6620052
ScatterType: a legible but hard-to-segment CAPTCHA
The ScatterType CAPTCHA (completely automated public Turing tests to tell computers and humans apart), designed to resist character-segmentation attacks and shown to be highly legible to human readers, is analyzed for vulnerabilities and is offered for experiments in automatic attack. As introduced in Baird and Riopka (2005), 'ScatterType' challenges are images of machine-print text whose characters are cut into pieces which then drift apart, in an attempt to frustrate segment-then-recognize computer vision attacks. Analysis of experimental human legibility data has shown that better than 95% correct legibility can be achieved through judicious choice of the pseudorandom generating parameters (Baird et al., 2005). That analysis is summarized and discussed here as motivation for a discussion of potential vulnerabilities. An invitation to attack ScatterType is offered.
16153164
Rapid Benchmarking for Semantic Web Knowledge Base Systems
We present a method for rapid development of benchmarks for Semantic Web knowledge base systems. At the core, we have a synthetic data generation approach for OWL that is scalable and models the real world data. The data-generation algorithm learns from real domain documents and generates benchmark data based on the extracted properties relevant for benchmarking. We believe that this is important because relative performance of systems will vary depending on the structure of the ontology and data used. However, due to the novelty of the Semantic Web, we rarely have sufficient data for benchmarking. Our approach helps overcome the problem of having insufficient real world data for benchmarking and allows us to develop benchmarks for a variety of domains and applications in a very time efficient manner. Based on our method, we have created a new Lehigh BibTeX Benchmark and conducted an experiment on four Semantic Web knowledge base systems. We have verified our hypothesis about the need for representative data by comparing the experimental result to that of our previous Lehigh University Benchmark. The difference in both experiments has demonstrated the influence of ontology and data on the capability and performance of the systems and thus the need of using a representative benchmark for the intended application of the systems.
4791841
Salient Boundary Detection using Ratio Contour
This paper presents a novel graph-theoretic approach, named ratio contour, to extract perceptually salient boundaries from a set of noisy boundary fragments detected in real images. The boundary saliency is defined using the Gestalt laws of closure, proximity, and continuity. This paper first constructs an undirected graph with two different sets of edges: solid edges and dashed edges. The weights of solid and dashed edges measure the local saliency in and between boundary fragments, respectively. Then the most salient boundary is detected by searching for an optimal cycle in this graph with minimum average weight. The proposed approach guarantees the global optimality without introducing any biases related to region area or boundary length. We collect a variety of images for testing the proposed approach with encouraging results.
2070714
A large quantity of novel human antisense transcripts detected by LongSAGE
MOTIVATION
Taking advantage of the high sensitivity and specificity of LongSAGE tag for transcript detection and genome mapping, we analyzed the 632 813 unique human LongSAGE tags deposited in public databases to identify novel human antisense transcripts.


RESULTS
Our study identified 45 321 tags that match the antisense strand of 9804 known mRNA sequences, 6606 of which contain antisense ESTs and 3198 are mapped only by SAGE tags. Quantitative analysis showed that the detected antisense transcripts are present at levels lower than their counterpart sense transcripts. Experimental results confirmed the presence of antisense transcripts detected by the antisense tags. We also constructed an antisense tag database that can be used to identify the antisense SAGE tags originated from the antisense strand of known mRNA sequences included in the RefSeq database.


CONCLUSIONS
Our study highlights the benefits of exploring SAGE data for comprehensive identification of human antisense transcripts and demonstrates the prevalence of antisense transcripts in the human genome.
6932570
3D Surface Matching and Recognition Using Conformal Geometry
3D surface matching is a fundamental issue in computer vision with many applications such as shape registration, 3D object recognition and classification. However, surface matching with noise, occlusion and clutter is a challenging problem. In this paper, we analyze a family of conformal geometric maps including harmonic maps, conformal maps and least squares conformal maps with regards to 3D surface matching. As a result, we propose a novel and computationally efficient surface matching framework that uses least squares conformal maps. According to conformal geometry theory, each 3D surface with disk topology can be mapped to a 2D domain through a global optimization and the resulting map is a diffeomorphism, i.e., one-to-one and onto. This allows us to simplify the 3D surface-matching problem to a 2D image-matching problem, by comparing the resulting 2D conformal geometric maps, which are stable, insensitive to resolution changes and robust to occlusion and noise. Therefore, highly accurate and efficient 3D surface matching algorithms can be achieved by using conformal geometric maps. Finally, the performance of conformal geometric maps is evaluated and analyzed comprehensively in 3D surface matching with occlusion, noise and resolution variation. We also provide a series of experiments on real 3D face data that achieve high recognition rates.
4791224
Image Segmentation with Minimum Mean Cut
We introduce a new graph-theoretic approach to image segmentation based on minimizing a novel class of ‘mean cut’ cost functions. Minimizing these cost functions corre sponds to finding a cut with minimum mean edge weight in a connected planar graph. This approach has several advantages over prior approaches to image segmentation. First, it allows cuts with both open and closed boundaries. Second, it guarantees that the partitions are connected. Third , the cost function does not introduce an explicit bias, such as a preference for large-area foregrounds, smooth or short boundaries, or similar-weight partitions. This lack of bia s allows it to produce segmentations that are better aligned with image edges, even in the presence of long thin regions. Finally, the global minimum of this cost function is largely insensitive to the precise choice of edge-weight function. In particular, we show that the global minimum is invariant under a linear transformation of the edge weights and thus insensitive to image contrast. Building on algorithms by Ahuja, Magnanti, and Orlin (1993), we present a polynomial-time algorithm for finding a global minimum of the mean-cut cost function and illustrate the results of applying that algorithm to several synthetic and real images.
2740448
A novel conflict detection and resolution strategy based on TLRSP in replicated mobile database systems
Replication is one of the key technologies in promoting the performance of mobile database systems. In this paper, a novel mobile database replication scheme, the transaction-level result-set propagation (TLRSP) model, is put forward. A conflict detection and resolution strategy based on TLRSP is discussed in detail and its implementation algorithm is proposed. In the TLRSP model, mobile users are allowed to access local replicas of the database and to submit local transactions when the system is disconnected. The locally committed transactions are sent to a fixed database server for conflict reconciliation and result-set incorporation when the system is reconnected. The TLRSP model uses the incremental refreshing method to synchronize database replicas and to maintain the consistency of the replicated mobile database system.