Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

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Advances in Spectral-Spatial Classification of Hyperspectral Images

This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

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Ultrasound image segmentation: a survey

The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

Automated breast cancer detection and classification using ultrasound images: A survey

https://hal.archives-ouvertes.fr/hal-00578860/document

Segmentation and classification of hyperspectral images using watershed transformation

A new color edge detector based on vector differences is proposed. The basic technique gives as its output the maximum distance between the vectors within a mask. When applied to scalar-valued images, the method reduces to the classic morphological gradient. The technique is relatively computationally efficient and can also be readily applied to other vector-valued images. To improve the performance in the presence of noise, a novel pairwise outlier rejection scheme is employed. A quantitative evaluation using Pratt's figure of merit shows the new technique to outperform other recently proposed color edge detectors. In addition, application to real images demonstrates the approach to be highly effective despite its low complexity.

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A morphological gradient approach to color edge detection

Describes a new fully motion-adaptive spatio-temporal filtering technique to reduce the speckle in ultrasound images. The advantages of this approach are demonstrated in echocardiographic boundary detection and in comparison with other techniques. The first stage of many automated echocardiographic image interpretation schemes is filtering to reduce the amount of speckle noise. The authors show how the two-dimensional least mean squares (TDLMS) filter can be configured as a motion-compensated filter for a time sequence of ultrasound images that eliminates the blurring associated with direct averaging. For an image corrupted by multiplicative speckle noise, the mode of the intensity distribution approximates the maximum likelihood estimator. In consequence, the temporal filter's output is biased towards the mode from the mean, using information contained within the speckle itself. A new adaptive algorithm for controlling the filter's convergence is also included. To evaluate performance, application to simulated, phantom, and an in vivo test sequence of the carotid artery are considered in comparison with other techniques. The effect of filtering on edges is of great importance, as these are used by subsequent image interpretation schemes. Quantitative measurements demonstrate the effectiveness of the Biased TDLMS filter, for both noise reduction and edge preservation. Echocardiographic images have a high noise content and suffer from poor contrast. Despite this challenging environment, the Biased TDLMS filter is shown to produce images that are better inputs for subsequent feature extraction. The benefits for echocardiographic images are highlighted by considering the problems of mitral valve analysis and extraction of the left atrium boundary.

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Biased motion-adaptive temporal filtering for speckle reduction in echocardiography

A technique for computing the field of short-term glacier surface motion on a local scale is presented. Time-lapsed image negatives, digitized to a high resolution, provide the raw data for the three-stage technique. First, cross-correlation is used to establish a number of candidate displacement vectors for a series of regularly spaced templates. A relaxation-labeling routine is then applied to select the most appropriate candidate vectors, according to the local flow. Novel aspects of the relaxation algorithm include a new, efficient form of the support function and the absence of a null-match category. A new development is the application of a post filter to the resultant flow field, providing suitable displacement vectors for templates that were originally unmatched and correcting vectors that are still inconsistent with the local flow. Results from an image sequence from New Zealand's Mount Cook National Park show the superiority of the technique over the maximum cross-correlation method and demonstrate the effectiveness of the post filter in improving correlation-relaxation labeling.

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Glacier surface motion computation from digital image sequences

The potential of the nasal region for expression robust 3D face recognition is thoroughly investigated by a novel five-step algorithm. First, the nose tip location is coarsely detected and the face is segmented, aligned and the nasal region cropped. Then, a very accurate and consistent nasal landmarking algorithm detects seven keypoints on the nasal region. In the third step, a feature extraction algorithm based on the surface normals of Gabor-wavelet filtered depth maps is utilised and, then, a set of spherical patches and curves are localised over the nasal region to provide the feature descriptors. The last step applies a genetic algorithm-based feature selector to detect the most stable patches and curves over different facial expressions. The algorithm provides the highest reported nasal region-based recognition ranks on the FRGC, Bosphorus and BU-3DFE datasets. The results are comparable with, and in many cases better than, many state-of-the-art 3D face recognition algorithms, which use the whole facial domain. The proposed method does not rely on sophisticated alignment or denoising steps, is very robust when only one sample per subject is used in the gallery, and does not require a training step for the landmarking algorithm.

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Nasal Patches and Curves for Expression-Robust 3D Face Recognition

Maps of the total electron content (TEC) of the ionosphere can be reconstructed using data extracted from global positioning system (GPS) signals. For historic and other sparse data sets, the reconstruction of TEC images is often performed using a multivariate interpolation technique. Although there are many interpolation methods available, only a limited number, for example kriging, have been applied to TEC data. This paper presents a quantitative comparison of various commonly used algorithms for scattered-data interpolation over a range of sparsi- ties. Techniques evaluated include a relatively new approach called Adaptive Normalized Convolution (ANC) that has not previously been applied to ionospheric reconstruction. The proposed evaluation scheme employs a quantitative methodology applied to both simulated and real TEC data. Results show that, although the performance of kriging is good in many cases, it is several times worse than the best performing techniques at some sparsities. Natural-neighbor interpolation has a better overall performance than kriging for both simulated and TEC data. Although its performance is a few percent worse than other methods for the simulated data, ANC produces the best performance for the TEC reconstructions.

/pdf/an-evaluation-of-interpolation-techniques-for-reconstructing-n842l64i4m.pdf

Adrian N. Evans

Papers

A morphological gradient approach to color edge detection

Biased motion-adaptive temporal filtering for speckle reduction in echocardiography

Glacier surface motion computation from digital image sequences

Nasal Patches and Curves for Expression-Robust 3D Face Recognition

An Evaluation of Interpolation Techniques for Reconstructing Ionospheric TEC Maps