10710388
CAPTCHA Challenge Tradeoffs: Familiarity of Strings versus Degradation of Images
It is a well documented fact that, for human readers, familiar text is more legible than unfamiliar text. Current-generation computer vision systems also are able to exploit some kinds of prior knowledge of linguistic context: for example, many OCR systems can use known lexica (word-lists, such as of commonly occurring English words) to disambiguate interpretations. It is interesting that human readers can exploit various degrees of familiarity; for example, strings of characters which, while not found in dictionaries, are similar to spelled words: e.g. "pronounceable" strings, or strings made up of frequently occurring character n-grams. In contrast to this, computer vision technologies for exploiting such poorly characterized constraints (absent an explicit, complete lexicon) are not yet well developed. This gap in ability may allow us to design stronger CAPTCHAs. We measure the familiarity of challenge strings generated by four methods (described by Bentley and Mallows) and we use the ScatterType CAPTCHA to degrade challenge images. We report the results of a human legibility trial which supports the hypothesis that more familiar strings are indeed more legible in CAPTCHAs. Our measurements may enable engineering CAPTCHAs with a more uniform distribution of difficulty by balancing image degradations against familiarity
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.
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.
14809978
Image contrast enhancement based on intensity-pair distribution
Current contrast enhancement algorithms sometimes come with undesired drawbacks, like the loss of tiny details, enhancement of image noise, occasional over-enhancement, and unnatural look of the processed images. In this paper, we propose a new approach for contrast enhancement based on the use of a so-called intensity-pair distribution. This distribution possesses both local information and global information of the image content. By analyzing the content of intensity-pair distribution, a set of expansion forces are generated for contrast enhancement while another set of anti-expansion forces are generated to suppress image noise. To avoid over-enhancement and preserve the natural look of the processed images, a magnitude mapping function is also proposed. Experimental results show that the proposed algorithm does provide a flexible and reliable way for contrast enhancement.
12212021
Variational Bayesian image modelling
We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models---Hidden Markov Random Fields (HMRFs). HMRFs are particularly well suited to image modelling and in this paper, we apply them to the problem of image segmentation. Unfortunately, HMRFs are notoriously hard to train and use because the exact inference problems they create are intractable. Our main contribution is to introduce an efficient variational approach for performing approximate inference of the Bayesian formulation of HMRFs, which we can then apply to the density estimation and model selection problems that arise when learning image models from data. With this variational approach, we can conveniently tackle the problem of image segmentation. We present experimental results which show that our technique outperforms recent HMRF-based segmentation methods on real world images.
11278363
A Distributed Artificial Network Solving Complex and Multiple Causal Associations
Causal reasoning (known also as abduction) is a hard task that cognitive agents perform reliably and quickly. A particular class of causal reasoning that raises several difficulties is the cancellation class. Cancellation occurs when a set of causes (hypotheses) cancel each other's explanation with respect to a given effect (observation). For example, a cloudy sky may suggest a rainy weather; whereas a shiny sky may suggest the absence of rain. In the current paper, we extend a recent neural model to handle cancellation interactions. We conduct a sensitivity analysis of this proposal on ad hoc problems put at extreme cases. Finally, we test the model on a large database and propose objective criteria to quantitatively evaluate its performance. Simulation results are very satisfactory and should encourage research.
554747
Image-Segmentation Evaluation From the Perspective of Salient Object Extraction
Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application, the evaluation results may not be extended to other applications. We present in this paper a new benchmark for evaluating image segmentation. Specifically, we formulate image segmentation as identifying the single most perceptually salient structure from an image. We collect a large variety of test images that conforms to this specific formulation, construct unambiguous ground truth for each image, and define a reliable way to measure the segmentation accuracy. We then present two special strategies to further address two important issues: (a) the most salient structures in some real images may not be unique or unambiguously defined, and (b) many available image-segmentation methods are not developed to directly extract a single salient structure. Finally, we apply this benchmark to evaluate and compare the performance of several state-of-the-art image-segmentation methods, including the normalized-cut method, the level-set method, the efficient graph-based method, the mean-shift method, and the ratio-contour method.
15396849
Convex grouping combining boundary and region information
Convexity is an important geometric property of many natural and man-made structures. Prior research has shown that it is imperative to many perceptual-organization and image-understanding tasks. This paper presents a new grouping method for detecting convex structures from noisy images in a globally optimal fashion. Particularly, this method combines both region and boundary information: the detected structural boundary is closed and well aligned with detected edges while the enclosed region has good intensity homogeneity. We introduce a ratio-form cost function for measuring the structural desirability, which avoids a possible bias to detect small structures. A new fragment-pruning algorithm is developed to achieve the structural convexity. The proposed method can also be extended to detect open boundaries, which correspond to the structures that are partially cropped by the image perimeter and incorporate a human-computer interaction for detecting a convex boundary around a specified point. We test the proposed method on a set of real images and compare it with the Jacobs'convex-grouping method