Precision fish farming: a new framework to improve production in aquaculture

Aquaculture production of finfish has seen rapid growth in production volume and economic yield over the last decades, and is today a key provider of seafood. As the scale of production increases, so does the likelihood that the industry will face emerging biological, economic and social challenges that may influence the ability to maintain ethically sound, productive and environmentally friendly production of fish. It is therefore important that the industry aspires to monitor and control the effects of these challenges to avoid also upscaling potential problems when upscaling production. We introduce the Precision Fish Farming (PFF) concept whose aim is to apply control-engineering principles to fish production, thereby improving the farmer's ability to monitor, control and document biological processes in fish farms. By adapting several core principles from Precision Livestock Farming (PLF), and accounting for the boundary conditions and possibilities that are particular to farming operations in the aquatic environment, PFF will contribute to moving commercial aquaculture from the traditional experience-based to a knowledge-based production regime. This can only be achieved through increased use of emerging technologies and automated systems. We have also reviewed existing technological solutions that could represent important components in future PFF applications. To illustrate the potential of such applications, we have defined four case studies aimed at solving specific challenges related to biomass monitoring, control of feed delivery, parasite monitoring and management of crowding operations

Transient biometrics using finger nails

Transient biometrics, a new concept for biometric recognition, is introduced in this paper. A traditional perspective of biometric recognition systems concentrates on biometric characteristics that are as constant as possible (such as the eye retina), giving accuracy over time but at the same time resulting in resistance to their use for non-critical applications due to the possibility of misuse. In contrast, transient biometrics is based on biometric characteristics that do change over time aiming at increased acceptance in non-critical applications. We show that the fingernail is a transient biometric with a lifetime of approximately two months. Our evaluation datasets are available to the research community. © 2013 IEEE

Unsupervised Image Partitioning with Semidefinite Programming

We apply a novel optimization technique, semidefinite programming, to the unsupervised partitioning of images. Representing images by graphs which encode pairwise (dis)similarities of local image features, a partition of the image into coherent groups is computed by determining optimal balanced graph cuts. Unlike recent work in the literature, we do not make any assumption concerning the objective criterion like metric pairwise interactions, for example. Moreover, no tuning parameter is necessary to compute the solution. We prove that, from the optimization point of view, our approach cannot perform worse than spectral relaxation approaches which, conversely, may completely fail for the unsupervised choice of the eigenvector threshold

Regularized Discrete Optimal Transport

This article introduces a generalization of the discrete optimal transport, with applications to color image manipulations. This new formulation includes a relaxation of the mass conservation constraint and a regularization term. These two features are crucial for image processing tasks, which necessitate to take into account families of multimodal histograms, with large mass variation across modes. The corresponding relaxed and regularized transportation problem is the solution of a convex optimization problem. Depending on the regularization used, this minimization can be solved using standard linear programming methods or first order proximal splitting schemes. The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes. Furthermore, the regularization of the transport plan helps to remove colorization artifacts due to noise amplification. We also extend this framework to the computation of barycenters of distributions. The barycenter is the solution of an optimization problem, which is separately convex with respect to the barycenter and the transportation plans, but not jointly convex. A block coordinate descent scheme converges to a stationary point of the energy. We show that the resulting algorithm can be used for color normalization across several images. The relaxed and regularized barycenter defines a common color palette for those images. Applying color transfer toward this average palette performs a color normalization of the input images.Sparsity, Image and Geometry to Model Adaptively Visual Processing

Semidefinite programming heuristics for surface reconstruction ambiguities

Abstract. We consider the problem of reconstructing a smooth surface under constraints that have discrete ambiguities. These problems arise in areas such as shape from texture, shape from shading, photometric stereo and shape from defocus. While the problem is computationally hard, heuristics based on semidefinite programming may reveal the shape of the surface.

Biconvex Relaxation for Semidefinite Programming in Computer Vision

Semidefinite programming (SDP) is an indispensable tool in computer vision, but general-purpose solvers for SDPs are often too slow and memory intensive for large-scale problems. Our framework, referred to as biconvex relaxation (BCR), transforms an SDP consisting of PSD constraint matrices into a specific biconvex optimization problem, which can then be approximately solved in the original, low-dimensional variable space at low complexity. The resulting problem is solved using an efficient alternating minimization (AM) procedure. Since AM has the potential to get stuck in local minima, we propose a general initialization scheme that enables BCR to start close to a global optimum---this is key for BCR to quickly converge to optimal or near-optimal solutions. We showcase the efficacy of our approach on three applications in computer vision, namely segmentation, co-segmentation, and manifold metric learning. BCR achieves solution quality comparable to state-of-the-art SDP methods with speedups between 4x and 35x.ISSN:0302-9743ISSN:1611-334