On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

Moving-edge detection via heat flow analogy

In this paper, a new and automatic moving-edge detection algorithm is proposed, based on using the heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain, to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic and linear heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving-edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary moving edges. Evaluation, on a variety of data, indicates that this approach can handle noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving-edges in image sequences, without background image subtraction

On a shape adaptive image ray transform

A conventional approach to image analysis is to perform separately feature extraction at a low level (such as edge detection) and follow this with high level feature extraction to determine structure (e.g. by collecting edge points using the Hough transform. The original image Ray Transform (IRT) demonstrated capability to extract structures at a low level. Here we extend the IRT to add shape specificity that makes it select specific shapes rather than just edges, the new capability is achieved by addition of a single parameter that controls which shape is elected by the extended IRT. The extended approach can then perform low-and high-level feature extraction simultaneously. We show how the IRT process can be extended to focus on chosen shapes such as lines and circles. We confirm the new capability by application of conventional methods for exact shape location. We analyze performance with images from the Caltech-256 dataset and show that the new approach can indeed select chosen shapes. Further research could capitalize on the new extraction ability to extend descriptive capability

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

The mean condensate heat resistance of dropwise condensation with flowing inert gases

The quantification of the condensate heat resistance is\ud studied for dropwise condensation from flowing air-steam\ud mixtures. Flows are essentially laminar and stable with gas\ud Reynolds numbers around 900 and 2000. The condensate shaping\ud up as hemispheres on a plastic plane wall and the presence\ud of inert gases make it possible that thermocapillary convection\ud occurs making the resistance less than the mean condensate\ud thickness (ca. 0.185 mm) divided by the heat conduction coefficient.\ud The analysis of experiments shows that the effective\ud mean condensate resistance might indeed be less, by a factor of\ud 0.8+0.2. The analysis takes account of the sensible heat transfer\ud which may be as large as 35% of the total heat transfer if inlet\ud vapor concentration, cin, is low (ca. 0.07). A method is presented\ud to determine the gas-condensate interface temperature,\ud ti, that is needed in the analysis of the heat resistance. The\ud highest temperature differences (t i- tw), t w being the mean\ud temperature of the condenser plate at the gas side, have been\ud found to occur for relatively high values of Cin (ca. 0.3)

On using an analogy to heat flow for shape extraction

We introduce a novel evolution-based segmentationalgorithm which uses the heat flow analogy togain practical advantage. The proposed algorithm consistsof two parts. In the first part, we represent a particular heatconduction problem in the image domain to roughly segmentthe region of interest. Then we use geometric heatflow to complete the segmentation, by smoothing extractedboundaries and removing noise inside the prior segmentedregion. The proposed algorithm is compared with activecontour models and is tested on synthetic and medicalimages. Experimental results indicate that our approachworks well in noisy conditions without pre-processing. Itcan detect multiple objects simultaneously. It is alsocomputationally more efficient and easier to control andimplement in comparison with active contour models

Fermionic reaction coordinates and their application to an autonomous Maxwell demon in the strong coupling regime

We establish a theoretical method which goes beyond the weak coupling and Markovian approximations while remaining intuitive, using a quantum master equation in a larger Hilbert space. The method is applicable to all impurity Hamiltonians tunnel-coupled to one (or multiple) baths of free fermions. The accuracy of the method is in principle not limited by the system-bath coupling strength, but rather by the shape of the spectral density and it is especially suited to study situations far away from the wide-band limit. In analogy to the bosonic case, we call it the fermionic reaction coordinate mapping. As an application we consider a thermoelectric device made of two Coulomb-coupled quantum dots. We pay particular attention to the regime where this device operates as an autonomous Maxwell demon shoveling electrons against the voltage bias thanks to information. Contrary to previous studies we do not rely on a Markovian weak coupling description. Our numerical findings reveal that in the regime of strong coupling and non-Markovianity, the Maxwell demon is often doomed to disappear except in a narrow parameter regime of small power output.Comment: 18 pages incl. references, appendix and 10 figures; accepted versio