Open Set Logo Detection and Retrieval

Current logo retrieval research focuses on closed set scenarios. We argue that the logo domain is too large for this strategy and requires an open set approach. To foster research in this direction, a large-scale logo dataset, called Logos in the Wild, is collected and released to the public. A typical open set logo retrieval application is, for example, assessing the effectiveness of advertisement in sports event broadcasts. Given a query sample in shape of a logo image, the task is to find all further occurrences of this logo in a set of images or videos. Currently, common logo retrieval approaches are unsuitable for this task because of their closed world assumption. Thus, an open set logo retrieval method is proposed in this work which allows searching for previously unseen logos by a single query sample. A two stage concept with separate logo detection and comparison is proposed where both modules are based on task specific CNNs. If trained with the Logos in the Wild data, significant performance improvements are observed, especially compared with state-of-the-art closed set approaches.Comment: accepted at VISAPP 201

Kontextmodelle für lokale Merkmale zur inhaltsbasierten Bildsuche in großen Bilddatenbanken

Vor allem seit Smartphones für viele zum ständigen Begleiter geworden sind, wächst die Menge der aufgenommenen Bilder rasant an. Oft werden die Bilder schon unmittelbar nach der Aufnahme über soziale Netzwerke mit anderen geteilt. Zur späteren Verwendung der Aufnahmen hingegen wird es zunehmend wichtiger, die für den jeweiligen Zweck relevanten Bilder in der Masse wiederzufinden. Für viele bekannte Objektklassen ist die automatische Verschlagwortung mit entsprechenden Detektionsverfahren bereits eine große Hilfe. Anhand der Metadaten können außerdem häufig Ort oder Zeit der gesuchten Aufnahmen eingegrenzt werden. Dennoch führt in bestimmten Fällen nur eine inhaltsbasierte Bildsuche zum Ziel, da dort explizit mit einem Anfragebild nach individuellen Objekten oder Szenen gesucht werden kann. Obwohl die Forschung im Bereich der inhaltsbasierten Bildsuche im letzten Jahrzehnt bereits zu vielen Anwendungen geführt hat, ist die Skalierbarkeit der sehr genauen Varianten noch eingeschränkt. Das bedeutet, dass die existierenden Verfahren, mit denen ein Bildpaar robust auf lokal ähnliche Teilinhalte untersucht werden kann, nicht ohne weiteres auf die Suche in vielen Millionen von Bildern ausgeweitet werden können. Diese Dissertation widmet sich dieser Art der inhaltsbasierten Bildsuche, die Bilder anhand ihrer lokalen Bildmerkmale indexiert, und adressiert zwei wesentliche Einschränkungen des populären Bag-of-Words-Modells. Zum einen sind die Quantisierung und Komprimierung der lokalen Merkmale, die für die Suchgeschwindigkeit in großen Bildmengen essentiell sind, mit einem gewissen Verlust von Detailinformation verbunden. Zum anderen müssen die indexierten Merkmale aller Bilder immer im Arbeitsspeicher vorliegen, da jede Suchanfrage den schnellen Zugriff auf einen beträchtlichen Teil des Index erfordert. Konkret beschäftigt sich die Arbeit mit Repräsentationen, die im Index nicht nur die quantisierten Merkmale, sondern auch ihren Kontext einbeziehen. Abweichend zu den bisher üblichen Ansätzen, wird der Kontext, also die größere Umgebung eines lokalen Merkmals, als eigenständiges Merkmal erfasst und ebenfalls quantisiert, was den Index um eine Dimension erweitert. Zunächst wird dafür ein Framework für die Evaluation solcher Umgebungsrepräsentationen entworfen. Anschließend werden zwei Repräsentationen vorgeschlagen: einerseits basierend auf den benachbarten lokalen Merkmalen, die mittels des Fisher Vektors aggregiert werden, andererseits auf Basis der Ergebnisse von Faltungsschichten von künstlichen neuronalen Netzen. Nach einem Vergleich der beiden Repräsentationen sowie Kombinationen davon im Rahmen des Evaluationsframeworks, werden die Vorteile für ein Gesamtsystem der inhaltsbasierten Bildsuche anhand von vier öffentlichen Datensätzen bewertet. Für die Suche in einer Million Bildern verbessern die vorgeschlagenen Repräsentationen auf Basis der neuronalen Netze die Suchergebnisse des Bag-of-Words-Modells deutlich. Da die zusätzliche Indexdimension einen effektiveren Zugriff auf die indexierten Merkmale ermöglicht, wird darüber hinaus eine neue Realisierung des Gesamtsystems vorgeschlagen. Das System ist bezüglich des Index nicht mehr auf den Arbeitsspeicher angewiesen, sondern kann von aktuellen nichtflüchtigen Speichermedien profitieren, etwa von SSD-Laufwerken. Von der Kombination der vorgeschlagenen Umgebungsrepräsentation der lokalen Merkmale und der Realisierung mit großen und günstigen SSD-Laufwerken können bereits heutige Systeme profitieren, denn sie können dadurch noch größere Bilddatenbanken für die inhaltsbasierte Bildsuche zugänglich machen

A Review of the NASA MLAS Flight Demonstration

The NASA Engineering and Safety Center (NESC) has tested the Max Launch Abort System (MLAS) as a risk-mitigation design should problems arise with the baseline Orion spacecraft launch abort design. The Max in MLAS is not Maximum, but rather dedicated to Max Faget, the renowned NASA Spacecraft designer. In July 2009, the mission was flown, with great success, from the NASA Wallops Flight Facility. The MLAS flight test vehicle prototype consists of a boost skirt, coast skirt, and the MLAS fairing itself, which houses an Orion Command Module (CM) boilerplate. The objective of the MLAS flight test is to reorient the fairing with the CM, weighing approximately 29,000 lbs and traveling 290 fps, 180 degrees to an orientation suitable for the release of the CM during a pad abort or low altitude abort. The boost and coast skirts provide the necessary thrust and stability to establish the flight test conditions and are released prior to the reorientation of the fairing. A secondary test objective after successful release of the CM from the fairing is to demonstrate the removal of the CM forward bay cover (FBC) with the CM drogue parachutes, and subsequent deployment of the CM main parachutes attached to the FBC. Although multiple parachute deployments are used in the MLAS flight test vehicle to complete its objective, there are only two parachute types employed in the flight test. Five of the nine parachutes used for MLAS are 27.6 ft DO ribbon parachutes already proven as a spin/stall parachute for military aircraft, and the remaining four are G-12 cargo parachutes modified for increased strength and reefing. This paper presents an overview of the 27.6 ft DO ribbon parachute system employed on the MLAS flight test vehicle for coast skirt separation, fairing reorientation, and as CM drogue parachutes. Discussion will include: the process used to select this design; descriptions of all components of the parachute system; the minor modifications necessary to adapt the parachute to the MLAS program; the techniques used to analyze the parachute for the multiple roles it performs including discussions of how the evolution of the program affected parachute usage and analysis; a summary of the results of the highly successful flight test, including video of the flight test; and an overview of the subsequent post-test analysis

Visualizing the Kinematics of Planet Formation

A stunning range of substructures in the dust of protoplanetary disks is routinely observed across a range of wavelengths. These gaps, rings and spirals are highly indicative of a population of unseen planets, hinting at the possibility of current observational facilities being able to capture planet-formation in action. Over the last decade, our understanding of the influence of a young planet on the dynamical structure of its parental disk has progressed significantly, revealing a host of potentially observable features which would betray the presence of a deeply embedded planet. In concert, recent observations have shown that subtle perturbations in the kinematic structure of protoplanetary disks are found in multiple sources, potentially the characteristic disturbances associated with embedded planets. In this work, we review the theoretical background of planet-disk interactions, focusing on the kinematical features, and the current methodologies used to observe these interactions in spatially and spectrally resolved observations. We discuss the potential pit falls of such kinematical detections of planets, providing best-practices for imaging and analysing interferometric data, along with a set of criteria to use as a benchmark for any claimed detection of embedded planets. We finish with a discussion on the current state of simulations in regard to planet-disk interactions, highlighting areas of particular interest and future directions which will provide the most significant impact in our search for embedded planets. This work is the culmination of the 'Visualizing the Kinematics of Planet Formation' workshop, held in October 2019 at the Center for Computational Astrophysics at the Flatiron Institute in New York City.Comment: To be submitted to PASA. Comments welcom

Nucleolar Accumulation of RNA Binding Proteins Induced by ActinomycinD Is Functional in Trypanosoma cruzi and Leishmania mexicana but Not in T. brucei

We have recently shown in T. cruzi that a group of RNA Binding Proteins (RBPs), involved in mRNA metabolism, are accumulated into the nucleolus in response to Actinomycin D (ActD) treatment. In this work, we have extended our analysis to other members of the trypanosomatid lineage. In agreement with our previous study, the mechanism seems to be conserved in L. mexicana, since both endogenous RBPs and a transgenic RBP were relocalized to the nucleolus in parasites exposed to ActD. In contrast, in T. brucei, neither endogenous RBPs (TbRRM1 and TbPABP2) nor a transgenic RBP from T. cruzi were accumulated into the nucleolus under such treatment. Interestingly, when a transgenic TbRRM1was expressed in T. cruzi and the parasites exposed to ActD, TbRRM1 relocated to the nucleolus, suggesting that it contains the necessary sequence elements to be targeted to the nucleolus. Together, both experiments demonstrate that the mechanism behind nucleolar localization of RBPs, which is present in T. cruzi and L. mexicana, is not functional in T. brucei, suggesting that it has been lost or retained differentially during the evolution of the trypanosomatid lineage

The evolution of mammalian brain size

Relative brain size has long been considered a reflection of cognitive capacities and has played a fundamental role in developing core theories in the life sciences. Yet, the notion that relative brain size validly represents selection on brain size relies on the untested assumptions that brain-body allometry is restrained to a stable scaling relationship across species and that any deviation from this slope is due to selection on brain size. Using the largest fossil and extant dataset yet assembled, we find that shifts in allometric slope underpin major transitions in mammalian evolution and are often primarily characterized by marked changes in body size. Our results reveal that the largest-brained mammals achieved large relative brain sizes by highly divergent paths. These findings prompt a reevaluation of the traditional paradigm of relative brain size and open new opportunities to improve our understanding of the genetic and developmental mechanisms that influence brain size

Camera-based forecasting of insolation for solar systems

With the transition towards renewable energies, electricity suppliers are faced with huge challenges. Especially the increasing integration of solar power systems into the grid gets more and more complicated because of their dynamic feed-in capacity. To assist the stabilization of the grid, the feed-in capacity of a solar power system within the next hours, minutes and even seconds should be known in advance. In this work, we present a consumer camera-based system for forecasting the feed-in capacity of a solar system for a horizon of 10 seconds. A camera is targeted at the sky and clouds are segmented, detected and tracked. A quantitative prediction of the insolation is performed based on the tracked clouds. Image data as well as truth data for the feed-in capacity was synchronously collected at one Hz using a small solar panel, a resistor and a measuring device. Preliminary results demonstrate both the applicability and the limits of the proposed system

Person detection in LWIR imagery using image retrieval

This paper addresses the detection and localization of persons in LWIR imagery which is useful especially in visual surveillance tasks such as intruder detection in military camps or for gaining situational awareness. A robust image retrieval function is used after a previous hot spot detection and localization step in LWIR using a suitable, extensive image data base that covers a variety of different shapes and appearances of persons in LWIR. The basic idea behind this approach is, in contrast to the visual optical band (VIS), that persons in thermal infrared exhibit somehow similar, weakly individualized signatures which can be matched to a sufficient degree to images in the data base and, thus, can be distinguished from background structures and other objects. Dedicated pre and post processing routines optimize the results and compensate for a possibly occuring lack of image features needed by the image retrieval function. The achieved results document the practical benefit and the robustness of the presented aproach

Extending the bag-of-words representation with neighboring local features and deep convolutional features

In this work, we propose and compare two methods to extend the bag-of-words representation which is still widely used in the domain of content-based image retrieval where a query image is used to search for those images in a large image database that show the same object or scene. To this end, typically, local features such as SIFT are quantized and treated independently to leverage an inverted file indexing scheme for speedup. As the quantization of local features impairs their discriminability, the ability to retrieve the relevant database images is decreasing in larger databases. We address this issue by extending every quantized local feature with information from its local spatial neighborhood. More precisely, we make use of two approaches widely used for global image features: the Fisher Vector representation aggregating the neighboring local features and a representation based on pooling features from deep convolutional neural network layer outputs. Using four public datasets, we evaluate the representations in terms of their performance after quantization

Towards large-scale image retrieval with a disk-only index

Facing ever-growing image databases, the focus of research in content-based image retrieval, where a query image is used to search for those images in a large database that show the same object or scene, has shifted in the last decade. Instead of using local features such as SIFT together with quantization and inverted file indexing schemes, models working with global features and exhaustive search have been proposed to encounter limited main memory and increasing query times. This, however, impairs the capability to find small objects in images with cluttered background. In this paper, we argue, that it is worth reconsidering image retrieval with local features because since then, two crucial ingredients became available: large solid-state disks providing dramatically shorter access times, and more discriminative models enhancing the local features, for example, by encoding their spatial neighborhood using features from convolutional neural networks resulting in way fewer ra ndom read memory accesses. We show that properly combining both insights renders it possible to keep the index of the database images on the disk rather than in the main memory which allows even larger databases on today’s hardware. As proof of concept we support our arguments with experiments on established public datasets for large-scale image retrieval