We describe a new approach to the detection and classification of scene breaks in video sequences. Our method can detect and classify a variety of scene breaks, including cuts, fades, dissolves and wipes, even in sequences involving signi cant motion. We detect the appearance of intensity edges that are distant from edges in the previous frame. A global motion computation is used to handle camera or object motion. The algorithm we propose withstands JPEG and MPEG artifacts, even at very high compression rates. Experimental evidence demonstrates that our method can detect and classify scene breaks that are difficult to detect with previous approaches. An initial implementation runs at approximately 2 frames per second on a Sun workstation.

We present ordinal measures of association for establishing visual correspondence in images. Linear correspondence measures like correlation and the sum of squared differences are known to be fragile. Ordinal measures, which are based on relative ordering of intensity values in windows, have demonstrable robustness to depth discontinuities, occlusion, and noise. The relative ordering of intensity values in each window is represented by a rank permutation which is obtained by sorting the corresponding intensity data. By using distance metrics between the rank permutations of windows, ordinal correlation coefficients can be arrived at. These coefficients are independent of absolute intensity scale, i.e they are normalized measures. Further, since rank permutations are invariant to monotone transformations of the intensity values, the coefficients are unaffected by nonlinear effects like gamma variation between images. We discuss two crucial properties of ordinal measures for stereo appli...

We present a simple and efficient dense matching method based on region growing techniques, which can be applied to a wide range of globally textured images like many outdoor scenes. Our method can deal with non-rigid scenes and large camera motions. First a few highly distinctive features like points or areas are extracted and matched. These initial matches are then used in a correlation-based region growing step which propagates the matches in textured and more ambiguous regions of the images. The implementation of the algorithm is also given and is demonstrated on real image pairs.

High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. One of the main bottlenecks in NMR structure determination is the interpretation of NMR data to obtain a sufficient number of accurate distance restraints by assigning nuclear Overhauser effect (NOE) spectral peaks to pairs of protons. The difficulty in automated NOE assignment mainly lies in the ambiguities arising both from the resonance degeneracy of chemical shifts and from the uncertainty due to experimental errors in NOE peak positions. In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment ( hana), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue37, 39, employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each rotamer from a statistically diverse library, and drives the selection of optimal position-specific rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn3 +tn log t), where t is the maximum number of rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol η) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90 % assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 ˚A and all-heavy-atom

nuclear Overhauser and exchange spectroscopy; RDC, residual dipolar coupling; PDB, Protein Data Bank; pol η UBZ, ubiquitin-binding zinc finger domain of the human Y-family DNA polymerase Eta; CH, C α-H α; hSRI, human Set2-Rpb1 interacting domain; FF2, FF Domain 2 of human transcription elongation factor CA150 (RNA polymerase II C-terminal domain interacting protein); POF, principal order frame; SA, simulated annealing; MD, molecular dynamics; SSE, secondary structure element; C ′ , carbonyl carbon; WPS, well-packed satisfying; vdW, van der Waals. The following is supplementary material which provides additional information to substantiate the claims of the paper. Section S1 presents the flow chart of RDC-PANDA. In Section S2 we give a detailed derivation of how to compute the φ and ψ backbone dihedral angles from the RDC equations. Section S3 presents the details of extracting sparse NOEs between secondary structure elements using only chemical shift information. Section S4 gives the computational complexity for PACKER. Section S5 describes the details of the HANA algorithm. In Section S6 we present an analysis of the running time of HANA. Section S7 presents the details of the local minimization approach. In Section S8, additional details for the Results