Capturing natural-colour 3D models of insects for species discovery

Collections of biological specimens are fundamental to scientific understanding and characterization of natural diversity. This paper presents a system for liberating useful information from physical collections by bringing specimens into the digital domain so they can be more readily shared, analyzed, annotated and compared. It focuses on insects and is strongly motivated by the desire to accelerate and augment current practices in insect taxonomy which predominantly use text, 2D diagrams and images to describe and characterize species. While these traditional kinds of descriptions are informative and useful, they cannot cover insect specimens "from all angles" and precious specimens are still exchanged between researchers and collections for this reason. Furthermore, insects can be complex in structure and pose many challenges to computer vision systems. We present a new prototype for a practical, cost-effective system of off-the-shelf components to acquire natural-colour 3D models of insects from around 3mm to 30mm in length. Colour images are captured from different angles and focal depths using a digital single lens reflex (DSLR) camera rig and two-axis turntable. These 2D images are processed into 3D reconstructions using software based on a visual hull algorithm. The resulting models are compact (around 10 megabytes), afford excellent optical resolution, and can be readily embedded into documents and web pages, as well as viewed on mobile devices. The system is portable, safe, relatively affordable, and complements the sort of volumetric data that can be acquired by computed tomography. This system provides a new way to augment the description and documentation of insect species holotypes, reducing the need to handle or ship specimens. It opens up new opportunities to collect data for research, education, art, entertainment, biodiversity assessment and biosecurity control.Comment: 24 pages, 17 figures, PLOS ONE journa

A USB3.0 FPGA Event-based Filtering and Tracking Framework for Dynamic Vision Sensors

Dynamic vision sensors (DVS) are frame-free sensors with an asynchronous variable-rate output that is ideal for hard real-time dynamic vision applications under power and latency constraints. Post-processing of the digital sensor output can reduce sensor noise, extract low level features, and track objects using simple algorithms that have previously been implemented in software. In this paper we present an FPGA-based framework for event-based processing that allows uncorrelated-event noise removal and real-time tracking of multiple objects, with dynamic capabilities to adapt itself to fast or slow and large or small objects. This framework uses a new hardware platform based on a Lattice FPGA which filters the sensor output and which then transmits the results through a super-speed Cypress FX3 USB microcontroller interface to a host computer. The packets of events and timestamps are transmitted to the host computer at rates of 10 Mega events per second. Experimental results are presented that demonstrate a low latency of 10us for tracking and computing the center of mass of a detected object.Ministerio de Economía y Competitividad TEC2012-37868-C04-0

Can graph-cutting improve microarray gene expression reconstructions?

Microarrays produce high-resolution image data that are, unfortunately, permeated with a great deal of “noise” that must be removed for precision purposes. This paper presents a technique for such a removal process. On completion of this non-trivial task, a new surface (devoid of gene spots) is subtracted from the original to render more precise gene expressions. The graph-cutting technique as implemented has the benefits that only the most appropriate pixels are replaced and these replacements are replicates rather than estimates. This means the influence of outliers and other artifacts are handled more appropriately (than in previous methods) as well as the variability of the final gene expressions being considerably reduced. Experiments are carried out to test the technique against commercial and previously researched reconstruction methods. Final results show that the graph-cutting inspired identification mechanism has a positive significant impact on reconstruction accuracy

A virtual world of paleontology

Computer-aided visualization and analysis of fossils has revolutionized the study of extinct organisms. Novel techniques allow fossils to be characterized in three dimensions and in unprecedented detail. This has enabled paleontologists to gain important insights into their anatomy, development, and preservation. New protocols allow more objective reconstructions of fossil organisms, including soft tissues, from incomplete remains. The resulting digital reconstructions can be used in functional analyses, rigorously testing long-standing hypotheses regarding the paleobiology of extinct organisms. These approaches are transforming our understanding of long-studied fossil groups, and of the narratives of organismal and ecological evolution that have been built upon them

Pre | Digital Liminalities: A Hermeneutics of the Intermedial and Materiality in the Print Intermedial Novel

How can print novels renew a digitally literate readers awareness of media materiality and medial differences? This dissertation develops a hermeneutics capable of analyzing media not only for their unique specificities or their convergence into a represented sameness, but also for the liminal site of their fusions, exchanges, and slippages of representationthe intermedial, or, the in between of media. Digital media amplify the mediating practices of exact recording devices such as the camera, representing other media through a technique that blurs their differences and inciting an illusion of a represented mediums presence or immediacy. The pretense that predigital (and thus old) media are available for such convergence occurs through the weakened instantiation of medias contexts and conditions of materiality. A closer examination of the way media engage with each other from the perspective of a so-called old mediumthe print novelprovides the grounds for an approach to media encounters that avoids sameness and encourages material awareness. This dissertation focuses on the contemporary print intermedial novel, a form of writing where references to three stages of technological inscription abound: the textual symbolic stage in the written word; the orthographic in exact recording devices; and the post-orthographic in digital media. The intermedial novels that I study use prints material medium to evoke these inscription functions, demonstrating the collaboration of older and newer media representational forms in todays cultural imagination and practice. First, I examine how orthographic immediacy is used to elicit absent objects as physically present. Next, I explore how post-orthographic immediacy is used to represent symbolic images as visualized and virtual objects. Finally, I analyze how post-orthographic inscription is used to complicate the relationship between material objects and representations of reality. Print in intermedial novels can restore a readers awareness of media materiality by creating what I call a dynamic media juxtaposition: a dissonance between the material medium of print and the visual and digital media that it represents. These novels thus demonstrate the need for an intermedial hermeneutics that is capable of analyzing this dissonance laterally (a comparative reading of media) and dynamically (a negotiation of their encounters)

Multi-modal dictionary learning for image separation with application in art investigation

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component models features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data - taken from digital acquisition of the Ghent Altarpiece (1432) - confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.Comment: submitted to IEEE Transactions on Images Processin