Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network

With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly rely on human operators to analyze the scene, typically composed of many object instances piled up in bulk. Helping them by robotizing the unitary extraction is a key challenge to speed up this tedious process. Whereas supervised deep learning has proven very efficient for such object-level scene understanding, e.g., generic object detection and segmentation in everyday scenes, it however requires large sets of per-pixel labeled images, that are hardly available for numerous application contexts, including industrial robotics. We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract. More precisely, we address in this paper the middle issue of object seg-mentation in top views of piles of bulk objects given a pixel location, namely seed, provided interactively by a human operator. We propose a twofold framework for generating edge-driven instance segments. First, we repurpose a state-of-the-art fully convolutional object contour detector for seed-based instance segmentation by introducing the notion of edge-mask duality with a novel patch-free and contour-oriented loss function. Second, we train one model using only synthetic scenes, instead of manually labeled training data. Our experimental results show that considering edge-mask duality for training an encoder-decoder network, as we suggest, outperforms a state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly Robotics, 10th International Workshop, Springer Proceedings in Advanced Robotics, vol 7. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly Robotics, 10th International Workshop,

Unified Image and Video Saliency Modeling

Visual saliency modeling for images and videos is treated as two independent tasks in recent computer vision literature. While image saliency modeling is a well-studied problem and progress on benchmarks like SALICON and MIT300 is slowing, video saliency models have shown rapid gains on the recent DHF1K benchmark. Here, we take a step back and ask: Can image and video saliency modeling be approached via a unified model, with mutual benefit? We identify different sources of domain shift between image and video saliency data and between different video saliency datasets as a key challenge for effective joint modelling. To address this we propose four novel domain adaptation techniques - Domain-Adaptive Priors, Domain-Adaptive Fusion, Domain-Adaptive Smoothing and Bypass-RNN - in addition to an improved formulation of learned Gaussian priors. We integrate these techniques into a simple and lightweight encoder-RNN-decoder-style network, UNISAL, and train it jointly with image and video saliency data. We evaluate our method on the video saliency datasets DHF1K, Hollywood-2 and UCF-Sports, and the image saliency datasets SALICON and MIT300. With one set of parameters, UNISAL achieves state-of-the-art performance on all video saliency datasets and is on par with the state-of-the-art for image saliency datasets, despite faster runtime and a 5 to 20-fold smaller model size compared to all competing deep methods. We provide retrospective analyses and ablation studies which confirm the importance of the domain shift modeling. The code is available at https://github.com/rdroste/unisalComment: Presented at the European Conference on Computer Vision (ECCV) 2020. R. Droste and J. Jiao contributed equally to this work. v3: Updated Fig. 5a) and added new MTI300 benchmark results to supp. materia

Privaros: A Framework for Privacy-Compliant Delivery Drones

We present Privaros, a framework to enforce privacy policies on drones. Privaros is designed for commercial delivery drones, such as the ones that will likely be used by Amazon Prime Air. Such drones visit a number of host airspaces, each of which may have different privacy requirements. Privaros provides an information flow control framework to enforce the policies of these hosts on the guest delivery drones. The mechanisms in Privaros are built on top of ROS, a middleware popular in many drone platforms. This paper presents the design and implementation of these mechanisms, describes how policies are specified, and shows that Privaros's policy specification can be integrated with India's Digital Sky portal. Our evaluation shows that a drone running Privaros can robustly enforce various privacy policies specified by hosts, and that its core mechanisms only marginally increase communication latency and power consumption

The spin label amino acid TOAC and its uses in studies of peptides: chemical, physicochemical, spectroscopic, and conformational aspects

We review work on the paramagnetic amino acid 2,2,6,6-tetramethyl-N-oxyl-4-amino-4-carboxylic acid, TOAC, and its applications in studies of peptides and peptide synthesis. TOAC was the first spin label probe incorporated in peptides by means of a peptide bond. In view of the rigid character of this cyclic molecule and its attachment to the peptide backbone via a peptide bond, TOAC incorporation has been very useful to analyze backbone dynamics and peptide secondary structure. Many of these studies were performed making use of EPR spectroscopy, but other physical techniques, such as X-ray crystallography, CD, fluorescence, NMR, and FT-IR, have been employed. The use of double-labeled synthetic peptides has allowed the investigation of their secondary structure. A large number of studies have focused on the interaction of peptides, both synthetic and biologically active, with membranes. In the latter case, work has been reported on ligands and fragments of GPCR, host defense peptides, phospholamban, and β-amyloid. EPR studies of macroscopically aligned samples have provided information on the orientation of peptides in membranes. More recent studies have focused on peptide–protein and peptide–nucleic acid interactions. Moreover, TOAC has been shown to be a valuable probe for paramagnetic relaxation enhancement NMR studies of the interaction of labeled peptides with proteins. The growth of the number of TOAC-related publications suggests that this unnatural amino acid will find increasing applications in the future