UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition

Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition. In principle, techniques like deblurring and super-resolution should yield improvements by de-emphasizing noise and increasing signal in an input image. But the historically divergent goals of the computational photography and visual recognition communities have created a significant need for more work in this direction. To facilitate new research, we introduce a new benchmark dataset called UG^2, which contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 160,000 annotated frames forhundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches. Further, current image restoration and enhancement techniques are evaluated by determining whether or not theyimprove baseline classification performance. Results showthat there is plenty of room for algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset: https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or

Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

Principles for Fairness and Efficiency in Enhancing Environmental Services in Asia: Payments, Compensation, or Co-Investment?

The term payments for environmental services (PES) has rapidly gained popularity, with its focus on market-based mechanisms for enhancing environmental services (ES). Current use of the term, however, covers a broad spectrum of interactions between ES suppliers and beneficiaries. A broader class of mechanisms pursues ES enhancement through compensation or rewards. Such mechanisms can be analyzed on the basis of how they meet four conditions: realistic, conditional, voluntary, and pro-poor. Based on our action research in Asia in the Rewarding Upland Poor for Environmental Services (RUPES) program since 2002, we examine three paradigms: commoditized ES (CES), compensation for opportunities skipped (COS), and co-investment in (environmental) stewardship (CIS). Among the RUPES action research sites, there are several examples of CIS with a focus on assets (natural + human + social capital) that can be expected to provide future flows of ES. CES, equivalent to a strict definition of PES, may represent an abstraction rather than a current reality. COS is a challenge when the legality of opportunities to reduce ES is contested. The primary difference between CES, COS, and CIS is the way in which conditionality is achieved, with additional variation in the scale (individual, household, or community) at which the voluntary principle takes shape. CIS approaches have the greatest opportunity to be pro-poor, as both CES and COS presuppose property rights that the rural poor often do not have. CIS requires and reinforces trust building after initial conflicts over the consequences of resource use on ES have been clarified and a realistic joint appraisal is obtained. CIS will often be part of a multiscale approach to the regeneration and survival of natural capital, alongside respect and appreciation for the guardians and stewards of landscapes

Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow

We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricular cavities. This leads to the generation of time series of the radius and thickness of myocardial segments to represent cardiac motion. These time series of motion features are reliable and explainable characteristics of pathological cardiac motion. Furthermore, they are combined with shape-related features to classify cardiac pathologies. Using only nine feature values as input, we propose an explainable, simple and flexible model for pathology classification. On ACDC training set and testing set, the model achieves 95% and 94% respectively as classification accuracy. Its performance is hence comparable to that of the state-of-the-art. Comparison with various other models is performed to outline some advantages of our model

Performance Pressure and Resource Allocation in Washington

Based on interviews with state, district, and school officials, explores how performance pressures have changed resource allocation decisions. Examines reform goals and how Washington's finance system impedes efforts to link resources to student learning