm also provides high speed. A 768/spl times/512 pixel image can be segmented within two seconds on a Pentium Pro 300 MHz PC.