Object Detection and Tracking Using Interest Points

Tato práce se zabývá detekcí a sledováním objektů  pomocí význačných bodů. Jsou zde popsány existující přístupy k této problematice. Je zde navržená inovovaná metoda detekce objektů založená na Obecné Houghově transformaci a iterativním prohledáváním Houghova prostoru. Na nejrůznějších typech objektu je demonstrována univerzálnost navrženého detektoru. Sledování objektů je řešeno detekcí objektu snímek po snímku.This paper deals with object detection and tracking using iterest points. Existing approaches are described here. Inovated method based on Generalized Hough transform and iterative Hough-space searching is  proposed in this paper. Generality of proposed detector is shown in various types of objects. Object tracking is designed as frame by frame detection.

DeDA: Deep Directed Accumulator

Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. These rim+ lesions exhibit a geometrically simple structure, where gradients at the lesion edge are radially oriented and a greater magnitude of gradients is observed in contrast to rim- (non rim+) lesions. However, recent studies have shown that the identification performance of such lesions remains unsatisfied due to the limited amount of data and high class imbalance. In this paper, we propose a simple yet effective image processing operation, deep directed accumulator (DeDA), that provides a new perspective for injecting domain-specific inductive biases (priors) into neural networks for rim+ lesion identification. Given a feature map and a set of sampling grids, DeDA creates and quantizes an accumulator space into finite intervals, and accumulates feature values accordingly. This DeDA operation is a generalized discrete Radon transform and can also be regarded as a symmetric operation to the grid sampling within the forward-backward neural network framework, the process of which is order-agnostic, and can be efficiently implemented with the native CUDA programming. Experimental results on a dataset with 177 rim+ and 3986 rim- lesions show that 10.1% of improvement in a partial (false positive rate<0.1) area under the receiver operating characteristic curve (pROC AUC) and 10.2% of improvement in an area under the precision recall curve (PR AUC) can be achieved respectively comparing to other state-of-the-art methods. The source code is available online at https://github.com/tinymilky/DeDAComment: 18 pages, 3 Tables and 4 figure

Label Efficient 3D Scene Understanding

3D scene understanding models are becoming increasingly integrated into modern society. With applications ranging from autonomous driving, Augmented Real- ity, Virtual Reality, robotics and mapping, the demand for well-behaved models is rapidly increasing. A key requirement for training modern 3D models is high- quality manually labelled training data. Collecting training data is often the time and monetary bottleneck, limiting the size of datasets. As modern data-driven neu- ral networks require very large datasets to achieve good generalisation, finding al- ternative strategies to manual labelling is sought after for many industries. In this thesis, we present a comprehensive study on achieving 3D scene under- standing with fewer labels. Specifically, we evaluate 4 approaches: existing data, synthetic data, weakly-supervised and self-supervised. Existing data looks at the potential of using readily available national mapping data as coarse labels for train- ing a building segmentation model. We further introduce an energy-based active contour snake algorithm to improve label quality by utilising co-registered LiDAR data. This is attractive as whilst the models may still require manual labels, these labels already exist. Synthetic data also exploits already existing data which was not originally designed for training neural networks. We demonstrate a pipeline for generating a synthetic Mobile Laser Scanner dataset. We experimentally evalu- ate if such a synthetic dataset can be used to pre-train smaller real-world datasets, increasing the generalisation with less data. A weakly-supervised approach is presented which allows for competitive per- formance on challenging real-world benchmark 3D scene understanding datasets with up to 95% less data. We propose a novel learning approach where the loss function is learnt. Our key insight is that the loss function is a local function and therefore can be trained with less data on a simpler task. Once trained our loss function can be used to train a 3D object detector using only unlabelled scenes. Our method is both flexible and very scalable, even performing well across datasets. Finally, we propose a method which only requires a single geometric represen- tation of each object class as supervision for 3D monocular object detection. We discuss why typical L2-like losses do not work for 3D object detection when us- ing differentiable renderer-based optimisation. We show that the undesirable local- minimas that the L2-like losses fall into can be avoided with the inclusion of a Generative Adversarial Network-like loss. We achieve state-of-the-art performance on the challenging 6DoF LineMOD dataset, without any scene level labels

Sample supervised search centric approaches in geographic object-based image analysis

Sample supervised search centric image segmentation denotes a general method where quality segments are generated based on the provision of a selection of reference segments. The main purpose of such a method is to correctly segment a multitude of identical elements in an image based on these reference segments. An efficient search algorithm traverses the parameter space of a given segmentation algorithm. A supervised quality measure guides the search for the best segmentation results, or rather the best performing parameter set. This method, which is academically pursued in the context of remote sensing and elsewhere, shows promise in assisting the generation of earth observation information products. The method may find applications specifically within the context of user driven geographic object-based image analysis approaches, mainly in respect of very high resolution optical data. Rapid mapping activities as well as general land-cover mapping or targeted element identification may benefit from such a method. In this work it is suggested that sample supervised search centric geographic segment generation forms the basis of a set of methods, or rather a methodological avenue. The original formulation of the method, although promising, is limited in the quality of the segments it can produce – it is still limited by the inherent capability of the given segmentation algorithm. From an optimisation viewpoint, various structures may be encoded forming the fitness or search landscape traversed by a given search algorithm. These structures may interact or have an interplay with the given segmentation algorithm. Various method variants considering expanded fitness landscapes are possible. Additional processes, or constituents, such as data mapping, classification and post-segmentation heuristics may be embedded into such a method. Three distinct and novel method variants are proposed and evaluated based on this concept of expanded fitness landscapes