12811258
A Parsimony Approach to Genome-Wide Ortholog Assignment
The assignment of orthologous genes between a pair of genomes is a fundamental and challenging problem in comparative genomics, since many computational methods for solving various biological problems critically rely on bona fide orthologs as input. While it is usually done using sequence similarity search, we recently proposed a new combinatorial approach that combines sequence similarity and genome rearrangement. This paper continues the development of the approach and unites genome rearrangement events and (post-speciation) duplication events in a single framework under the parsimony principle. In this framework, orthologous genes are assumed to correspond to each other in the most parsimonious evolutionary scenario involving both genome rearrangement and (post-speciation) gene duplication. Besides several original algorithmic contributions, the enhanced method allows for the detection of inparalogs. Following this approach, we have implemented a high-throughput system for ortholog assignment on a genome scale, called MSOAR, and applied it to the genomes of human and mouse. As the result will show, MSOAR is able to find 99 more true orthologs than the INPARANOID program did. We have also compared MSOAR with the iterated exemplar algorithm on simulated data and found that MSOAR performed very well in terms of assignment accuracy. These test results indiate that our approach is very promising for genome-wide ortholog assignment.
8099146
A new compression scheme for color-quantized images
An efficient compression scheme for color-quantized images based on progressive coding of color information has been developed. Instead of sorting color indexes into a linear list struc- ture, a binary-tree structure of color indexes is proposed. With this structure, the new algorithm can progressively recover an image from two colors to all of the colors contained in the original image, i.e., a lossless recovery is achieved. Experimental results showed that it can efficiently compress images in both lossy and lossless cases. Typically for color-quantized Lena image with 256 colors, the algorithm achieved 0.5 bpp below state-of-the-art lossless com- pression methods while preserving the efficient lossy compression. Such a compression scheme is very attractive to many applications that require the ability of fast browsing or progressive transmis- sion, and if necessary, to exactly recover the original image. Index Terms—Color-quantized images, data compression, index sorting, progressive refinement.
62212743
Resampling for face detection by self-adaptive genetic algorithm
Over the past ten years, face detection has been thoroughly studied in computer vision research for its interesting applications. However, all of the state-of-the-art statistical methods suffer from the data collection for training a classifier. This paper presents a self-adaptive genetic algorithm (GA)-based method to swell face database through re-sampling from the existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. To verify the generalization capability of the proposed method, we also use the expanded database to train an Adaboost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods.
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
7672318
Fusion of Infrared and Range Data: Multi-modal Face Images
Infrared and range imagery are intriguing sensing modalities for face recognition systems. They may offer better performance than other modalities due to their robustness to environmental effects and deliberate attempts to obscure identity. Furthermore, a combination of these modalities may offer additional discrimination power. Toward this end, we present a semi-automatic system that captures range and infrared data of a human subject’s face, registers and integrates multiple 3D views into one model, and applies the infrared measurements as a registered texture map.
62599943
Finding nuggets in documents: A machine learning approach
Document keyphrases provide a concise summary of a document's content, offering semantic metadata summarizing a document. They can be used in many applications related to knowledge management and text mining, such as automatic text summarization, development of search engines, document clustering, document classification, thesaurus construction, and browsing interfaces. Because only a small portion of documents have keyphrases assigned by authors, and it is time-consuming and costly to manually assign keyphrases to documents, it is necessary to develop an algorithm to automatically generate keyphrases for documents. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified phrases to assign weights to the candidate keyphrases. The logic of our algorithm is: The more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. KIP's learning function can enrich the glossary database by automatically adding new identified keyphrases to the database. KIP's personalization feature will let the user build a glossary database specifically suitable for the area of hissher interest. The evaluation results show that KIP's performance is better than the systems we compared to and that the learning function is effective. © 2006 Wiley Periodicals, Inc.
13185710
Foveated observation of shape and motion
Robotic navigation and interaction frequently require that the shape and motion of external objects and events be observed. Many interesting events occur at mixed scales. Subtle localized shape and motion often occurs together with long-range movements. One of the chief challenges in recovering these events is to obtain high resolution imagery suitable for resolving small details, while simultaneously increasing the working volume in which recovery is possible. This paper proposes architecture for mixed scale motion recovery. The robust coverage of a large working volume is provided by a wide area of tracking system. This system localizes interesting motions, and guides a separate foveated system of pan tilt cameras to observe the detailed event at high resolution. We demonstrate two applications, foveated structured light scanning and the capture of muscle deformation while walking. Both applications allow subtle detailed recovery that would not be possible using existing single scale systems.