1746465
Pyramidal search of maximum coherence direction for biomedical image interpolation
A multiresolution technique for biomedical image interpolation is presented. It is an extension of the work on directional coherence interpolation (DCI) - a novel gray-level image interpolation method that interpolates the missing image data along the smoothed maximum coherence directions (MCDs). We propose to apply a pyramidal search strategy for MCD estimation. This coarse-to-fine scheme requires less computation time by starting with the reduced amount of data and propagating searching results to finer resolutions. In addition, it also improves robustness compared with our previous single resolution DCI.
10128966
Detecting image orientation based on low-level visual content
Accurately and automatically detecting image orientation is of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on both the luminance (structural) and chrominance (color) low-level content features. The statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated in this work. Furthermore, two rejection options (regular and re-enforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. Large amounts of experiments have been conducted on a database of more than 14,000 images to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper.
15293150
On achievable error exponents for watermarking
Probability-of-error exponents have recently been derived for watermarking systems based on spread-spectrum and quantization-index modulation methods. This paper takes this work one step further and presents minmax error exponents for any embedding scheme and any attack (subject to distortion constraints) at all rates below capacity. The decoders used are universal: they do not know the attck used. Randomized codes outperform deterministic codes, except in the case of memoryless attacks where the same performance is obtained using either kind of code.
206873031
Lossy compression of images using logic minimization
A technique for lossy compression of images is presented, utilizing ideas of logic minimization. The approach specifically addresses the compression of the binary image data originated in block truncation coding (BTC). The binary vector corresponding to a block of 4/spl times/4 pixels is treated as the output of a Boolean function and prime cubes are generated. The largest prime cube is encoded. Bit rate less than 1.5 bits/pel is attained in BTC without many perceivable errors in the reconstructed grey scale image. Training, pre-stored tables or codebooks, and prior knowledge of the image source are not required by the technique which uses simple logic operations. Computational simplicity of the algorithm makes it suitable for VLSI implementation. Potential of the technique in attaining a rate less than 0.5 bit/pel by applying it on image sequences and extending to blocks of larger size is indicated.
36989079
Sub-picture: ROI coding and unequal error protection
Region-of-interest coding and unequal error protection are two important tools in video communication systems to improve the received visual quality. One common property of the two techniques is that unequal coding or transmission is applied to improve the quality of the most important parts of images. The proposed sub-picture coding technique facilitates both region-of-interest coding and unequal error protection by partitioning images to regions of interest and separating the corresponding coded data units from each other. Simulation results show that the overall subjective quality is considerably improved compared to the conventional coding schemes.
14266211
Efficient algorithms for minimum congestion hypergraph embedding in a cycle
The minimum congestion hypergraph embedding in a cycle (MCHEC) problem is to embed the hyperedges of a hypergraph as paths in a cycle with the same node set such that the maximum congestion (the maximum number of paths that use any single edge in the cycle) is minimized. The MCHEC problem has many applications, including optimizing communication congestions in computer networks and parallel computing. The problem is NP-hard. In this paper, we give a 1.8-approximation algorithm for the MCHEC problem. This improves the previous 2-approximation results. Our algorithm has the optimal time complexity O(mn) for a hypergraph with m hyperedges and n nodes. We also propose an algorithm which finds an embedding with the optimal congestion L* for the MCHEC problem in O(n(nL*)/sup L*/) time. This improves the previous O((mn)/sup L*+1/) time algorithm.
18448866
Estimation of Multiple Illuminants from a Single Image of Arbitrary Known Geometry
We present a new method for the detection and estimation of multiple illuminants, using one image of any object with known geometry and Lambertian reflectance. Our method obviates the need to modify the imaged scene by inserting calibration objects of any particular geometry, relying instead on partial knowledge of the geometry of the scene. Thus, the recovered multiple illuminants can be used both for image-based rendering and for shape reconstruction. We first develop our method for the case of a sphere with known size, illuminated by a set of directional light sources. In general, each point of such a sphere will be illuminated by a subset of these sources. We propose a novel, robust way to segment the surface into regions, with each region illuminated by a different set of sources. The regions are separated by boundaries consisting of critical points (points where one illuminant is perpendicular to the normal). Our region-based recursive least-squares method is impervious to noise and missing data and significantly outperforms a previous boundary-based method using spheres[21]. This robustness to missing data is crucial to extending the method to surfaces of arbitrary smooth geometry, other than spheres. We map the normals of the arbitrary shape onto a sphere, which we can then segment, even when only a subset of the normals is available on the scene. We demonstrate experimentally the accuracy of our method, both in detecting the number of light sources and in estimating their directions, by testing on images of a variety of synthetic and real objects.