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Precision agriculture has enabled significant progress in improving yield outcomes for farmers. Recent progress in sensing and perception promises to further enhance the use of precision agriculture by allowing the detection of plant diseases and pests. When coupled with robotics methods for spatial localisation, early detection of plant diseases will al- low farmers to respond in a timely and localised manner to dis- ease outbreaks and limit crop damage. This paper proposes the use of hyperspectral imaging (VNIR and SWIR) and machine learning techniques for the detection of the Tomato Spotted Wilt Virus (TSWV) in capsicum plants. Discriminatory features are extracted using the full spectrum, a variety of vegetation indices, and probabilistic topic models. These features are used to train classifiers for discriminating between leaves obtained from healthy and inoculated plants. The results show excellent discrimination based on the full spectrum and comparable results based on data-driven probabilistic topic models and the domain vegetation indices. Additionally our results show increasing classification performance as the dimensionality of the features increase.

We explore an approach to use simple classification models to solve complex problems by partitioning the input domain into smaller regions that are more amenable to the classifier. For this purpose weinvestigate two variants of partitioning based on energy, as measured by the variance. We argue that restricting the energy of the input domain limits the complexity of the problem. Therefore, our method directly controls the energy in each partition. The partitioning methods and several classifiers are evaluated on a road detection application. Our results indicate that partitioning improves the performance of a linear Support Vector Machine and a classifier which considers the average label in each partition, to match the performance of a more sophisticated Neural Network classifier.

Computer vision techniques such as Structurefrom- Motion (SfM) and object recognition tend to fail on scenes with highly reflective objects because the reflections behave differently to the true geometry of the scene. Such image sequences may be treated as two layers superimposed over each other - the nonreflection scene source layer and the reflection layer. However, decomposing the two layers is a very challenging task as it is ill-posed and common methods rely on prior information. This work presents an automated technique for detecting reflective features with a comprehensive analysis of the intrinsic, spatial, and temporal properties of feature points. A support vector machine (SVM) is proposed to learn reflection feature points. Predicted reflection feature points are used as priors to guide the reflection layer separation. This gives more robust and reliable results than what is achieved by performing layer separation alone.

The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for process control work in a manufacturing plant and identifying parts of a car from a photo for automatic damage detection. Unfortunately most of an object's parts of interest in such applications share the same pixel characteristics, having similar colour and texture. This makes segmenting the object into its components a non-trivial task for conventional image segmentation algorithms. In this paper, we propose a "Model Assisted Segmentation" method to tackle this problem. A 3D model of the object is registered over the given image by optimising a novel gradient based loss function. This registration obtains the full 3D pose from an image of the object. The image can have an arbitrary view of the object and is not limited to a particular set of views. The segmentation is subsequently performed using a level-set based method, using the projected contours of the registered 3D model as initialisation curves. The method is fully automatic and requires no user interaction. Also, the system does not require any prior training. We present our results on photographs of a real car., 18 LaTeX pages, 11 figures, 1 algorithm, 1 table

The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods: It does neither require prior training nor learning, nor knowledge of the camera parameters, nor explicit point correspondences or matching features between image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object, and works on a single static image from a given view, and under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection are presented.