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.
14805900
Shape-based 3D surface correspondence using geodesics and local geometry
This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points is generated using a shape-based matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach.
2653351
Fingerprint images encryption via multi-scroll chaotic attractors
This paper proposes a chaotic fingerprint images encryption approach. An image of a fingerprint is encrypted via a two-dimensional (2D) chaotic sequence obtained from multi-scroll chaotic attractors. Initial values of the chaotic attractors are served as the private key, which can be generated from the pixel distribution of the binary images of the captured fingerprints. Due to the dynamic uncertainties in the acquisition process of fingerprint images, the keys generated from the pixel value distribution are virtually random. With the elaborately designed 2D chaotic sequence, the encrypted fingerprint images have balanced 0-1 ratio and ideal nonlinearity. Only with the valid private key can the images of fingerprint be recovered. Simulation results and 2D-DFT validate this chaotic encryption approach.
28794181
JSEG Based Color Separation of Tongue Image in Traditional Chinese Medicine
The process of color separation of tongue image in traditional Chinese medicine (TCM) is decomposed into two steps: region partition and color classification. In the first step, a partition method based on JSEG is proposed to obtain homogenous regions in the tongue. In the second step, a priori template and some standard color patches are designed to assist the classification according to a priori knowledge from the TCM experts, where the nearest neighbour classifier is applied for color classification. The experimental results show that the proposed approach is greatly promising to computerized tongue diagnosis.
19020033
Estimation of multiple directional light sources for synthesis of augmented reality images
We present a new method for the detection and estimation of multiple directional illuminants, using a single image of any object with known geometry and Lambertian reflectance. We use the resulting highly accurate estimates to modify virtually the illumination and geometry of a real scene and produce correctly illuminated mixed reality images. 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. Our method combines information both from the shading of the object and from shadows cast on the scene by the object. Initially we use a method based on shadows and a method based on shading independently. The shadow based method utilizes brightness variation inside the shadows cast by the object, whereas the shading based method utilizes brightness variation on the directly illuminated portions of the object. We demonstrate how the two sources of information complement each other in a number of occasions. We then describe an approach that integrates the two methods, with results superior to those obtained if the two methods are used separately. The resulting illumination information can be used (i) to render synthetic objects in a real photograph with correct illumination effects, and (ii) to virtually re-light the scene.
28934685
Unsupervised Bilingual Word Sense Disambiguation Using Web Statistics
Word sense disambiguation has sense division and sense selection as its two sub-problems. An appropriate solution to the sense division problem is usually dependent on the application being pursued. In the context of machine translation, picking the correct translation for a word among multiple candidates, is known as target word selection. 
 
The work in this paper uses the Web as the main knowledge source to address the difficulty of making a target word selection based on statistics, which are normally drawn from rather limited corpora. The proposed approach uses simple and easily accessible web statistics–search engine hits (number of document returned for a particular query) to demonstrate the great potential of the Web as a knowledge source for word sense disambiguation. Our experimental results so far are very encouraging.
15617422
R*-Histograms: efficient representation of spatial relations between objects of arbitrary topology
Representation of relative spatial relations between objects is often required in many multimedia database applications because spatial relations between objects in an image convey important information about the image. Quantitative representation of spatial relations taking into account shape, size, orientation and distance is often required. The R-Histogram is such a quantitative representation of spatial relations between two objects. However, this method only considers pixels on the object boundary, assuming that the objects are homeomorphic to a 2-ball. For objects with more complicated topology, we propose in this paper the R*-Histogram, a new extension to the R-Histogram. The R*-Histogram generalizes the R-Histogram by taking into account all the pixels in the objects. We also introduce an efficient O(kN log N) time algorithm to compute the R*-Histogram, which is asymptotically faster than the original O(N<sup>2</sup>) time algorithm for the R-Histogram even when k=O(n). Here, N=n<sup>2</sup> denotes the number of pixels in the processed n x n image and k is the number of different directions considered. The effectiveness of the R*-Histogram is evaluated empirically with a Query By Example (QBE) system on a database of 2000 synthetic images containing objects with complicated shape and topology. Experiments have shown that the similarly search results match human intuition very well.