Hybrid inverse halftoning using adaptive filtering
We propose a novel fast inverse halftoning technique using a combination of spatial varying filtering and spatial invariant filtering. The proposed algorithm is significantly simpler than most existing algorithms. Without explicit edge region classification, the proposed spatial varying filter is shown to be capable of preserving edges effectively.

title: Novel blind multiple watermarking technique for images
abstract: Three novel blind watermarking techniques are proposed to embed watermarks into digital images for different purposes. The watermarks are designed to be decoded or detected without the original images. The first one, called single watermark embedding (SWE), is used to embed a watermark bit sequence into digital images using two secret keys. The second technique, called multiple watermark embedding (MWE), extends SWE to embed multiple watermarks simultaneously in the same watermark space while minimizing the watermark (distortion) energy. The third technique, called iterative watermark embedding (IWE), embeds watermarks into JPEG-compressed images. The iterative approach of IWE can prevent the potential removal of a watermark in the JPEG recompression process. Experimental results show that embedded watermarks using the proposed techniques can give good image quality and are robust in varying degree to JPEG compression, low-pass filtering, noise contamination, and print-and-scan.

title: A blind watermarking technique for multiple watermarks
abstract: We propose a blind watermarking technique to embed multiple watermarks simultaneously. It allows the use of correlated key to embed multiple watermarks. A dual-key system is used to reduce the chance of the removal of watermark. Each embedded watermark can be decoded/detected by its own key. One direct approach and two iterative approaches are proposed to reduce the correlation effects among the noise-like keys. Experimental results show that multiple watermarks can be embedded without significant loss in PSNR and the watermark can be detected when the watermarked image is JPEG compressed.

title: On improving the iterative watermark embedding technique for JPEG-to-JPEG watermarking
abstract: We previously proposed an iterative technique called iterative watermark embedding (IWE) for watermark embedding in JPEG compressed domains. The proposed technique embeds the watermark successfully when the quality factor of the JPEG image is high, but when the quality factor is low, not all the bits of the watermark can be embedded. This is due to uneven distribution of capacities in different regions of the image. Moreover, the computation complexity of the iterative loop is high. In this paper we improve the technique using shuffling to equalize the capacities of different regions. Also, the iterative loop is replaced by a direct approach to reduce the computational complexity. We call this direct approach Direct JPEG Watermarking Embedding (DJWE). A Human Visual System (HVS) model is used to prioritize the DCT coefficients to achieve good visual quality. Experimental results show that the watermarks are robust against Gaussian noise attack and JPEG transcoding attacks.

title: Fast multi-block selection for H.264 video coding
abstract: The JVT/H.264 is the new video coding standard which achieves higher coding efficiency than previous standards such as H.263 or MPEG-2. The improvement in the rate-distortion performance in H.264 is mainly due to accurate motion estimation with different mode decision including multiple block size and multiple reference frame motion estimation. However, full exhaustive search of all block sizes is computational intensive with complexity increasing linearly with the number of allowed block size. In this paper, a fast multi-block motion estimation (FMBME) is proposed for H.264 video coding. Experimental results show that the proposed FMBME can efficiently reduce the computational cost by 58% with similar visual quality and bit rate.

title: Predictive motion estimation for reduced-resolution video from high-resolution compressed video
abstract: To convert a compressed video sequence to a lower-resolution compressed video, one typically needs to decompress the original sequence, down-sample each frame, and recompress it. It involves motion estimation in the reduced sequence which is computational intensive. We propose a novel fast algorithm to predict the motion vector of the reduced video by using the original motion information in the compressed bitstream. We achieve a much higher quality than existing algorithms with low additional complexity.

title: Dynamic visualization of transient data streams
abstract: We introduce two dynamic visualization techniques using multidimensional scaling to analyze transient data streams such as newswires and remote sensing imagery. While the time-sensitive nature of these data streams requires immediate attention in many applications, the unpredictable and unbounded characteristics of this information can potentially overwhelm many scaling algorithms that require a full re-computation for every update. We present an adaptive visualization technique based on data stratification to ingest stream information adaptively when influx rate exceeds processing rate. We also describe an incremental visualization technique based on data fusion to project new information directly onto a visualization subspace spanned by the singular vectors of the previously processed neighboring data. The ultimate goal is to leverage the value of legacy and new information and minimize re-processing of the entire dataset in full resolution. We demonstrate these dynamic visualization results using a newswire corpus and a remote sensing imagery sequence.

title: CRL: A Context-Aware Request Language for Mobile Computing
abstract: This paper introduces an XML-based generic Context Request Language (CRL), whose construction is part of a web services framework in the domain of mobile context sensing. The paper describes an implementation of the technique that is in accordance with the formal mathematical representational model, using first-order temporal language . The language is an attempt to introduce intelligence into context-aware computing by defining context-sensing elements into logical entities. The use of first-order calculus in this language definition serving on web service technology allows users to utilize context aggregation and to embed user control in contextual information. By carrying out on-the-fly context inferences at the middleware level, we can achieve a complete separation of concerns between user application and context sensing. Moreover, the declaration of contextual knowledge based on situations and events within the predicate domain allows users to express changes in contextual information and to quantify these elements among times and durations.

title: Visual Analytics
abstract: The information revolution is upon us, and it is guaran-teed to change our lives and the way we conduct our daily business. The fact that we have to deal with not just the size but also the variety and complexity of this in-formation makes it a real challenge to survive the revolu-tion. Enter Visual Analytics, a contemporary and proven approach to combine the art of human intuition and the science of mathematical deduction to directly perceive patterns and derive knowledge and insight from them.

title: Vector fields simplification-a case study of visualizing climate modeling and simulation data sets
abstract: In our study of regional climate modeling and simulation, we frequently encounter vector fields that are crowded with large numbers of critical points. A critical point in a flow is where the vector field vanishes. While these critical points accurately reflect the topology of the vector fields, in our study only a subset of them is worth further investigation. We present a filtering technique based on the vorticity of the vector fields to eliminate the less interesting and sometimes sporadic critical points in a multiresolution fashion. The neighboring regions of the preserved features, which are characterized by strong shear and circulation, are potential locations of weather instability. We apply our feature filtering technique to a regional climate modeling data set covering East Asia in the summer of 1991.

title: Visualizing sequential patterns for text mining
abstract: A sequential pattern in data mining is a finite series of elements such as A/spl rarr/B/spl rarr/C/spl rarr/D where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As out computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual data-mining environment.