Seafloor habitat characterization, classification, and maps for the lower Piscataqua River estuary

Data from multibeam echosounders were used to implement segmentations and classifications of the seafloor, and measurements from underwater images and physical samples were used to relate segmentations and predictions to observed seafloor characteristics. Texture analysis, using local Fourier histogram (LFH) texture features, was applied to multibeam bathymetry data in unsupervised- and supervised-classification modes. Unsupervised classification of bathymetry using texture features produced segmentations that corresponded to known spatial distributions of seafloor sediments, but required arbitrary choices for some parameter values and, therefore, included potential bias. Supervised classification of bathymetric texture overcame bias related to arbitrarily-chosen parameters and produced classifications that corresponded well with identified seafloor habitats, but accuracy of supervised-classification results depended on detailed and accurately-georeferenced ground-truth data. The LFH texture feature classification technique, using only gridded bathymetric data, was generally effective for predicting spatial distributions of seafloor morphologies and habitat structure classes on a per-grid-cell basis and was robust to data noise. In some cases, different substrates had similar morphologies, and in these cases, texture alone was inadequate for discriminating habitats. Using acoustic backscatter strength in addition to texture or roughness sometimes facilitated discrimination of habitats with distinct substrates and similar morphologies. Microtopographical roughness influences high frequency acoustic scattering, and roughness measurements can facilitate modeling and interpretation of backscatter data. Spectra model parameters (slope and intercept) were calculated to describe roughness of seafloor microtopography in sediment profile images (SPI). SPI spectral-model parameters were consistent with published estimates for data from other devices such as stereophotographs, and values varied by sedimentary facies and bioturbational regime. Traditional methods of ground truthing were not always sufficient for characterizing attributes of features seen in shallow-water multibeam data. Seafloor video-image mosaics were used to characterize biogenic features and verify transitions between habitats and allowed descriptions of features that were not determinable from other imagery

The histogram of partitioned localized image textures

In the field of machine learning and pattern recognition, texture has been a prominent area of research. Humans are uniquely equipped to distinguish texture; however, computers are more equipped to automate the process. Computers accomplish this by taking images and extracting meaningful features that describe their texture. Some of these features are the Haralick texture features, local binary pattern (LBP), and the local direction pattern (LDP). Using the local directional pattern as an example, we propose a new texture feature called the histogram of partitioned localized image textures (HoPLIT). This feature utilizes a set of filters, not necessarily directional, and generates filter response vectors at every pixel location. These response vectors can be thought of as words in a document, which causes one to think of the bag-of-words model. Using the bag-of-words model, a codebook is created by partitioning a subset of response vectors from the entire data set. The partitions are represented by their mean texture and thus a word in the codebook. The mean textures now represent the keywords within the document, i.e. image. A histogram descriptor for an image is the frequency of pixels that belong to each partition. This feature is applied to a texture classification and segmentation problem as well as object detection. Within each problem domain, the HoPLIT feature is compared to the Haralick texture features, LBP, and LDP. The HoPLIT feature does very well classifying texture as well as segmenting large texture mosaics. HoPLIT also shows a surprising robustness to noise. Object detection proves to be slightly more difficult than texture classification for HoPLIT. However, it continues to outperform LBP and LDP.Field of study: Electrical and computer engineering.|James M. Keller, Ph.D., Thesis Supervisor.Includes bibliographical references (pages 54-58)

Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor

In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification

A multiresolution approach to automated classification of protein subcellular location images

<p>Abstract</p> <p>Background</p> <p>Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.</p> <p>Results</p> <p>We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR) decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%.</p> <p>Conclusion</p> <p>We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.</p

Extracting texture features for time series classification

Time series are present in many pattern recognition applications related to medicine, biology, astronomy, economy, and others. In particular, the classification task has attracted much attention from a large number of researchers. In such a task, empirical researches has shown that the 1-Nearest Neighbor rule with a distance measure in time domain usually performs well in a variety of application domains. However, certain time series features are not evident in time domain. A classical example is the classification of sound, in which representative features are usually present in the frequency domain. For these applications, an alternative representation is necessary. In this work we investigate the use of recurrence plots as data representation for time series classification. This representation has well-defined visual texture patterns and their graphical nature exposes hidden patterns and structural changes in data. Therefore, we propose a method capable of extracting texture features from this graphical representation, and use those features to classify time series data. We use traditional methods such as Grey Level Co-occurrence Matrix and Local Binary Patterns, which have shown good results in texture classification. In a comprehensible experimental evaluation, we show that our method outperforms the state-ofthe-art methods for time series classification.CNPqFAPESP (grants #2011/17698-5, #2012/07295-3, #2012/50714-7 and #2013/23037-7