Two classes of digital watermarks have been developed to protect the copyright ownership of digital images. Robust watermarks are designed to withstand attacks on an image (such as compression or scaling), while fragile watermarks are designed to detect minute changes in an image. Fragile marks can also identify where an image has been altered. This paper compares two fragile watermarks. The first method uses a hash function to obtain a digest of the image. An altered or forged version of the original image is then hashed and the digest is compared to the digest of the original image. If the image has changed the digests will be different. We will describe how images can be hashed so that any changes can be spatially localized. The second method uses the Variable-Watermark Two-Dimensional algorithm (VW2D) [1]. The sensitivity to changes is user-specific. Either no changes can be permitted (similar to a hard hash function), or an image can be altered and still be labeled authentic. Latter algorithms are known as semi-fragile watermarks.  We will describe the performance of these two techniques and discuss under what circumstances one would use a particular technique.

In this paper we present a novel multiresolution scheme for the detection of spiculated lesions in digital mammograms. First, a multiresolution representation of the original mammogram is obtained using a linear phase nonseparable 2-D wavelet transform. A set of features is then extracted at each resolution in the wavelet pyramid for every pixel. This approach addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear in different sizes. Detection is performed from the coarsest resolution to the finest resolution using a binary tree classifier. This top-down approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. Experimental results using the MIAS image database have shown that this algorithm is capable of detecting spiculated lesions of very different sizes at low false positive rates.

In this paper we present new results relative to the “expectation maximization/maximization of the posterior marginals” (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The EM/MPM algorithm uses a Markov random field model for the pixel class labels and alternately approximates the MPM estimate of the pixel class labels and estimates parameters of the observed image model.  The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels.  We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters.  We also present new new experimental results demonstrating the performance of the EM/MPM algorithm.

We present a new algorithm for segmentation of textured images using a multiresolution Bayesian approach. The new algorithm uses a multiresolution Gaussian autoregressive (MGAR) model for the pyramid representation of the observed image, and assumes a multiscale Markov random field model for the class label pyramid. Unlike previously proposed Bayesian multiresolution segmentation approaches, which have either used a single-resolution representation of the observed image or implicitly assumed independence between different levels of a multiresolution representation of the observed image, the models used in this paper incorporate correlations between different levels of both the observed image pyramid and the class label pyramid. The criterion used for segmentation is the minimization of the expected value of the number of misclassified nodes in the multiresolution lattice. The estimate which satisfies this criterion is referred to as the “multiresolution maximization of the posterior marginals” (MMPM) estimate, and is a natural extension of the single-resolution “maximization of the posterior marginals” (MPM) estimate. Previous multiresolution segmentation techniques have been based on the maximum a posteriori (MAP) estimation criterion, which has been shown to be less appropriate for segmentation than the MPM criterion. It is assumed that the number of distinct textures in the observed image is known. The parameters of the MGAR model—the means, prediction coefficients, and prediction error variances of the different textures—are unknown. A modified version of the expectation-maximization (EM) algorithm is used to estimate these parameters. The parameters of the Gibbs distribution for the label pyramid are assumed to be known. Experimental results demonstrating the performance of the algorithm are presented.

The use of mathematical morphology in low and mid-level image processing and computer vision applications has allowed the development of a class of techniques for analyzing shape information in monochrome images. In this paper these techniques are extended to color images. We investigate two approaches for \color morphology”: a vector approach, in which color vectors are ranked using a multivariate ranking concept known as reduced ordering, and a component-wise approach, in which grayscale morphological operations are applied to each of the three color component images independently. New vector morphological filtering operations are defined, and a set-theoretic analysis of these vector operations is presented. We also present experimental results comparing the performance of the vector approach and the component-wise approach for two applications: multiscale color image analysis and noise suppression in color images.

Collaborative and distributed applications, such as dynamic coalitions and virtualized grid computing, often require integrating access control policies of collaborating parties. Such an integration must be able to support complex authorization specifications and the fine-grained integration requirements that the various parties may have. In this paper, we introduce an algebra for fine-grained integration of sophisticated policies. The algebra is able to support the specification of a large variety of integration constraints. To assess the expressive power of our algebra, we prove its completeness and minimality. We then propose a framework that uses the algebra for the fine-grained integration of policies expressed in XACML. We also present a methodology for generating the actual integrated XACML policy, based on the notion of Multi-Terminal Binary Decision Diagrams.

When transmitting compressed video over a data network, one has to deal with how channel errors affect the decoding process. This is particularly problematic with data loss or erasures. In this paper we describe techniques to address this problem in the context of networks where channel errors or congestion can result in the loss of entire macroblocks when MPEG video is transmitted. We describe spatial and temporal techniques for the recovery of lost macroblocks. In particular, we develop estimation techniques for the reconstruction of missing macroblocks using a Markov Random Field model. We show that the widely used heuristic motion compensated error concealment technique based on averaging motion vectors is a special case of our estimation technique. We further describe a technique that can be implemented in real-time.

When compressed video is transmitted through a data network, such as an ATM or a wireless network, data can be lost due to channel errors and/or congestion. Techniques that post-process the received video and conceal the errors in real-time are needed. In this paper we describe an implementation of error concealment techniques on the Texas Instruments TMS320C6201 (‘C6201) digital signal processor.

In this paper, we present a new wavelet based rate scalable video compression algorithm. We shall refer to this new technique as the Scalable Adaptive Motion COmpensated Wavelet (SAMCoW) algorithm. SAMCoW uses motion compensation to reduce temporal redundancy. The prediction error frames and the intra-coded frames are encoded using an approach similar to the embedded zerotree wavelet (EZW) coder. observed. An adaptive motion compensation (AMC) scheme is described to address error propagation problems. We show that using our AMC scheme the quality of the decoded video can be maintained at various data rates. correlation. large transitions,  it is highly likely for the luminance signal to have large transitions. We also describe an EZW approach that exploits the interdependency between color components in the luminance/chrominance color space. We show that in addition to providing a wide range of rate scalability, our encoder achieves comparable performance to the more traditional hybrid video coders, such as MPEG1 and H.263. Furthermore, our coding scheme allows the data rate to be dynamically changed during decoding, which is very appealing for network oriented applications.

Color embedded image compression is investigated by means of a set of core experiments that seek to evaluate the advantages of various color transformations, spatial orientation trees and the use of monochrome embedded coding schemes such as EZW and SPIHT. In order to take advantage of the interdependencies of the color components for a given color space, two new spatial orientation trees that relate frequency bands and color components are investigated.

In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. The system first segments video sequences into shots by using a new feature vector known as the Generalized Trace obtained from the DC-sequence of the compressed data. Each video shot is then represented by a hierarchical structure known as the shot tree. The shots are then classified into pseudo-semantic classes that describe the shot content. Finally, the results are presented to the user in an active browsing environment using a similarity pyramid data structure. The similarity pyramid allows the user to view the video database at various levels of detail. The user can also define semantic classes and reorganize the browsing environment based on relevance feedback. We describe how ViBE performs on a database of MPEG sequences.

In this paper we extend the shot transition detection component of the ViBE video database system to include gradual scene changes. ViBE (Video Indexing and Browsing Environment), a browseable/searchable paradigm for organizing video data containing a large number of sequences, is being developed at Purdue as a testbed to explore ideas and concepts in video databases. We also present results on the performance of our cut detection algorithm using a large test set. The performance of two other techniques are compared against our method.

Pseudo-semantic labeling represents a novel approach for automatic content description of video. This information can be used in the context of a video database to improve browsing and searching. In this paper we describe our work on using face detection techniques for pseudo-semantic labeling. We present our results using a database of MPEG sequences.

In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. We describe how ViBE performs on a database of MPEG sequences.

In this paper I will not discuss the research frontiers of image and video databases but who will be the users of these systems. Questions that have not been adequately addressed by the research community is who are the users and what do they really want these systems to do? The purpose of this paper is to be controversial and to engage a debate within the research community as to where the real applications of our work lie. It should be noted that the author does not agree with every point made in this paper.