206750828
Directly operable image representation of multiscale primal sketch
In this paper, we propose a versatile semantics-driven image representation that can support many common operations in visual computing and communications, in addition to being itself an efficient image coding scheme. The proposed image representation is based on a semantically meaningful construct called multiscale primal sketch (MPS). The MPS consists of edges that are extracted and organized successively from fine to coarse scales. The edges are further classified into two types: pulse edge and step edge. MPS is an intermediate-level image representation, which is between pixel-based (low-level) and object-based (high-level) descriptions. The MPS image representation reaches a good compromise between its construction cost and descriptive power. It has a compact form, and hence is amenable to image compression. Furthermore, because the new representation consists of semantically meaningful primitives - edges of different scales and types, and background - many common image operations, such as classification, restoration, detection, and content-based retrieval, can be performed directly in the MPS framework, without first converting the coded image back to the spatial domain.
36354030
Globally optimal uneven error-protected packetization of scalable code streams
In this paper, we present a family of new algorithms for rate-fidelity optimal packetization of scalable source bit streams with uneven error protection. In the most general setting where no assumption is made on the probability function of packet loss or on the rate-fidelity function of the scalable code stream, one of our algorithms can find the globally optimal solution to the problem in O(N/sup 2/L/sup 2/) time, compared to a previously obtained O(N/sup 3/L/sup 2/) complexity, where N is the number of packets and L is the packet payload size. If the rate-fidelity function of the input is convex, the time complexity can be reduced to O(NL/sup 2/) for a class of erasure channels, including channels for which the probability function of losing n packets is monotonically decreasing in n and independent erasure channels with packet erasure rate no larger than N/2(N + 1). Furthermore, our O(NL/sup 2/) algorithm for the convex case can be modified to rind an approximation solution for the general case. All of our algorithms do away with the expediency of fractional bit allocation, a limitation of some existing algorithms.
29396765
Detection of LSB Steganography via Sample Pair Analysis
This paper introduces a new, principled approach to detecting LSB steganography in digital signals such as images and audio. It is shown that the length of hidden message embedded in the least significant bits of signal samples can be estimated with relatively high precision. The new steganalytic approach is based on some statistical measures of sample pairs that are highly sensitive to LSB embedding operations. The resulting detection algorithm is simple and fast. To evaluate the robustness of the proposed steganalytic approach, bounds on estimation errors are developed. Furthermore, the vulnerability of the new approach to possible attacks is also assessed, and counter measures are suggested.
17164296
Optimal context quantization in lossless compression of image data sequences
In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context C/sub t/=(X/sub t-t1/,X/sub t-t2/,...,X/sub t-tK/) of a source symbol X/sub t/ into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and /spl alpha/-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.
19420160
Multiscale LMMSE-based image denoising with optimal wavelet selection
In this paper, a wavelet-based multiscale linear minimum mean square-error estimation (LMMSE) scheme for image denoising is proposed, and the determination of the optimal wavelet basis with respect to the proposed scheme is also discussed. The overcomplete wavelet expansion (OWE), which is more effective than the orthogonal wavelet transform (OWT) in noise reduction, is used. To explore the strong interscale dependencies of OWE, we combine the pixels at the same spatial location across scales as a vector and apply LMMSE to the vector. Compared with the LMMSE within each scale, the interscale model exploits the dependency information distributed at adjacent scales. The performance of the proposed scheme is dependent on the selection of the wavelet bases. Two criteria, the signal information extraction criterion and the distribution error criterion, are proposed to measure the denoising performance. The optimal wavelet that achieves the best tradeoff between the two criteria can be determined from a library of wavelet bases. To estimate the wavelet coefficient statistics precisely and adaptively, we classify the wavelet coefficients into different clusters by context modeling, which exploits the wavelet intrascale dependency and yields a local discrimination of images. Experiments show that the proposed scheme outperforms some existing denoising methods.
2595966
Color demosaicking via directional linear minimum mean square-error estimation
Digital cameras sample scenes using a color filter array of mosaic pattern (e.g., the Bayer pattern). The demosaicking of the color samples is critical to the image quality. This paper presents a new color demosaicking technique of optimal directional filtering of the green-red and green-blue difference signals. Under the assumption that the primary difference signals (PDS) between the green and red/blue channels are low pass, the missing green samples are adaptively estimated in both horizontal and vertical directions by the linear minimum mean square-error estimation (LMMSE) technique. These directional estimates are then optimally fused to further improve the green estimates. Finally, guided by the demosaicked full-resolution green channel, the other two color channels are reconstructed from the LMMSE filtered and fused PDS. The experimental results show that the presented color demosaicking technique outperforms the existing methods both in PSNR measure and visual perception.
3547360
Accessibility tradeoffs in public transit planning
Abstract. Spatial accessibility is a critical consideration in the provision of services, both public and private. In public transit planning, accessibility is comprised of access and geographic coverage. Interestingly, these two considerations are somewhat at odds with each other. Access is important because it is the process associated with getting to and departing from the service. Such access is typically perceived of in spatial terms as the physical proximity to transit stops or stations. Additional stops along a route usually mean greater access, because a stop is more likely to be within an acceptable walking/driving standard for a larger number of people. On the other hand, more stops and greater access slow transit travel speeds, thereby decreasing the area of service reachable given a travel time budget. More stops along a route translate to greater service interruption and longer travel times. The faster the travel time, the more desirable the service. Further, if travel times become excessive, then user demand for service will decrease. All of this means that stop spacing along a route is central to accessibility, as it is a tradeoff of access (more stops) and geographic coverage (service efficiency through less stops). This paper details modeling approaches for addressing accessibility concerns in an integrated fashion. Bus-based transit service in Columbus, Ohio will be utilized to illustrate the usefulness of these approaches in transit planning.
12525108
The Layered Net Surface Problems in Discrete Geometry and Medical Image Segmentation
Efficient detection of multiple inter-related surfaces representing the boundaries of objects of interest in d-D images (d >/= 3) is important and remains challenging in many medical image analysis applications. In this paper, we study several layered net surface (LNS) problems captured by an interesting type of geometric graphs called ordered multi-column graphs in the d-D discrete space (d >/= 3 is any constant integer). The LNS problems model the simultaneous detection of multiple mutually related surfaces in three or higher dimensional medical images. Although we prove that the d-D LNS problem (d >/= 3) on a general ordered multi-column graph is NP-hard, the (special) ordered multi-column graphs that model medical image segmentation have the self-closure structures and thus admit polynomial time exact algorithms for solving the LNS problems. Our techniques also solve the related net surface volume (NSV) problems of computing well-shaped geometric regions of an optimal total volume in a d-D weighted voxel grid. The NSV problems find applications in medical image segmentation and data mining. Our techniques yield the first polynomial time exact algorithms for several high dimensional medical image segmentation problems. Experiments and comparisons based on real medical data showed that our LNS algorithms and software are computationally efficient and produce highly accurate and consistent segmentation results.
26409739
Guest Editors' Introduction
45029055
Image Segmentation with Asteroidality/Tubularity and Smoothness Constraints
Image segmentation with specific constraints has found applications in several areas such as biomedical image analysis and data mining. In this paper, we study the problem of segmenting star-shaped and smooth objects in 2-D and tubular objects in 3-D images. Image objects of these shapes are often studied in medical applications. For the 2-D case of the problem, we present an O(IJ log J) time algorithm, improving the previously best known O(IJ2M) time algorithm by a factor of time, where the size of the input 2-D image is I × J and M is the smoothness parameter with 1 ≤ M ≤ J. Our 2-D algorithm is based on a combination of dynamic programming and divide-and-conquer strategy, and on computing an optimal path in an implicitly represented graph. We also prove that a generalized version of the 3-D case of the problem is NP-hard.
8713870
Optimal Terrain Construction Problems and Applications
in Intensity-Modulated Radiation Therapy
AbstractIn this paper we study several rectilinear terrain construction
problems, which model the leaf sequencing problems in
intensity-modulated radiation therapy (IMRT).
We present a novel unified approach
based on geometric techniques for solving these terrain
construction problems. Our ideas include formulating the terrain
construction problems as computing shortest paths in a weighted directed
graph and building the graph by computing optimal bipartite matchings on various geometric objects
subject to specific constraints of each of the problems. Further,
since we need to compute optimal bipartite matchings on many sets
of geometric objects, we use techniques for computing such
matchings in a batch fashion to speed up these matching
computations. Our approach leads to the first algorithms for several leaf
sequencing problems in IMRT that are practically fast and guarantee
 an output which is optimal for a large sub-class of solutions. The previously known leaf
sequencing algorithms which are currently used in radiation therapy practice
are all heuristics that do not guarantee any good
quality of the output solutions and may run in a long time.
Our implementation
results show that our terrain construction algorithms run very
fast on real medical data (all under few seconds).