6462272
Intrinsic Images by Entropy Minimization
A method was recently devised for the recovery of an invariant image from a 3-band colour image. The invariant image, originally 1D greyscale but here derived as a 2D chromaticity, is independent of lighting, and also has shading removed: it forms an intrinsic image that may be used as a guide in recovering colour images that are independent of illumination conditions. Invariance to illuminant colour and intensity means that such images are free of shadows, as well, to a good degree. The method devised finds an intrinsic reflectivity image based on assumptions of Lambertian reflectance, approximately Planckian lighting, and fairly narrowband camera sensors. Nevertheless, the method works well when these assumptions do not hold. A crucial piece of information is the angle for an “invariant direction” in a log-chromaticity space. To date, we have gleaned this information via a preliminary calibration routine, using the camera involved to capture images of a colour target under different lights. In this paper, we show that we can in fact dispense with the calibration step, by recognizing a simple but important fact: the correct projection is that which minimizes entropy in the resulting invariant image. To show that this must be the case we first consider synthetic images, and then apply the method to real images. We show that not only does a correct shadow-free image emerge, but also that the angle found agrees with that recovered from a calibration. As a result, we can find shadow-free images for images with unknown camera, and the method is applied successfully to remove shadows from unsourced imagery.
7786870
Construction of a hierarchical classifier schema using a combination of text-based and image-based approaches
Web document hierarchical classification approaches often rely on textual features alone even though web pages include multimedia data. We propose a new hierarchical integrated web classification approach that combines image-based and text-based approaches. Instead of using a flat classifier to combine text and image classification, we perform classification on a hierarchy differently on different levels of the tree, using text for branches and images only at leaves. The results of our experiments show that the use of the hierarchical structure improved web document classification performance significantly.
2968463
Making Data Mining Models Useful to Model Non-paying Customers of Exchange Carriers
Due to both limitations of technologies and the nature of the problems, data mining may not be able to solve a problem completely in a way as one wishes. When this happens, we need to first understand the actual need of business, characteristic of available partial solution, and then make compromises between the technology solution and business needs. A majority of the papers published in data mining conferences and journals seem to concentrate only on the success side of the story. In this paper, we discuss our experiences and the complete process from near failure to success when applying inductive learning techniques to predict non-paying customers of competitive local exchange carriers (CLEC’s), currently at 20%. Experiments with a number of stateof-the-art methods and algorithms found that most customers were labeled as paying on time. Cost-sensitive learning is not possible since the target company cannot define a cost-model. Finally, after discussing with the billing department, a compromised but still useful solution is to predict the probability that someone will default. The billing team can use the predicted score to prioritize collection efforts as well as to predict cash flow. We have found that two randomized decision tree ensemble methods (Fan’s random decision tree and a probabilistic extension of Breiman’s random forest) are consistently more accurate in posterior probability estimation than single decision tree based probability calibration methods. The software, both Fan’s RDT and probabilistic extension of random forest, as well as a longer version of this paper will be made available by the contacting author.
11974123
Practical 3-D Shape Measurement Using Optimal Intensity-Modulated Projection and Intensity-Phase Analysis Techniques
In the field of 3D image measurement, a technique based on stripe pattern projection and observation image intensity analysis is expected to be able to detect several stripes by a single projection. It is necessary to increase the stripe number of the projection pattern in order to improve the measurement accuracy of depth distance. However, when the stripe number is increased, the difference of the intensity between stripes will be reduced and stripe detection will become difficult. In order to improve the detection accuracy of the stripe order and shorten the 3D measurement time, we use the optimal intensity-modulation projection (OIMP) technique, and in order to improve the depth distance measurement accuracy, we propose an intensity-phase analysis (IPA) technique. In the proposed IPA technique, the observation pattern be segmented by the intensity of the intensity-modulated stripe, and in every segmentation the depth distance of all pixels are obtained by phase analysis. By using a combination of the OIMP and IPA techniques, high-speed (single projection and double image captures) and high-accuracy (error less than 0.1%) practical 3D shape measurement can be realized
9704136
Detecting region outliers in meteorological data
Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Detection of spatial outliers helps reveal important and valuable information from large spatial data sets. In the field of meteorology, for example, spatial outliers can be associated with disastrous natural events such as tornadoes, hurricane, and forest fires. Previous study of spatial outlier mainly focuses on point data. However, in the meteorological data or other applications, spatial outliers are frequently represented in region, i.e., a group of points, with two dimensions or even three dimensions, and the previous point-based approaches may not be appropriate to be used. As region outliers are commonly multi-scale objects, wavelet analysis is an effective tool to study them. In this paper, we propose a wavelet analysis based approach to detect region outliers. We discuss the region outlier detection problem and design a suite of algorithms to effectively discover them. The algorithms were implemented and evaluated with a real-world meteorological data set.
15346536
Distance-two labelings of graphs
For given positive integers j ≥ k, an L(j,k)-labeling of a graph G is a function f : V (G) → {0, 1, 2,...} such that |f(u) - f(v)| ≥ j when dG(u, v) = 1 and |f(u) - f(v)| ≥ k when dG(u, v) = 2. The L(j, k)-labeling number λj,k(G) of G is defined as the minimum m such that there is an L(j, k)-labeling f of G with f(V(G)) ⊆ {0, 1, 2,....,m}. For a graph G of maximum degree Δ ≥ 1 it is the case that λj,k(G) ≥ j + (Δ - 1)k. The purpose of this paper is to study the structures of graphs G with maximum degree Δ ≥ 1 and λj,k(G) = j + (Δ - 1)k.
11606897
Malicious ICMP Tunneling: Defense against the Vulnerability
This paper presents a systematic solution to the problem of using ICMP tunneling for covert channel. ICMP is not multiplexed via port numbers and the data part of the ICMP packet provides considerable bandwidth for malicious covert channels. These factors make it an integral part of many malicious software like remote access and denial of service attack tools. These tools use ICMP to establish covert communication channels. In this paper a stateless model is proposed to prevent ICMP tunneling. A Linux kernel module was implemented to demonstrate the proposed stateless solution. The module enforces a fixed payload policy for ICMP packets and virtually eliminates ICMP tunneling which arises due to the data carrying capability of ICMP. The performance impact on end hosts and routers due to the stateless monitoring model is described.