Machine learning techniques for high dimensional data

This thesis presents data processing techniques for three different but related application areas: embedding learning for classification, fusion of low bit depth images and 3D reconstruction from 2D images. For embedding learning for classification, a novel manifold embedding method is proposed for the automated processing of large, varied data sets. The method is based on binary classification, where the embeddings are constructed so as to determine one or more unique features for each class individually from a given dataset. The proposed method is applied to examples of multiclass classification that are relevant for large scale data processing for surveillance (e.g. face recognition), where the aim is to augment decision making by reducing extremely large sets of data to a manageable level before displaying the selected subset of data to a human operator. In addition, an indicator for a weighted pairwise constraint is proposed to balance the contributions from different classes to the final optimisation, in order to better control the relative positions between the important data samples from either the same class (intraclass) or different classes (interclass). The effectiveness of the proposed method is evaluated through comparison with seven existing techniques for embedding learning, using four established databases of faces, consisting of various poses, lighting conditions and facial expressions, as well as two standard text datasets. The proposed method performs better than these existing techniques, especially for cases with small sets of training data samples. For fusion of low bit depth images, using low bit depth images instead of full images offers a number of advantages for aerial imaging with UAVs, where there is a limited transmission rate/bandwidth. For example, reducing the need for data transmission, removing superfluous details, and reducing computational loading of on-board platforms (especially for small or micro-scale UAVs). The main drawback of using low bit depth imagery is discarding image details of the scene. Fortunately, this can be reconstructed by fusing a sequence of related low bit depth images, which have been properly aligned. To reduce computational complexity and obtain a less distorted result, a similarity transformation is used to approximate the geometric alignment between two images of the same scene. The transformation is estimated using a phase correlation technique. It is shown that that the phase correlation method is capable of registering low bit depth images, without any modi�cation, or any pre and/or post-processing. For 3D reconstruction from 2D images, a method is proposed to deal with the dense reconstruction after a sparse reconstruction (i.e. a sparse 3D point cloud) has been created employing the structure from motion technique. Instead of generating a dense 3D point cloud, this proposed method forms a triangle by three points in the sparse point cloud, and then maps the corresponding components in the 2D images back to the point cloud. Compared to the existing methods that use a similar approach, this method reduces the computational cost. Instated of utilising every triangle in the 3D space to do the mapping from 2D to 3D, it uses a large triangle to replace a number of small triangles for flat and almost flat areas. Compared to the reconstruction result obtained by existing techniques that aim to generate a dense point cloud, the proposed method can achieve a better result while the computational cost is comparable

Pushbroom Stereo for High-Speed Navigation in Cluttered Environments

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV

Towards the development of a smart flying sensor: illustration in the field of precision agriculture

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

Fully Onboard Low-Power Localization with Semantic Sensor Fusion on a Nano-UAV using Floor Plans

Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoor applications and for close proximity to humans. To enable autonomy, the nano-UAV must be able to self-localize in its operating environment. This is a particularly-challenging task due to the limited sensing and compute resources on board. This work presents an online and onboard approach for localization in floor plans annotated with semantic information. Unlike sensor-based maps, floor plans are readily-available, and do not increase the cost and time of deployment. To overcome the difficulty of localizing in sparse maps, the proposed approach fuses geometric information from miniaturized time-of-flight sensors and semantic cues. The semantic information is extracted from images by deploying a state-of-the-art object detection model on a high-performance multi-core microcontroller onboard the drone, consuming only 2.5mJ per frame and executing in 38ms. In our evaluation, we globally localize in a real-world office environment, achieving 90% success rate. We also release an open-source implementation of our work.Comment: Under review for ICRA 2024, 7 page

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Department of Mehcanical EngineeringUnmanned aerial vehicles (UAVs) are widely used in various areas such as exploration, transportation and rescue activity due to light weight, low cost, high mobility and intelligence. This intelligent system consists of highly integrated and embedded systems along with a microprocessor to perform specific task by computing algorithm or processing data. In particular, image processing is one of main core technologies to handle important tasks such as target tracking, positioning, visual servoing using visual system. However, it often requires heavy amount of computation burden and an additional micro PC controller with a flight computer should be additionally used to process image data. However, performance of the controller is not so good enough due to limited power, size, and weight. Therefore, efficient image processing techniques are needed considering computing load and hardware resources for real time operation on embedded systems. The objective of the thesis research is to develop an efficient image processing framework on embedded systems utilizing neural network and various optimized computation techniques to satisfy both efficient computing speed versus resource usage and accuracy. Image processing techniques has been proposed and tested for management computing resources and operating high performance missions in embedded systems. Graphic processing units (GPUs) available in the market can be used for parallel computing to accelerate computing speed. Multiple cores within central processing units (CPUs) are used like multi-threading during data uploading and downloading between the CPU and the GPU. In order to minimize computing load, several methods have been proposed. The first method is visualization of convolutional neural network (CNN) that can perform both localization and detection simultaneously. The second is region proposal for input area of CNN through simple image processing, which helps algorithm to avoid full frame processing. Finally, surplus computing resources can be saved by control the transient performance such as the FPS limitation. These optimization methods have been experimentally applied to a ground vehicle and quadrotor UAVs and verified that the developed methods offer an optimization to process in embedded environment by saving CPU and memory resources. In addition, they can support to perform various tasks such as object detection and path planning, obstacle avoidance. Through optimization and algorithms, they reveal a number of improvements for the embedded system compared to the existing. Considering the characteristics of the system to transplant the various useful algorithms to the embedded system, the method developed in the research can be further applied to various practical applications.ope

Motion blur in digital images - analys, detection and correction of motion blur in photogrammetry

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This thesis proves the negative affect that blurred images have on photogrammetric processing. It shows that small amounts of blur do have serious impacts on target detection and that it slows down processing speed due to the requirement of human intervention. Larger blur can make an image completely unusable and needs to be excluded from processing. To exclude images out of large image datasets an algorithm was developed. The newly developed method makes it possible to detect blur caused by linear camera displacement. The method is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values of the same dataset. This algorithm enables the exclusion of blurred images and subsequently allows photogrammetric processing without them. However, it is also possible to use deblurring techniques to restor blurred images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears muddy and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images for deblurring may limit the application. Another method to enhance the image is the unsharp mask method, which improves images significantly and makes photogrammetric processing more successful. However, deblurring of images needs to focus on geometric correct deblurring to assure geometric correct measurements. Furthermore, a novel edge shifting approach was developed which aims to do geometrically correct deblurring. The idea of edge shifting appears to be promising but requires more advanced programming