571 research outputs found

    A Review of Codebook Models in Patch-Based Visual Object Recognition

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    The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods

    Localization and classification of paddy field pests using a saliency map and deep convolutional neural network

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    We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods

    Fruit and microbial cues in the behavioural ecology and management of Drosophila suzukii

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    Investigating the factors that determine the behaviour of new pests is essential for understanding, predicting and managing their impact on natural and agricultural ecosystems. The spotted wing drosophila (SWD), Drosophila suzukii (Matsumura; Diptera: Drosophilidae), is a worldwide spreading polyphagous pest of soft fruit and berries. Drosophila suzukii is capable to oviposit and develop in ripening fruit, which represents an ecological host shift relative to Drosophila species that prefer overripe fruit. Notably, D. suzukii lives in close association with yeasts, like saprophagous Drosophila flies, but the ecological relevance of this association is insufficiently understood. Chemical cues are important when D. suzukii exploits fruit as a niche. Chemosensory adaptations allow D. suzukii to detect chemical cues emitted by ripening and overripe host fruits. Host attraction, consequently, is odour-guided and precedes egg-laying and exploitation of fruit as the larval niche. However, it is not clear to which extent fruit ripeness, presence of yeast, or sex and mating state of the flies, modulate attraction and host choice. This thesis demonstrates: (i) D. suzukii host choice is modulated by fruit ripeness and fly mating, (ii) a reciprocal niche construction and mutualistic interaction between D. suzukii and the yeast Hanseniaspora uvarum (Niehaus; Ascomycota: Saccharomyceta) on fresh fruit, (iii) the D. suzukii-H. uvarum association can be exploited for the development of lures to monitor and control the invasive pest. The collective findings advance our fundamental understanding of D. suzukii host choice decisions and niche construction. This understanding is of relevance for the development of new pest management tools such as manipulation of insect behaviour

    An Insect-Inspired Target Tracking Mechanism for Autonomous Vehicles

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    Target tracking is a complicated task from an engineering perspective, especially where targets are small and seen against complex natural environments. Due to the high demand for robust target tracking algorithms a great deal of research has focused on this area. However, most engineering solutions developed for this purpose are often unreliable in real world conditions or too computationally expensive to be used in real-time applications. While engineering methods try to solve the problem of target detection and tracking by using high resolution input images, fast processors, with typically computationally expensive methods, a quick glance at nature provides evidence that practical real world solutions for target tracking exist. Many animals track targets for predation, territorial or mating purposes and with millions of years of evolution behind them, it seems reasonable to assume that these solutions are highly efficient. For instance, despite their low resolution compound eyes and tiny brains, many flying insects have evolved superb abilities to track targets in visual clutter even in the presence of other distracting stimuli, such as swarms of prey and conspecifics. The accessibility of the dragonfly for stable electrophysiological recordings makes this insect an ideal and tractable model system for investigating the neuronal correlates for complex tasks such as target pursuit. Studies on dragonflies identified and characterized a set of neurons likely to mediate target detection and pursuit referred to as ‘small target motion detector’ (STMD) neurons. These neurons are selective for tiny targets, are velocity-tuned, contrast-sensitive and respond robustly to targets even against the motion of background. These neurons have shown several high-order properties which can contribute to the dragonfly’s ability to robustly pursue prey with over a 97% success rate. These include the recent electrophysiological observations of response ‘facilitation’ (a slow build-up of response to targets that move on long, continuous trajectories) and ‘selective attention’, a competitive mechanism that selects one target from alternatives. In this thesis, I adopted a bio-inspired approach to develop a solution for the problem of target tracking and pursuit. Directly inspired by recent physiological breakthroughs in understanding the insect brain, I developed a closed-loop target tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. First, I tested this model in virtual world simulations using MATLAB/Simulink. The results of these simulations show robust performance of this insect-inspired model, achieving high prey capture success even within complex background clutter, low contrast and high relative speed of pursued prey. Additionally, these results show that inclusion of facilitation not only substantially improves success for even short-duration pursuits, it also enhances the ability to ‘attend’ to one target in the presence of distracters. This inspect-inspired system has a relatively simple image processing strategy compared to state-of-the-art trackers developed recently for computer vision applications. Traditional machine vision approaches incorporate elaborations to handle challenges and non-idealities in the natural environments such as local flicker and illumination changes, and non-smooth and non-linear target trajectories. Therefore, the question arises as whether this insect inspired tracker can match their performance when given similar challenges? I investigated this question by testing both the efficacy and efficiency of this insect-inspired model in open-loop, using a widely-used set of videos recorded under natural conditions. I directly compared the performance of this model with several state-of-the-art engineering algorithms using the same hardware, software environment and stimuli. This insect-inspired model exhibits robust performance in tracking small moving targets even in very challenging natural scenarios, outperforming the best of the engineered approaches. Furthermore, it operates more efficiently compared to the other approaches, in some cases dramatically so. Computer vision literature traditionally test target tracking algorithms only in open-loop. However, one of the main purposes for developing these algorithms is implementation in real-time robotic applications. Therefore, it is still unclear how these algorithms might perform in closed-loop real-world applications where inclusion of sensors and actuators on a physical robot results in additional latency which can affect the stability of the feedback process. Additionally, studies show that animals interact with the target by changing eye or body movements, which then modulate the visual inputs underlying the detection and selection task (via closed-loop feedback). This active vision system may be a key to exploiting visual information by the simple insect brain for complex tasks such as target tracking. Therefore, I implemented this insect-inspired model along with insect active vision in a robotic platform. I tested this robotic implementation both in indoor and outdoor environments against different challenges which exist in real-world conditions such as vibration, illumination variation, and distracting stimuli. The experimental results show that the robotic implementation is capable of handling these challenges and robustly pursuing a target even in highly challenging scenarios.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

    Deep Learning for Plant Identification in Natural Environment

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    Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry

    Development and improvement of next generation sequencing pipelines for mixed and bulk samples of German fauna

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    As a relatively new technology, DNA metabarcoding has already shown potential for a wide variety of practical applications. Biodiversity monitoring is a discipline of particular importance currently, as hundreds or thousands of species become extinct each year, and most extant species remain undescribed. Metabarcoding can greatly assist in increasing the speed and decreasing the cost of large-scale biodiversity monitoring campaigns, but development and improvement of techniques involved in the steps of a metabarcoding pipeline, from DNA extraction through taxonomic identification of sequence data, are still needed. Projects presented in this thesis cover a range of applications of DNA metabarcoding, from biodiversity monitoring of terrestrial invertebrates, to forensic entomology, reverse taxonomy, and the quality control of food, beverage, and novel food products. A multi-year biomonitoring survey with a special focus on early detection of invasive and/or pest species was conducted in the largest national park in Europe. Results demonstrate the effectiveness of metabarcoding for characterizing biodiversity patterns and phenologies, with Principal Component Analyses and ANOSIM tests showing a significant difference in BIN compositions between groups of samples taken from inside of versus outside of the park, for each of the two study years (2016 r = 0.2, p = 2e-04; 2018 r = 0.239, p = 1e-04). Results of the same study also provide support for employing multiple methods of DNA extraction from bulk samples (i.e. homogenizing the specimens themselves, and utilization of the preservative ethanol as a source of genetic material), as well as combining multiple reference sequence databases, in order to improve the chances of detecting species of interest. An attempt was made to counter the issue of specimen size bias, by pre-sorting specimens according to size, but was not successful for the smallest specimens. The invasive pest Lymantria dispar (Linnaeus, 1758) was detected in an ethanol-extracted sample, representing the first detection of this species in the Bavarian Forest National Park. In another project, a DNA barcode library was created, with records for 2,453 named species and 5,200 total BINs, whereby metabarcoding sequence clusters were able to be assigned to “dark” taxa, or taxa which have not yet been described, but are known only by BIN or MOTU, in a reverse taxonomic approach. For families containing “dark taxa”, an inverse correlation was discovered between body size and percentage of unnamed taxa (r = -0.41, p = 4e-04). A pilot study in DNA barcoding for forensic entomology resulted in the contribution of 120 high quality COI barcode sequences to the ZSM reference library, with 46 newly added species belonging to 11 orders. Metabarcoding facilitated the characterization of insect material collected on decomposing porcine corpses, with 469 species identified molecularly from HTS data. Metabarcoding of food and brewing yeasts was also performed. It was demonstrated that metabarcoding can be successfully applied as a non-targeted approach to detecting differing species in supposedly pure yeast starter cultures, using the 26S rDNA D1/D2 region of chromosome XII in Saccharomyces spp. All of the work herein contributes to the growing knowledge bases of describing the earth’s biodiversity, as well as, from a practical standpoint, the refinement of methods involved in the process of DNA metabarcoding for molecular taxon identification.Als relativ neue Technologie hat das DNA-Metabarcoding bereits Potenzial fĂŒr eine Vielzahl praktischer Anwendungen gezeigt. Die Überwachung der biologischen Vielfalt ist derzeit eine Disziplin von besonderer Bedeutung, da jedes Jahr Hunderte oder Tausende von Arten aussterben und die meisten vorhandenen Arten unbeschrieben bleiben. DNA-Metabarcoding kann erheblich dazu beitragen, die Geschwindigkeit zu erhöhen und die Kosten fĂŒr groß angelegte Kampagnen zur Überwachung der biologischen Vielfalt zu senken. Die Entwicklung und Verbesserung von Techniken, die an den Schritten einer Metabarcodingpipeline von der DNA-Extraktion bis zur taxonomischen Identifizierung von Sequenzdaten beteiligt sind, sind jedoch weiterhin erforderlich. Die in dieser Arbeit vorgestellten Projekte decken eine Reihe von Anwendungen der DNA-Metabarcoding Technologie ab, von der Überwachung der biologischen Vielfalt terrestrischer Wirbelloser ĂŒber forensische Entomologie, umgekehrter Taxonomie bis hin zur QualitĂ€tskontrolle von Lebensmitteln, GetrĂ€nken und neuartigen Lebensmitteln. Im grĂ¶ĂŸten Nationalpark Europas wurde ein mehrjĂ€hriges Biomonitoring-Projekt mit besonderem Schwerpunkt auf der FrĂŒherkennung invasiver und / oder SchĂ€dlingsarten durchgefĂŒhrt. Die Ergebnisse zeigen die Wirksamkeit des DNA-Metabarcodings fĂŒr die Charakterisierung von BiodiversitĂ€tsmustern und -phĂ€nologien, wobei Hauptkomponentenanalysen und ANOSIM-Tests einen signifikanten Unterschied in der BIN-Zusammensetzung zwischen Gruppen von Proben zeigen, die innerhalb und außerhalb des Parks fĂŒr jedes der beiden Studienjahre (2016) entnommen wurden (r = 0,2, p = 2e-04; 2018 r = 0,239, p = 1e-04). Die Ergebnisse derselben Studie unterstĂŒtzen auch die Anwendung mehrerer Methoden zur DNA-Extraktion aus Massenproben (beziehungsweise Homogenisierung der Proben selbst und Verwendung des Konservierungsmittels Ethanol als Quelle fĂŒr genetisches Material) sowie die Kombination mehrerer Referenzsequenzdatenbanken, um die Chancen zu verbessern, alle Arten zu entdecken. Es wurde versucht, dem Problem der Abweichung der ProbengrĂ¶ĂŸe durch Vorsortieren der Proben nach GrĂ¶ĂŸe entgegenzuwirken, was jedoch bei den kleinsten Proben nicht erfolgreich war. Der invasive SchĂ€dling Lymantria dispar (Linnaeus, 1758) wurde in einer mit Ethanol extrahierten Probe nachgewiesen, was den ersten Nachweis dieser Art im Nationalpark Bayerischer Wald darstellt. In einem anderen Projekt wurde eine DNA-Barcode-Bibliothek mit Aufzeichnungen fĂŒr 2.453 benannte Arten und insgesamt 5.200 BINs erstellt, wobei Metabarcoding-Sequenzcluster „dunklen“ Taxa oder Taxa zugeordnet werden konnten, die noch nicht beschrieben wurden, aber nur bekannt sind von BIN oder MOTU in einem reverse-taxonomy Ansatz. FĂŒr Familien mit „dunklen Taxa“ wurde eine inverse Korrelation zwischen KörpergrĂ¶ĂŸe und Prozentsatz unbenannter Taxa entdeckt (r = -0,41, p = 4e-04). Eine Pilotstudie zum DNA-Barcodierung fĂŒr die forensische Entomologie ergab den Beitrag von 120 hochwertigen COI-Barcode-Sequenzen zur ZSM-Referenzbibliothek, wobei 46 neu hinzugefĂŒgte Arten zu 11 Ordnungen gehörten. Das Metabarcoding erleichterte die Charakterisierung von Insektenmaterial, das bei der Zersetzung von Schweinen gesammelt wurde, wobei 469 Arten molekular aus HTS-Daten identifiziert wurden. Metabarcoding von Lebensmitteln und Brauhefen wurde ebenfalls durchgefĂŒhrt. Es wurde gezeigt, dass das Metabarcoding erfolgreich als nicht zielgerichteter Ansatz zum Nachweis unterschiedlicher Arten in vermeintlich reinen Hefestarterkulturen unter Verwendung der 26S-rDNA-D1 / D2-Region von Chromosom XII in Saccharomyces spp. angewendet werden kann. Alle hierin enthaltenen Arbeiten tragen zu den wachsenden Wissensgrundlagen zur Beschreibung der biologischen Vielfalt der Erde sowie zur praktischen Verfeinerung der Methoden bei, die am Prozess des DNA-Metabarcodings zur Identifizierung molekularer Taxa beteiligt sind
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