Open source computer-vision based guidance system for UAVs on-board decision making

The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.

Functional imaging of peripheral vision and dorsal stream function in the human cerebral cortex

Visual information processing in brain proceeds in both serial and parallel fashion throughout various functionally distinct hierarchically organised cortical areas. Feedforward signals from retina and hierarchically lower cortical levels are the major activators of visual neurons, but top-down and feedback signals from higher level cortical areas have a modulating effect on neural processing. My work concentrates on visual encoding in hierarchically low level cortical visual areas in human brain and examines neural processing especially in cortical representation of visual field periphery. I use magnetoencephalography and functional magnetic resonance imaging to measure neuromagnetic and hemodynamic responses during visual stimulation and oculomotor and cognitive tasks from healthy volunteers. My thesis comprises six publications. Visual cortex forms a great challenge for modeling of neuromagnetic sources. My work shows that a priori information of source locations are needed for modeling of neuromagnetic sources in visual cortex. In addition, my work examines other potential confounding factors in vision studies such as light scatter inside the eye which may result in erroneous responses in cortex outside the representation of stimulated region, and eye movements and attention. I mapped cortical representations of peripheral visual field and identified a putative human homologue of functional area V6 of the macaque in the posterior bank of parieto-occipital sulcus. My work shows that human V6 activates during eye-movements and that it responds to visual motion at short latencies. These findings suggest that human V6, like its monkey homologue, is related to fast processing of visual stimuli and visually guided movements. I demonstrate that peripheral vision is functionally related to eye-movements and connected to rapid stream of functional areas that process visual motion. In addition, my work shows two different forms of top-down modulation of neural processing in the hierachically lowest cortical levels; one that is related to dorsal stream activation and may reflect motor processing or resetting signals that prepare visual cortex for change in the environment and another local signal enhancement at the attended region that reflects local feed-back signal and may perceptionally increase the stimulus saliency.

Vision based indoor positioning in a retail environment

Modern smart phones often come with a significant amount of computational power and an integrated digital camera making them an ideal platform for intelligents assistants. This work is restricted to retail environments, where users could be provided with for example navigational in- structions to desired products or information about special offers within their close proximity. This kind of applications usually require information about the user's current location in the domain environment, which in our case corresponds to a retail store. We propose a vision based positioning approach that recognizes products the user's mobile phone's camera is currently pointing at. The products are related to locations within the store, which enables us to locate the user by pointing the mobile phone's camera to a group of products. The first step of our method is to extract meaningful features from digital images. We use the Scale- Invariant Feature Transform SIFT algorithm, which extracts features that are highly distinctive in the sense that they can be correctly matched against a large database of features from many images. We collect a comprehensive set of images from all meaningful locations within our domain and extract the SIFT features from each of these images. As the SIFT features are of high dimensionality and thus comparing individual features is infeasible, we apply the Bags of Keypoints method which creates a generic representation, visual category, from all features extracted from images taken from a specific location. A category for an unseen image can be deduced by extracting the corresponding SIFT features and by choosing the category that best fits the extracted features. We have applied the proposed method within a Finnish supermarket. We consider grocery shelves as categories which is a sufficient level of accuracy to help users navigate or to provide useful information about nearby products. We achieve a 40% accuracy which is quite low for commercial applications while significantly outperforming the random guess baseline. Our results suggest that the accuracy of the classification could be increased with a deeper analysis on the domain and by combining existing positioning methods with ours.

Convex Relaxation for Low-Dimensional Representation: Phase Transitions and Limitations

There is a growing interest in taking advantage of possible patterns and structures in data so as to extract the desired information and overcome the curse of dimensionality. In a wide range of applications, including computer vision, machine learning, medical imaging, and social networks, the signal that gives rise to the observations can be modeled to be approximately sparse and exploiting this fact can be very beneficial. This has led to an immense interest in the problem of efficiently reconstructing a sparse signal from limited linear observations. More recently, low-rank approximation techniques have become prominent tools to approach problems arising in machine learning, system identification and quantum tomography.

In sparse and low-rank estimation problems, the challenge is the inherent intractability of the objective function, and one needs efficient methods to capture the low-dimensionality of these models. Convex optimization is often a promising tool to attack such problems. An intractable problem with a combinatorial objective can often be "relaxed" to obtain a tractable but almost as powerful convex optimization problem. This dissertation studies convex optimization techniques that can take advantage of low-dimensional representations of the underlying high-dimensional data. We provide provable guarantees that ensure that the proposed algorithms will succeed under reasonable conditions, and answer questions of the following flavor:

More specifically, i) Focusing on linear inverse problems, we generalize the classical error bounds known for the least-squares technique to the lasso formulation, which incorporates the signal model. ii) We show that intuitive convex approaches do not perform as well as expected when it comes to signals that have multiple low-dimensional structures simultaneously. iii) Finally, we propose convex relaxations for the graph clustering problem and give sharp performance guarantees for a family of graphs arising from the so-called stochastic block model. We pay particular attention to the following aspects. For i) and ii), we aim to provide a general geometric framework, in which the results on sparse and low-rank estimation can be obtained as special cases. For i) and iii), we investigate the precise performance characterization, which yields the right constants in our bounds and the true dependence between the problem parameters.

Initializing Vision Based Trackers Using Semantic Texton Forests

Vision based tracking can provide the spatial location of project related entities such as equipment, workers, and materials in a large-scale congested construction site. It tracks entities in a video stream by inferring their motion. To initiate the process, it is required to determine the pixel areas of the entities to be tracked in the following consecutive video frames. For the purpose of fully automating the process, this paper presents an automated way of initializing trackers using Semantic Texton Forests (STFs) method. STFs method performs simultaneously the segmentation of the image and the classification of the segments based on the low-level semantic information and the context information. In this paper, STFs method is tested in the case of wheel loaders recognition. In the experiments, wheel loaders are further divided into several parts such as wheels and body parts to help learn the context information. The results show 79% accuracy of recognizing the pixel areas of the wheel loader. These results signify that STFs method has the potential to automate the initialization process of vision based tracking.

Machine Vision-Based Infrastructure As-Built Documentation Using Edge Points

The existing machine vision-based 3D reconstruction software programs provide a promising low-cost and in some cases automatic solution for infrastructure as-built documentation. However in several steps of the reconstruction process, they only rely on detecting and matching corner-like features in multiple views of a scene. Therefore, in infrastructure scenes which include uniform materials and poorly textured surfaces, these programs fail with high probabilities due to lack of feature points. Moreover, except few programs that generate dense 3D models through significantly time-consuming algorithms, most of them only provide a sparse reconstruction which does not necessarily include required points such as corners or edges; hence these points have to be manually matched across different views that could make the process considerably laborious. To address these limitations, this paper presents a video-based as-built documentation method that automatically builds detailed 3D maps of a scene by aligning edge points between video frames. Compared to corner-like features, edge points are far more plentiful even in untextured scenes and often carry important semantic associations. The method has been tested for poorly textured infrastructure scenes and the results indicate that a combination of edge and corner-like features would allow dealing with a broader range of scenes.

A programmable SIMD vision chip for real-time vision applications

A programmable vision chip for real-time vision applications is presented. The chip architecture is a combination of a SIMD processing element array and row-parallel processors, which can perform pixel-parallel and row-parallel operations at high speed. It implements the mathematical morphology method to carry out low-level and mid-level image processing and sends out image features for high-level image processing without I/O bottleneck. The chip can perform many algorithms through software control. The simulated maximum frequency of the vision chip is 300 MHz with 16 x 16 pixels resolution. It achieves the rate of 1000 frames per second in real-time vision. A prototype chip with a 16 x 16 PE array is fabricated by the 0.18 mu m standard CMOS process. It has a pixel size of 30 mu m x 40 mu m and 8.72 mW power consumption with a 1.8 V power supply. Experiments including the mathematical morphology method and target tracking application demonstrated that the chip is fully functional and can be applied in real-time vision applications.

A 1,000 Frames/s Programmable Vision Chip with Variable Resolution and Row-Pixel-Mixed Parallel Image Processors

A programmable vision chip with variable resolution and row-pixel-mixed parallel image processors is presented. The chip consists of a CMOS sensor array, with row-parallel 6-bit Algorithmic ADCs, row-parallel gray-scale image processors, pixel-parallel SIMD Processing Element (PE) array, and instruction controller. The resolution of the image in the chip is variable: high resolution for a focused area and low resolution for general view. It implements gray-scale and binary mathematical morphology algorithms in series to carry out low-level and mid-level image processing and sends out features of the image for various applications. It can perform image processing at over 1,000 frames/s (fps). A prototype chip with 64 x 64 pixels resolution and 6-bit gray-scale image is fabricated in 0.18 mu m Standard CMOS process. The area size of chip is 1.5 mm x 3.5 mm. Each pixel size is 9.5 mu m x 9.5 mu m and each processing element size is 23 mu m x 29 mu m. The experiment results demonstrate that the chip can perform low-level and mid-level image processing and it can be applied in the real-time vision applications, such as high speed target tracking.

Research of Face Location System Based on Human Vision Simulations

In this paper we present a robust face location system based on human vision simulations to automatically locate faces in color static images. Our method is divided into four stages. In the first stage we use a gauss low-pass filter to remove the fine information of images, which is useless in the initial stage of human vision. During the second and the third stages, our technique approximately detects the image regions, which may contain faces. During the fourth stage, the existence of faces in the selected regions is verified. Having combined the advantages of Bottom-Up Feature Based Methods and Appearance-Based Methods, our algorithm performs well in various images, including those with highly complex backgrounds.

A Novel Architecture of Vision Chip for Fast Traffic Lane Detection and FPGA Implementation

This paper presents a novel architecture of vision chip for fast traffic lane detection (FTLD). The architecture consists of a 32*32 SIMD processing element (PE) array processor and a dual-core RISC processor. The PE array processor performs low-level pixel-parallel image processing at high speed and outputs image features for high-level image processing without I/O bottleneck. The dual-core processor carries out high-level image processing. A parallel fast lane detection algorithm for this architecture is developed. The FPGA system with a CMOS image sensor is used to implement the architecture. Experiment results show that the system can perform the fast traffic lane detection at 50fps rate. It is much faster than previous works and has good robustness that can operate in various intensity of light. The novel architecture of vision chip is able to meet the demand of real-time lane departure warning system.