558 research outputs found
Hybrid 2D and 3D face verification
No full textFace verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional\ud
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(3D) data, is not changed by pose or illumination variations.\ud
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Over the past five years methods have been investigated to combine 2D and\ud
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3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures).\ud
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A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation:\ud
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1. the use of holistic difference vectors of the face, and\ud
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2. by dividing the 3D face into Free-Parts.\ud
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The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined.\ud
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This thesis also examines methods for performing classifier fusion score fusion.\ud
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Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques.\ud
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The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed.\ud
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The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%
Optimisation-based Design of a Manipulator for Harvesting Capsicum
No full textThis paper presents a global-optimisation frame-work for the design of a manipulator for harvesting capsicum(peppers) in the field. The framework uses a simulated capsicum scenario with automatically generated robot models based on DH parameters. Each automatically generated robot model is then placed in the simulated capsicum scenario and the ability of the robot model to get to several goals (capsicum with varying orientations and positions) is rated using two criteria:the length of a collision-free path and the dexterity of the end-effector. These criteria form the basis of the objective function used to perform a global optimisation. The paper shows a preliminary analysis and results that demonstrate the potential of this method to choose suitable robot models with varying degrees of freedom
Sweet pepper pose detection and grasping for automated crop harvesting
No full textThis paper presents a method for estimating the\ud
6DOF pose of sweet-pepper (capsicum) crops for autonomous\ud
harvesting via a robotic manipulator. The method uses the\ud
Kinect Fusion algorithm to robustly fuse RGB-D data from\ud
an eye-in-hand camera combined with a colour segmentation\ud
and clustering step to extract an accurate representation of the crop. The 6DOF pose of the sweet peppers is then estimated via a nonlinear least squares optimisation by fitting a superellipsoid to the segmented sweet pepper. The performance of the method is demonstrated on a real 6DOF manipulator with a custom gripper. The method is shown to estimate the 6DOF pose successfully enabling the manipulator to grasp sweet peppers for a range of different orientations. The results obtained improve largely on the performance of grasping when compared\ud
to a naive approach, which does not estimate the orientation of the crop
A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms
No full textThis paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers)
On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture
No full textDeveloping accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information
Functionalization of the internal surface of pure-silica MFI zeolite with aliphatic alcohols
No full textThe functionalization of the internal surface of pure-silica MFI zeolite using aliphatic alcohols (1-butanol and 1-hexanol) is reported. Calcined MFI nanoparticles (50, 100, 200, and 500 nm in size) are treated with neat 1-butanol and 1-hexanol under reflux conditions. The 1-butanol loadings on 200 and 500 nm particles are determined to be 0.7 mmol/g SiO2 by thermogravimetric analysis (TGA) and are similar to the tetrapropylammonium (TPA) template content of the as-made nanoparticles. C-13 cross-polarization magic-angle spinning (CP-MAS) NMR and TGA data suggest that the observed 1-butanol loading is strongly correlated with the concentration of internal silanol defect sites. In addition, significantly higher 1-butanol, loadings on 50 nm (1.1 mmol/g SiO2) and 100 nm (0.93 mmol/g SiO2) MFI nanoparticles reflect the concurrent functionalization of silanols on the external surfaces of the nanoparticles. These results are in systematic agreement with theoretical estimates that consider both internal and external surface functionalization sites. MFI nanoparticles (50, 100, and 300 nm) treated with 1-hexanol result in 1.34, 1.28, and 1.14 mmol/g SiO2 of 1-hexanol content, respectively, levels that are higher than expected from consideration of the results of butanol treatment. These higher organic loadings of 1-hexanol may imply the existence of additional physisorbed 1-hexanol molecules trapped between other 1-hexanol molecules within the zeolite micropores and/or the formation of dimeric/oligomeric complexes by hydrophobic interactions between the hexyl groups. C-13/Si-29 MAS and CP-MAS NMR investigations suggest that the organic groups are covalently bonded to the internal silanol defect sites, consistent with previous work on the chemisorption of methanol in MFI.
Peduncle detection of sweet pepper for autonomous crop harvesting: Combined colour and 3D information
No full textThis paper presents a 3D visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting the peduncle cleanly is one of the most difficult stages of the harvesting process, where the peduncle is the part of the crop that attaches it to the main stem of the plant. Accurate peduncle detection in 3D space is therefore a vital step in reliable autonomous harvesting of sweet peppers, as this can lead to precise cutting while avoiding damage to the surrounding plant. This paper makes use of both colour and geometry information acquired from an RGB-D sensor and utilises a supervised-learning approach for the peduncle detection task. The performance of the proposed method is demonstrated and evaluated using qualitative and quantitative results (the Area-Under-the-Curve (AUC) of the detection precision-recall curve). We are able to achieve an AUC of 0.71 for peduncle detection on field-grown sweet peppers. We release a set of manually annotated 3D sweet pepper and peduncle images to assist the research community in performing further research on this topic
Robot for weed species plant-specific management
No full textThe rapid evolution of herbicide-resistant weed species has revitalized research in nonchemical methods for weed destruction. Robots with vision-based capabilities for online weed detection and classification are a key enabling factor for the specialized treatment of individual weed species. This paper describes the design, development, and testing of a modular robotic platform with a heterogeneous weeding array for agriculture. Starting from requirements derived from farmer insights, technical specifications are put forward. A design of a robotic platform is conducted based on the required technical specifications, and a prototype is manufactured and tested. The second part of the paper focuses on the weeding mechanism attached to the robotic platform. This includes aspects of vision for weed detection and classification, as well as the design of a weeding array that combines chemical and mechanical methods for weed destruction. Field trials of the weed detection and classification system show an accuracy of 92.3% across a range of weed species, while the heterogeneous weed management system is able to selectively apply a mechanical or chemical control method based on the species of weed. Together, the robotic platform and weeding array demonstrate the potential for robotic plant-species–specific weed management enabled by the vision-based online detection and classification algorithms
A transplantable system for weed classification by agricultural robotics
No full textThis work presents a rapidly deployable system for automated precision weeding with minimal human labeling time. This overcomes a limiting factor in robotic precision weeding related to the use of vision-based classification systems trained for species that may not be relevant to specific farms. We present a novel approach to overcome this problem by employing unsupervised weed scouting, weed-group labeling, and finally, weed classification that is trained on the labeled scouting data. This work demonstrates a novel labeling approach designed to maximize labeling accuracy whilst needing to label as few images as possible. The labeling approach is able to provide the best classification results of any of the examined exemplar-based labeling approaches whilst needing to label over seven times fewer images than full data labeling
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