This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

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Contour Detection and Hierarchical Image Segmentation

Circular dichroism (CD) is an excellent tool for rapid determination of the secondary structure and folding properties of proteins that have been obtained using recombinant techniques or purified from tissues. The most widely used applications of protein CD are to determine whether an expressed, purified protein is folded, or if a mutation affects its conformation or stability. In addition, it can be used to study protein interactions. This protocol details the basic steps of obtaining and interpreting CD data, and methods for analyzing spectra to estimate the secondary structural composition of proteins. CD has the advantage that measurements may be made on multiple samples containing < or =20 microg of proteins in physiological buffers in a few hours. However, it does not give the residue-specific information that can be obtained by x-ray crystallography or NMR.

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Using circular dichroism spectra to estimate protein secondary structure

A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described. O-PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y. In an earlier paper, Wold et al. (Chemometrics Intell. Lab. Syst. 1998; 44: 175-185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O-PLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non-correlated variation itself can be analyzed further. As an example, near-infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O-PLS resulted in reduced model complexity with preserved prediction ability, effective removal of non-correlated variation in X and, not least, improved interpretational ability of both correlated and non-correlated variation in the NIR spectra.

Orthogonal projections to latent structures (O-PLS)

Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.

Review of the most common pre-processing techniques for near-infrared spectra

Respiration effects and cardiac pulsatility can induce signal modulations in functional MR image time series that increase noise and degrade the statistical significance of activation signals. A simple image-based correction method is described that does not have the limitations of k-space methods that preclude high spatial frequency correction. Low-order Fourier series are fit to the image data based on time of each image acquisition relative to the phase of the cardiac and respiratory cycles, monitored using a photoplethysmograph and pneumatic belt, respectively. The RETROICOR method is demonstrated using resting-state experiments on three subjects and compared with the k-space method. The method is found to perform well for both respiration- and cardiac-induced noise without imposing spatial filtering on the correction. Magn Reson Med 44:162‐167, 2000. © 2000 Wiley-Liss, Inc.

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Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR.

Smoothing and Differentiation of Data by Simplified Least Squares Procedures.

The static multislit principle is examined, and some ancillary number theoretical concepts are exposed briefly A discussion is also presented of the aberrations which limit the available field area in various spectroscopic systemsA description is given of an apparatus embodying the static multislit principle and designed to give a cathode-ray panoramic display of short segments of the infrared spectrum in the NaCl and KBr prism rangeThe rate at which information about infrared spectra can be obtained in this manner is illustrated by a photograph of the CO2 spectrum which includes both the 14μ and 15μ absorption bands, and which was obtained in 100 milliseconds

Static multislit spectrometry and its application to the panoramic display of infrared spectra.

The factors which limit the recording speed and the resolution of infra-red spectra are reviewed, and methods offering increased overall photometric efficiency are examined.The attainment of greater efficiency by means of multi-slit arrangements is discussed and the experimental results are described.

Multi-Slit Spectrometry*

Point Arrays Having Compact, Nonredundant Autocorrelations

The value 2e^{2} is calculated by approximations as a conjectured asymptotic limit for the best central to sidelobe energy ratio of very long low autocorrelation binary sequences. The same bound is calculated by approximation for skewsymmetric sequences, hence it is concluded that the requirement of skewsymmetry constitutes a powerful and effective sieve for the search for near-optimal sequences. A second sieve based on the use of complementary sequences is discussed and shown to be quite effective for sequence lengths up to 31, and its investigation for longer lengths by means of computers is suggested. A third and a fourth sieve, based on the selection of restricted classes of complementary sequences, are also discussed.

Marcel J. E. Golay

Papers

Smoothing and Differentiation of Data by Simplified Least Squares Procedures.

Static multislit spectrometry and its application to the panoramic display of infrared spectra.

Multi-Slit Spectrometry*

Point Arrays Having Compact, Nonredundant Autocorrelations

Sieves for low autocorrelation binary sequences