Temporally stable feature clusters for maritime object tracking in visible and thermal imagery

This paper describes a new approach to detect and track maritime objects in real time. The approach particularly addresses the highly dynamic maritime environment, panning cameras, target scale changes, and operates on both visible and thermal imagery. Object detection is based on agglomerative clustering of temporally stable features. Object extents are first determined based on persistence of detected features and their relative separation and motion attributes. An explicit cluster merging and splitting process handles object creation and separation. Stable object clus- ters are tracked frame-to-frame. The effectiveness of the approach is demonstrated on four challenging real-world public datasets.

On fusion for multispectral iris recognition

Multispectral iris recognition uses information from multiple bands of the electromagnetic spectrum to better represent certain physiological characteristics of the iris texture and enhance obtained recognition accuracy. This paper addresses the questions of single versus cross spectral performance and compares score-level fusion accuracy for different feature types, combining different wavelengths to overcome limitations in less constrained recording environments. Further it is investigated whether Doddington's “goats” (users who are particularly difficult to recognize) in one spectrum also extend to other spectra. Focusing on the question of feature stability at different wavelengths, this work uses manual ground truth segmentation, avoiding bias by segmentation impact. Experiments on the public UTIRIS multispectral iris dataset using 4 feature extraction techniques reveal a significant enhancement when combining NIR + Red for 2-channel and NIR + Red + Blue for 3-channel fusion, across different feature types. Selective feature-level fusion is investigated and shown to improve overall and especially cross-spectral performance without increasing the overall length of the iris code.

Texture, realism, performance: exploring the intersection of transtexts and the contemporary sitcom

Contemporary US sitcom is at an interesting crossroads: it has received an increasing amount of scholarly attention (e.g. Mills 2009; Butler 2010; Newman and Levine 2012; Vermeulen and Whitfield 2013), which largely understands it as shifting towards the aesthetically and narratively complex. At the same time, in the post-broadcasting era, US networks are particularly struggling for their audience share. With the days of blockbuster successes like Must See TV’s Friends (NBC 1994-2004) a distant dream, recent US sitcoms are instead turning towards smaller, engaged audiences. Here, a cult sensibility of intertextual in-jokes, temporal and narrational experimentation (e.g. flashbacks and alternate realities) and self-reflexive performance styles have marked shows including Community (NBC 2009-2015), How I Met Your Mother (CBS 2005-2014), New Girl (Fox 2011-present) and 30 Rock (NBC 2006-2013). However, not much critical attention has so far been paid to how these developments in textual sensibility in contemporary US sitcom may be influenced by, and influencing, the use of transmedia storytelling practices, an increasingly significant industrial concern and rising scholarly field of enquiry (e.g. Jenkins 2006; Mittell 2015; Richards 2010; Scott 2010; Jenkins, Ford and Green 2013). This chapter investigates this mutual influence between sitcom and transmedia by taking as its case studies two network shows that encourage invested viewership through their use of transtexts, namely How I Met Your Mother (hereafter HIMHM) and New Girl (hereafter NG). As such, it will pay particular attention to the most transtextually visible character/actor from each show: HIMYM’s Barney Stinson, played by Neil Patrick Harris, and NG’s Schmidt, played by Max Greenfield. This chapter argues that these sitcoms do not simply have their particular textual sensibility and also (happen to) engage with transmedia practices, but that the two are mutually informing and defining. This chapter explores the relationships and interplay between sitcom aesthetics, narratives and transmedia storytelling (or industrial transtexts), focusing on the use of multiple delivery channels in order to disperse “integral elements of a fiction” (Jenkins, 2006 95-6), by official entities such as the broadcasting channels. The chapter pays due attention to the specific production contexts of both shows and how these inform their approaches to transtexts. This chapter’s conceptual framework will be particularly concerned with how issues of texture, the reality envelope and accepted imaginative realism, as well as performance and the actor’s input inform and illuminate contemporary sitcoms and transtexts, and will be the first scholarly research to do so. It will seek out points of connections between two (thus far) separate strands of scholarship and will move discussions on transtexts beyond the usual genre studied (i.e. science-fiction and fantasy), as well as make a contribution to the growing scholarship on contemporary sitcom by approaching it from a new critical angle. On the basis that transmedia scholarship stands to benefit from widening its customary genre choice (i.e. telefantasy) for its case studies and from making more use of in-depth close analysis in its engagement with transtexts, the chapter argues that notions of texture, accepted imaginative realism and the reality envelope, as well as performance and the actor’s input deserve to be paid more attention to within transtext-related scholarship.

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Interactive Vector Field Feature Identification

We introduce a flexible technique for interactive exploration of vector field data through classification derived from user-specified feature templates. Our method is founded on the observation that, while similar features within the vector field may be spatially disparate, they share similar neighborhood characteristics. Users generate feature-based visualizations by interactively highlighting well-accepted and domain specific representative feature points. Feature exploration begins with the computation of attributes that describe the neighborhood of each sample within the input vector field. Compilation of these attributes forms a representation of the vector field samples in the attribute space. We project the attribute points onto the canonical 2D plane to enable interactive exploration of the vector field using a painting interface. The projection encodes the similarities between vector field points within the distances computed between their associated attribute points. The proposed method is performed at interactive rates for enhanced user experience and is completely flexible as showcased by the simultaneous identification of diverse feature types.

A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection

This paper proposes a parallel hardware architecture for image feature detection based on the Scale Invariant Feature Transform algorithm and applied to the Simultaneous Localization And Mapping problem. The work also proposes specific hardware optimizations considered fundamental to embed such a robotic control system on-a-chip. The proposed architecture is completely stand-alone; it reads the input data directly from a CMOS image sensor and provides the results via a field-programmable gate array coupled to an embedded processor. The results may either be used directly in an on-chip application or accessed through an Ethernet connection. The system is able to detect features up to 30 frames per second (320 x 240 pixels) and has accuracy similar to a PC-based implementation. The achieved system performance is at least one order of magnitude better than a PC-based solution, a result achieved by investigating the impact of several hardware-orientated optimizations oil performance, area and accuracy.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

Texture analysis based on maximum contrast walker

Recently, the deterministic tourist walk has emerged as a novel approach for texture analysis. This method employs a traveler visiting image pixels using a deterministic walk rule. Resulting trajectories provide clues about pixel interaction in the image that can be used for image classification and identification tasks. This paper proposes a new walk rule for the tourist which is based on contrast direction of a neighborhood. The yielded results using this approach are comparable with those from traditional texture analysis methods in the classification of a set of Brodatz textures and their rotated versions, thus confirming the potential of the method as a feasible texture analysis methodology. (C) 2010 Elsevier B.V. All rights reserved.

The Sphene-Centered Ocellar Texture: An Effect of Grain-Supported Flow and Melt Migration in a Hyperdense Magma Mush

The sphene-centered ocellar texture consists of leucocratic ocelli with sphene (titanite) crystals at the center, enclosed in a biotite-rich matrix. This texture has been recognized worldwide in hybrid intermediate rocks. On the basis of structural, petrological, and geochronological data from selected outcrops of the Variscan Ribadelago pluton (NW Iberian Massif), we propose that the ocelli were formed by migration and accumulation of a residual melt through a plagioclase- and biotite-dominated crystalline framework. At the late stage of crystallization, the magma acted as a hyperdense suspension and reacted to the pressure gradient caused by the regional stress field, entering the domain of grain-supported flow. Microstructures reveal that aligned crystal domains arose in the crystal framework from the shearing and compaction of the crystal mush and behaved as magmatic microshears. Relative displacement of adjacent crystal clusters along these microshears corresponded to the onset of Reynolds dilatancy that generated an expansion of the crystal mush, involving melt migration and pore aperture. The mineralogy of the ocelli, dominated by andesine and sphene, represents the composition of the migrating melt. The chemistry of this late, Ti-rich melt stems from the incongruent melting of biotite. Magmatic sphene from the ocelli yields a U-Pb age of 317 +/- 1 Ma, which represents the final crystallization of the hybridized magmatic system. Moreover, this texture offers an opportunity to better understand the rheological behavior of highly crystallized magmas.

Quartz recrystallization regimes, c-axis texture transitions and fluid inclusion reequilibration in a prograde greenschist to amphibolite facies mylonite zone (Ribeira Shear Zone, SE Brazil)

The crystal-plastic behavior of quartz mylonites from the Ribeira Shear Zone (SE Brazil), a major strike-slip structure that was active during a prograde metamorphic phase related to the Neoproterozoic Brasiliano-Pan African Orogeny, was investigated using a multi-method approach. Geothermobarometry results indicate deformational conditions ranging from similar to 300 to similar to 630 degrees C and 500-700 MPa. A strong correlation between mapped metamorphic zones and a dominance of different dynamic recrystallization mechanisms of quartz occurs within the mylonite zone. Bulging recrystallization (BLG) dominates within the chlorite zone between 300 and 410 degrees C, subgrain rotation recrystallization (SGR) operates within the biotite zone from 410 to 520 degrees C, and grain boundary migration recrystallization (GBM) dominates in the garnet zone above 520 degrees C. The development of quartz c-axis textures is mainly governed by temperature and dynamic recrystallization mechanisms. Textures from BLG zone mylonites are characterized by maxima around Z; SGR zone mylonites display single girdles or asymmetric type I crossed girdles; and GBM zone mylonites comprise maxima around Y and intermediate between X and Z. The scarcity or absence of water-bearing fluid inclusions in quartz mylonites from the SGR and GBM zones, which are dominated by carbonic inclusions, suggests water-deficient conditions, whereas BLG zone mylonites are dominated by water-bearing inclusions. This evidence indicates that water was available in the protoliths but has been eliminated with increasing deformation and deformation temperature. No effect of the water content variation on the quartz microstructural and recrystallized grain size evolution was detected, and little influence on c-axis texture development was observed. Most of the fluid inclusion densities were reequilibrated during the shear zone exhumation history, recording a decompression in the range of 300-500 MPa, while microstructural reequilibration effects related to the prograde metamorphism are largely preserved. Fluid inclusion microstructures and densities from two SGR zone samples preserved evidence for a near isothermal compression within the interior of the Ribeira Shear Zone during the prograde metamorphism. (C) 2009 Elsevier B.V. All rights reserved.

Alkaline magmatism in the Amambay area, NE Paraguay: The Cerro Sarambi complex

The Early Cretaceous alkaline magmatism in the northeastern region of Paraguay (Amambay Province) is represented by stocks, plugs, dikes, and dike swarms emplaced into Carboniferous to Triassic-Jurassic sediments and Precambrian rocks. This magmatism is tectonically related to the Ponta Pora Arch, a NE-trending structural feature, and has the Cerro Sarambi and Cerro Chiriguelo carbonatite complexes as its most significant expressions. Other alkaline occurrences found in the area are the Cerro Guazu and the small bodies of Cerro Apua, Arroyo Gasory, Cerro Jhu, Cerro Tayay, and Cerro Teyu. The alkaline rocks comprise ultramafic-mafic, syenitic, and carbonatitic petrographic associations in addition to lithologies of variable composition and texture occurring as dikes; fenites are described in both carbonatite complexes. Alkali feldspar and clinopyroxene, ranging from diopside to aegirine, are the most abundant minerals, with feldspathoids (nepheline, analcime), biotite, and subordinate Ti-rich garnet; minor constituents are Fe-Ti oxides and cancrinite as the main alteration product from nepheline. Chemically, the Amambay silicate rocks are potassic to highly potassic and have miaskitic affinity, with the non-cumulate intrusive types concentrated mainly in the saturated to undersaturated areas in silica syenitic fields. Fine-grained rocks are also of syenitic affiliation or represent more mafic varieties. The carbonatitic rocks consist dominantly of calciocarbonatites. Variation diagrams plotting major and trace elements vs. SiO(2) concentration for the Cerro Sarambi rocks show positive correlations for Al(2)O(3), K(2)O, and Rb, and negative ones for TiO(2), MgO, Fe(2)O(3), CaO, P(2)O(5), and Sr, indicating that fractional crystallization played an important role in the formation of the complex. Incompatible elements normalized to primitive mantle display positive spikes for Rb, La, Pb, Sr, and Sm, and negative for Nb-Ta, P, and Ti, as these negative anomalies are considerably more pronounced in the carbonatites. Chondrite-normalized REE patterns point to the high concentration of these elements and to the strong LRE/HRE fractionation. The Amambay rocks are highly enriched in radiogenic Sr and have T(DM) model ages that vary from 1.6 to 1.1 Ga. suggesting a mantle source enriched in incompatible elements by metasomatic events in Paleo-Mesoproterozoic times. Data are consistent with the derivation of the Cerro Sarambi rocks from a parental magma of lamprophyric (minette) composition and suggest an origin by liquid immiscibility processes for the carbonatites. (C) 2011 Elsevier Ltd. All rights reserved.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.