Accelerating Monte Carlo simulations with an NVIDIA® graphics processor

Modern graphics cards, commonly used in desktop computers, have evolved beyond a simple interface between processor and display to incorporate sophisticated calculation engines that can be applied to general purpose computing. The Monte Carlo algorithm for modelling photon transport in turbid media has been implemented on an NVIDIA® 8800gt graphics card using the CUDA toolkit. The Monte Carlo method relies on following the trajectory of millions of photons through the sample, often taking hours or days to complete. The graphics-processor implementation, processing roughly 110 million scattering events per second, was found to run more than 70 times faster than a similar, single-threaded implementation on a 2.67 GHz desktop computer

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study

High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with non-linear, conductance-based synapses. Emulations of these networks on the analog neuromorphic hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm ...Comment: 20 pages, 10 figures, supplement

A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries

Deterministic networks for probabilistic computing

Neural-network models of high-level brain functions such as memory recall and reasoning often rely on the presence of stochasticity. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of uncorrelated external noise. However, both in vivo and in silico, the number of noise sources is limited due to space and bandwidth constraints. Hence, neurons in large networks usually need to share noise sources. Here, we show that the resulting shared-noise correlations can significantly impair the performance of stochastic network models. We demonstrate that this problem can be overcome by using deterministic recurrent neural networks as sources of uncorrelated noise, exploiting the decorrelating effect of inhibitory feedback. Consequently, even a single recurrent network of a few hundred neurons can serve as a natural noise source for large ensembles of functional networks, each comprising thousands of units. We successfully apply the proposed framework to a diverse set of binary-unit networks with different dimensionalities and entropies, as well as to a network reproducing handwritten digits with distinct predefined frequencies. Finally, we show that the same design transfers to functional networks of spiking neurons.Comment: 22 pages, 11 figure

Interaktive Analyse von multispektralen und hyperspektralen Bilddaten

A multispectral or hyperspectral sensor captures images of high spectral resolution by dividing the light spectrum into many narrow bands. With the advent of affordable and flexible sensors, the modality is constantly widening its range of applications. This necessitates novel tools that allow general and intuitive analysis of the image data. In this work, a software framework is presented that bundles interactive visualization techniques with powerful analysis capabilities and is accessible through efficient computation and an intuitive user interface. Towards this goal, several algorithmic solutions to open problems are presented in the fields of edge detection, clustering, supervised segmentation and visualization of hyperspectral images. In edge detection, the structure of a scene can be extracted by finding discontinuities between image regions. The high dimensionality of hyperspectral data poses specific challenges for this task. A solution is proposed based on a data-driven pseudometric. The pseudometric is computed through a fast manifold learning technique and outperforms established metrics and similarity measures in several edge detection scenarios. Another approach to scene understanding in the hyperspectral or a derived feature space is data clustering. Through pixel-cluster assignment, a global segmentation of an image is obtained based on reflectance effects and materials in the scene. An established mode-seeking method provides high-quality clustering results, but is slow to compute in the hyperspectral domain. Two methods of speedup are proposed that allow computations for interactive use. A further method is proposed that finds clusters in a learned topological representation of the data manifold. Experimental results demonstrate a quick and accurate clustering of the image data without any assumptions or prior knowledge, and that the proposed methods are applicable for the extraction of material prototypes and for fuzzy clustering of reflectance effects. For supervised image analysis, an algorithm for seed-based segmentation is introduced to the hyperspectral domain. Specific segmentations can be quickly obtained by giving cues about regions to be included in or excluded from a segment. The proposed method builds on established similarity measures and the proposed data-driven pseudometric. A new benchmark is introduced to assess its performance. The aforementioned analysis methods are then combined with capable visualization techniques. A method for non-linear false-color visualization is proposed that distinguishes captured spectra in the spatial layout of the image. This facilitates the finding of relationships between objects and materials in the scene. Additionally, a visualization for the spectral distribution of an image is proposed. Raw data exploration becomes more feasible through manipulation of this plot and its link to traditional displays. The combination of false-color coding, spectral distribution plots, and traditional visualization enables a new workflow in manual hyperspectral image analysis.Multispektrale und hyperspektrale Kameras nehmen Bilder mit hoher spektraler Auflösung auf, indem das Lichtspektrum in viele schmale Bänder zerlegt wird. Durch die Verfügbarkeit von günstigen und flexiblen Bildsensoren wird die Technologie für stetig neue Anwendungen interessant. Dabei entsteht ein Bedarf für neue Werkzeuge, die eine allgemeine und intuitive Analyse der Bilder erlauben. Diese Arbeit führt ein Softwareframework ein, das interaktive Visualisierungstechniken mit performanten Analysefähigkeiten kombiniert und durch eine effiziente und intuitive graphische Oberfläche zugänglich ist. Hierfür werden algorithmische Lösungen zu bisher offenen Problemen in den Bereichen Kantendetektion, Clustering, nutzergestützte Segmentierung und Visualisierung von hyperspektralen Bildern entwickelt. Kantendetektion ermöglicht durch das Finden von Unstetigkeiten zwischen Bildbereichen, Rückschlüsse auf die Struktur der Bildszene zu ziehen. Dabei bringt die hohe spektrale Auflösung bestehende Verfahren an und über ihre Grenzen. Um diese zu verschieben, stellen wir eine datenbezogene Halbmetrik vor, die mithilfe einer Methode zum schnellen Erlernen einer Mannigfaltigkeit berechnet wird. Sie erzielt gegenüber etablierten Metriken und Ähnlichkeitsmaßen deutliche Verbesserungen in verschiedenen Kantendetektionsszenarien. Die Bildszene kann auch im hyperspektralen Raum oder einem davon abgeleiteten Datenraum mittels Datenclustering greifbar werden. Durch die Clusterzugehörigkeiten der Pixel erhält man eine globale Segmentierung des Bildes basierend auf Reflexionseffekten und Materialien in der Szene. Eine etablierte Clustering-Methode, die sehr gute Ergebnisse liefert, ist im hyperspektralen Raum nur langsam zu berechnen. Zwei Methoden zur Beschleunigung werden vorgestellt, die diese Berechnung im interaktiven Zeitrahmen ermöglichen. Eine weitere Methode wird vorgeschlagen, die eine erlernte topologische Repräsentation der von den Daten aufgespannten Mannigfaltigkeit zum Clustering nutzt. Die experimentellen Ergebnisse zeigen, dass ein schnelles und präzises Clustering der Bilddaten ohne spezifische Annahmen oder weiteres Vorwissen erreicht werden kann. Ferner wird die Extraktion von Materialprototypen sowie weiches Clustern von Reflexionseffekten ermöglicht. Um die nutzergestützte Bildanalyse zu erlauben, führen wir einen Algorithmus zur saatbasierten Segmentierung für hyperspektrale Bilder ein. Er verwendet Hinweise über Regionen, die im Segment enthalten oder von ihm ausgeschlossen sind. So kann der Nutzer rasch die gewünschte Segmentierung erzielen. Die vorgestellte Methode nutzt etablierte Ähnlichkeitsmaße sowie die neue datenbezogene Halbmetrik. Ihre Tauglichkeit wird mittels eines neu erstellten Benchmarks nachgewiesen. Schließlich werden die genannten Analysemethoden mit leistungsfähigen Visualisierungstechniken kombiniert. Eine Methode zur nichtlinearen Falschfarbendarstellung wird eingeführt, die aufgenommene Spektren in der Anordnung des Bildes differenziert und es ermöglicht, Zusammenhänge zwischen den Objekten und Materialien in der Szene zu finden. Ebenso wird eine Visualisierung der spektralen Verteilung eines Bildes eingeführt. Interaktive Manipulation dieses Plots und seine Verknüpfung mit anderen Ansichten ermöglicht die Erkundung unverarbeiteter Daten. Die Kombination von Falschfarbendarstellung, Plots der spektralen Verteilung und traditioneller Visualisierungen erlaubt einen neuen Arbeitsablauf bei der manuellen Inspektion von multispektralen und hyperspektralen Bildern

Do capuchin monkeys (Sapajus apella) use exploration to form intuitions about physical properties?

We are grateful to the Royal Zoological Society of Scotland (RZSS) and the University of St Andrews for core financial support to the RZSS Edinburgh Zoo’s Living Links Research Facility where this project was carried out.Humans’ flexible innovation relies on our capacity to accurately predict objects’ behaviour. These predictions may originate from a “physics-engine” in the brain which simulates our environment. To explore the evolutionary origins of intuitive physics, we investigate whether capuchin monkeys’ object exploration supports learning. Two capuchin groups experienced exploration sessions involving multiple copies of two objects, one object was easily opened (functional), the other was not (non-functional). We used two within-subject conditions (enrichment-then-test, and test-only) with two object sets per group. Monkeys then underwent individual test sessions where the objects contained rewards, and they choose one to attempt to open. The monkeys spontaneously explored, performing actions which yielded functional information. At test, both groups chose functional objects above chance. While high performance of the test-only group precluded us from establishing learning during exploration, this study reveals the promise of harnessing primates’ natural exploratory tendencies to understand how they see the world.Publisher PDFPeer reviewe

Edge detection in multispectral images using the n-dimensional self-organizing map

We propose a new method for performing edge detection in multi-spectral images based on the self-organizing map (SOM) concept. Previously, 1-dimensional or 2-dimensional SOMs were trained to provide a linear mapping of high-dimensional multispectral vectors. Then, edge detection was applied on that mapping. However, the 1-dimensional SOM may not converge on a suitable global order for images with rich content. Likewise, the 2-dimensional SOM intro-duces false edges due to linearization artifacts. Our method feeds the edge detector without linearization. Instead, it exploits directly the distances of SOM neurons. This avoids the aforementioned draw-backs and is more general, as a SOM of arbitrary dimensionality can be used. We show that our method achieves significantly bet-ter edge detection results than previous work on a high-resolution multispectral image database. Index Terms — Multispectral imaging, Image edge detection, Self organizing feature maps, Machine Visio