SurF: an innovative framework in biosecurity and animal health surveillance evaluation

Surveillance for biosecurity hazards is being conducted by the New Zealand Competent Authority, the Ministry for Primary Industries (MPI) to support New Zealand's biosecurity system. Surveillance evaluation should be an integral part of the surveillance life cycle, as it provides a means to identify and correct problems and to sustain and enhance the existing strengths of a surveillance system. The surveillance evaluation Framework (SurF) presented here was developed to provide a generic framework within which the MPI biosecurity surveillance portfolio, and all of its components, can be consistently assessed. SurF is an innovative, cross‐sectoral effort that aims to provide a common umbrella for surveillance evaluation in the animal, plant, environment and aquatic sectors. It supports the conduct of the following four distinct components of an evaluation project: (i) motivation for the evaluation, (ii) scope of the evaluation, (iii) evaluation design and implementation and (iv) reporting and communication of evaluation outputs. Case studies, prepared by MPI subject matter experts, are included in the framework to guide users in their assessment. Three case studies were used in the development of SurF in order to assure practical utility and to confirm usability of SurF across all included sectors. It is anticipated that the structured approach and information provided by SurF will not only be of benefit to MPI but also to other New Zealand stakeholders. Although SurF was developed for internal use by MPI, it could be applied to any surveillance system in New Zealand or elsewhere

SenseCam image localisation using hierarchical SURF trees

The SenseCam is a wearable camera that automatically takes photos of the wearer's activities, generating thousands of images per day. Automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing semantic information with each photo, such as the wearer's location during capture time. We propose a method for automatically determining the wearer's location using an annotated image database, described using SURF interest point descriptors. We show that SURF out-performs SIFT in matching SenseCam images and that matching can be done efficiently using hierarchical trees of SURF descriptors. Additionally, by re-ranking the top images using bi-directional SURF matches, location matching performance is improved further

The problem with the SURF scheme

There is a serious problem with one of the assumptions made in the security proof of the SURF scheme. This problem turns out to be easy in the regime of parameters needed for the SURF scheme to work. We give afterwards the old version of the paper for the reader's convenience.Comment: Warning : we found a serious problem in the security proof of the SURF scheme. We explain this problem here and give the old version of the paper afterward

Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain

In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided

The calibration of photographic and spectroscopic films. The utilization of the digital image processor in the determination of aging of the surf clam (Spisula solidissima)

The age of the surf clam (Spisula solidissima) can be determined with the use of the Digital Image Processor. This technique is used in conjunction with a modified method for aging, refined by John Ropes of the Woods Hole Laboratory, Massachusetts. This method utilizes a thinned sectioned chondrophore of the surf clam which contains annual rings. The rings of the chondrophore are then counted to determine age. By digitizing the chondrophore, the Digital Image Processor is clearly able to separate these annual rings more accurately. This technique produces an easier and more efficient way to count annual rings to determine the age of the surf clam

FPGA-based module for SURF extraction

We present a complete hardware and software solution of an FPGA-based computer vision embedded module capable of carrying out SURF image features extraction algorithm. Aside from image analysis, the module embeds a Linux distribution that allows to run programs specifically tailored for particular applications. The module is based on a Virtex-5 FXT FPGA which features powerful configurable logic and an embedded PowerPC processor. We describe the module hardware as well as the custom FPGA image processing cores that implement the algorithm's most computationally expensive process, the interest point detection. The module's overall performance is evaluated and compared to CPU and GPU based solutions. Results show that the embedded module achieves comparable disctinctiveness to the SURF software implementation running in a standard CPU while being faster and consuming significantly less power and space. Thus, it allows to use the SURF algorithm in applications with power and spatial constraints, such as autonomous navigation of small mobile robots