An Optimal Scale for Edge Detection

Many problems in early vision are ill posed. Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by the regularization parameter $\\ lambda $ and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter $\\lambda $ is derived from regularization analysis for the case of small values of $\\lambda$. We also discuss the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, we use our framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise.

Feature Extraction Without Edge Detection

Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.

Implementation of a Theory of Edge Detection

This report describes the implementation of a theory of edge detection, proposed by Marr and Hildreth (1979). According to this theory, the image is first processed independently through a set of different size filters, whose shape is the Laplacian of a Gaussian, ***. Zero-crossings in the output of these filters mark the positions of intensity changes at different resolutions. Information about these zero-crossings is then used for deriving a full symbolic description of changes in intensity in the image, called the raw primal sketch. The theory is closely tied with early processing in the human visual systems. In this report, we first examine the critical properties of the initial filters used in the edge detection process, both from a theoretical and practical standpoint. The implementation is then used as a test bed for exploring aspects of the human visual system; in particular, acuity and hyperacuity. Finally, we present some preliminary results concerning the relationship between zero-crossings detected at different resolutions, and some observations relevant to the process by which the human visual system integrates descriptions of intensity changes obtained at different resolutions.

Synchrotron-laser interactions in hexagonal boron nitride: an examination of charge trapping dynamics at the boron K-edge

Poolton, Nigel; Towlson, B.M.; Hamilton, B.; Evans, D.A., (2006) 'Synchrotron-laser interactions in hexagonal boron nitride: an examination of charge trapping dynamics at the boron K-edge', New Journal of Physics 8 pp.76 RAE2008

Safe Composition of Web Communication Protocols for Extensible Edge Services

As new multi-party edge services are deployed on the Internet, application-layer protocols with complex communication models and event dependencies are increasingly being specified and adopted. To ensure that such protocols (and compositions thereof with existing protocols) do not result in undesirable behaviors (e.g., livelocks) there needs to be a methodology for the automated checking of the "safety" of these protocols. In this paper, we present ingredients of such a methodology. Specifically, we show how SPIN, a tool from the formal systems verification community, can be used to quickly identify problematic behaviors of application-layer protocols with non-trivial communication models—such as HTTP with the addition of the "100 Continue" mechanism. As a case study, we examine several versions of the specification for the Continue mechanism; our experiments mechanically uncovered multi-version interoperability problems, including some which motivated revisions of HTTP/1.1 and some which persist even with the current version of the protocol. One such problem resembles a classic degradation-of-service attack, but can arise between well-meaning peers. We also discuss how the methods we employ can be used to make explicit the requirements for hardening a protocol's implementation against potentially malicious peers, and for verifying an implementation's interoperability with the full range of allowable peer behaviors.

Parameter Sensitive Detectors

Object detection can be challenging when the object class exhibits large variations. One commonly-used strategy is to first partition the space of possible object variations and then train separate classifiers for each portion. However, with continuous spaces the partitions tend to be arbitrary since there are no natural boundaries (for example, consider the continuous range of human body poses). In this paper, a new formulation is proposed, where the detectors themselves are associated with continuous parameters, and reside in a parameterized function space. There are two advantages of this strategy. First, a-priori partitioning of the parameter space is not needed; the detectors themselves are in a parameterized space. Second, the underlying parameters for object variations can be learned from training data in an unsupervised manner. In profile face detection experiments, at a fixed false alarm number of 90, our method attains a detection rate of 75% vs. 70% for the method of Viola-Jones. In hand shape detection, at a false positive rate of 0.1%, our method achieves a detection rate of 99.5% vs. 98% for partition based methods. In pedestrian detection, our method reduces the miss detection rate by a factor of three at a false positive rate of 1%, compared with the method of Dalal-Triggs.

Learning a Family of Detectors

Object detection and recognition are important problems in computer vision. The challenges of these problems come from the presence of noise, background clutter, large within class variations of the object class and limited training data. In addition, the computational complexity in the recognition process is also a concern in practice. In this thesis, we propose one approach to handle the problem of detecting an object class that exhibits large within-class variations, and a second approach to speed up the classification processes. In the first approach, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved with using a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. For applications where explicit parameterization of the within-class states is unavailable, a nonparametric formulation of the kernel can be constructed with a proper foreground distance/similarity measure. Detector training is accomplished via standard Support Vector Machine learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the image masks for foreground objects are provided in training, the detectors can also produce object segmentation. Methods for generating a representative sample set of detectors are proposed that can enable efficient detection and tracking. In addition, because individual detectors verify hypotheses of foreground state, they can also be incorporated in a tracking-by-detection frame work to recover foreground state in image sequences. To run the detectors efficiently at the online stage, an input-sensitive speedup strategy is proposed to select the most relevant detectors quickly. The proposed approach is tested on data sets of human hands, vehicles and human faces. On all data sets, the proposed approach achieves improved detection accuracy over the best competing approaches. In the second part of the thesis, we formulate a filter-and-refine scheme to speed up recognition processes. The binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the face recognition grand challenge version 2 data set, hand shape detection and parameter estimation on a hand data set, and vehicle detection and estimation of the view angle on a multi-pose vehicle data set. On all data sets, our approach is at least five times faster than simply evaluating all foreground state hypotheses with virtually no loss in classification accuracy.

Inter-ELM evolution of the edge current density profile on the ASDEX upgrade tokamak

The sudden decrease of plasma stored energy and subsequent power deposition on the first wall of a tokamak due to edge localised modes (ELMs) is potentially detrimental to the success of a future fusion reactor. Understanding and control of ELMs is critical for the longevity of these devices and also to maximise their performance. The commonly accepted picture of ELMs posits a critical pressure gradient and current density in the plasma edge, above which coupled magnetohy drodynamic peeling-ballooning modes become unstable. Much analysis has been presented in recent years on the spatial and temporal evolution of the edge pressure gradient. However, the edge current density has typically been overlooked due to the difficulties in measuring this quantity. In this thesis, a novel method of current density recovery is presented, using the equilibrium solver CLISTE to reconstruct a high resolution equilibrium utilising both external magnetic and internal edge kinetic data measured on the ASDEX Upgrade tokamak. The evolution of the edge current density relative to an ELM crash is presented, showing that a resistive delay in the buildup of the current density is unlikely. An uncertainty analysis shows that the edge current density can be determined with an accuracy consistent with that of the kinetic data used. A comparison with neoclassical theory demonstrates excellent agreement be- tween the current density determined by CLISTE and the calculated profiles. Three ELM mitigation regimes are investigated: Type-II ELMs, ELMs sup- pressed by external magnetic perturbations, and Nitrogen seeded ELMs. In the first two cases, the current density is found to decrease as mitigation on- sets, indicating a more ballooning-like plasma behaviour. In the latter case, the flux surface averaged current density can decrease while the local current density increases, providing a mechanism to suppress both the peeling and ballooning modes.

Facet-embedded thin-film III-V edge-emitting lasers integrated with SU-8 waveguides on silicon.

A thin-film InGaAs/GaAs edge-emitting single-quantum-well laser has been integrated with a tapered multimode SU-8 waveguide onto an Si substrate. The SU-8 waveguide is passively aligned to the laser using mask-based photolithography, mimicking electrical interconnection in Si complementary metal-oxide semiconductor, and overlaps one facet of the thin-film laser for coupling power from the laser to the waveguide. Injected threshold current densities of 260A/cm(2) are measured with the reduced reflectivity of the embedded laser facet while improving single mode coupling efficiency, which is theoretically simulated to be 77%.

Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets.

Stimulated CD4(+) T lymphocytes can differentiate into effector T cell (Teff) or inducible regulatory T cell (Treg) subsets with specific immunological roles. We show that Teff and Treg require distinct metabolic programs to support these functions. Th1, Th2, and Th17 cells expressed high surface levels of the glucose transporter Glut1 and were highly glycolytic. Treg, in contrast, expressed low levels of Glut1 and had high lipid oxidation rates. Consistent with glycolysis and lipid oxidation promoting Teff and Treg, respectively, Teff were selectively increased in Glut1 transgenic mice and reliant on glucose metabolism, whereas Treg had activated AMP-activated protein kinase and were dependent on lipid oxidation. Importantly, AMP-activated protein kinase stimulation was sufficient to decrease Glut1 and increase Treg generation in an asthma model. These data demonstrate that CD4(+) T cell subsets require distinct metabolic programs that can be manipulated in vivo to control Treg and Teff development in inflammatory diseases.

ERM transactivation is up-regulated by the repression of DNA binding after the PKA phosphorylation of a consensus site at the edge of the ETS domain

The final step of the transduction pathway is the activation of gene transcription, which is driven by kinase cascades leading to changes in the activity of many transcription factors. Among these latter, PEA3/E1AF, ER81/ETV1, and ERM, members of the well conserved PEA3 group from the Ets family are involved in these processes. We show here that protein kinase A (PKA) increases the transcriptional activity of human ERM and human ETV1, through a Ser residue situated at the edge of the ETS DNA-binding domain. PKA phosphorylation does not directly affect the ERM transactivation domains but does affect DNA binding activity. Unphosphorylated wild-type ERM bound DNA avidly, whereas after PKA phosphorylation it did so very weakly. Interestingly, S367A mutation significantly reduced the ERM-mediated transcription in the presence of the kinase, and the DNA binding of this mutant, although similar to that of unphosphorylated wild-type protein, was insensitive to PKA treatment. Mutations, which may mimic a phosphorylated serine, converted ERM from an efficient DNA-binding protein to a poor DNA binding one, with inefficiency of PKA phosphorylation. The present data clearly demonstrate a close correlation between the capacity of PKA to increase the transactivation of ERM and the drastic down-regulation of the binding of the ETS domain to the targeted DNA. What we thus demonstrate here is a relatively rare transcription activation mechanism through a decrease in DNA binding, probably by the shift of a non-active form of an Ets protein to a PKA-phosphorylated active one, which should be in a conformation permitting a transactivation domain to be active.

Development of a GEM Electronic Board (GEB) for triple-GEM detectors

Developed for use with triple GEM detectors, the GEM Electronic Board (GEB) forms a crucial part of the electronics readout system being developed as part of the CMS muon upgrade program. The objective of the GEB is threefold; to provide stable powering and ground for the VFAT3 front ends, to enable high-speed communication between 24 VFAT3 front ends and an optohybrid, and to shield the GEM detector from electromagnetic interference. The paper describes the concept and design of a large-size GEB in detail, highlighting the challenges in terms of design and feasibility of this deceptively difficult system component.

Phase-accuracy comparisons and improved far-field estimates for 3-D edge elements on tetrahedral meshes

Edge-element methods have proved very effective for 3-D electromagnetic computations and are widely used on unstructured meshes. However, the accuracy of standard edge elements can be criticised because of their low order. This paper analyses discrete dispersion relations together with numerical propagation accuracy to determine the effect of tetrahedral shape on the phase accuracy of standard 3-D edgeelement approximations in comparison to other methods. Scattering computations for the sphere obtained with edge elements are compared with results obtained with vertex elements, and a new formulation of the far-field integral approximations for use with edge elements is shown to give improved cross sections over conventional formulations.