Machine Learning based Mountainous Skyline Detection and Visual Geo-Localization

With the ubiquitous availability of geo-tagged imagery and increased computational power, geo-localization has captured a lot of attention from researchers in computer vision and image retrieval communities. Significant progress has been made in urban environments with stable man-made structures and geo-referenced street imagery of frequently visited tourist attractions. However, geo-localization of natural/mountain scenes is more challenging due to changed vegetations, lighting, seasonal changes and lack of geo-tagged imagery. Conventional approaches for mountain/natural geo-localization mostly rely on mountain peaks and valley information, visible skylines and ridges etc. Skyline (boundary segmenting sky and non-sky regions) has been established to be a robust natural feature for mountainous images, which can be matched with the synthetic skylines generated from publicly available terrain maps such as Digital Elevation Models (DEMs). Skyline or visible horizon finds further applications in various other contexts e.g. smooth navigation of Unmanned Aerial Vehicles (UAVs)/Micro Aerial Vehicles (MAVs), port security, ship detection and outdoor robot/vehicle localization.\parProminent methods for skyline/horizon detection are based on non-realistic assumptions and rely on mere edge detection and/or linear line fitting using Hough transform. We investigate the use of supervised machine learning for skyline detection. Specifically we propose two novel machine learning based methods, one relying on edge detection and classification while other solely based on classification. Given a query image, an edge or classification map is first built and converted into a multi-stage graph problem. Dynamic programming is then used to find a shortest path which conforms to the detected skyline in the given image. For the first method, we provide a detailed quantitative analysis for various texture features (Scale Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and their combinations) used to train a Support Vector Machine (SVM) classifier and different choices (binary edges, classified edge score, gradient score and their combinations) for the nodal costs for Dynamic Programming (DP). For the second method, we investigate the use of dense classification maps for horizon line detection. We use Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as our classifier choices and use normalized intensity patches as features. Both proposed formulations are compared with a prominent edge based method on two different data sets.\par We propose a fusion strategy which boosts the performance of the edge-less approach using edge information. The fusion approach, which has been tested on an additional challenging data set, outperforms each of the two methods alone. Further, we demonstrate the capability of our formulations to detect absence of horizon boundary and detection of partial horizon lines. This could be of great value in applications where a confidence measure of the detection is necessary e.g. localization of planetary rovers/robots. In an extended work, we compare our edge-less skyline detection approach against deep learning networks recently proposed for semantic segmentation on an additional data set. Specifically, we compare our proposed fusion formulation with Fully Convolutional Network (FCN), SegNet and another classical supervised learning based method.\par We further propose a visual geo-localization pipeline based on evolutionary computing; where Particle Swarm Optimization (PSO) is adopted to find/refine an orientation estimate by minimizing the cost function based on horizon-ness probability of pixels. The dense classification score image resulting from our edge-less/fusion approach is used as a fitness measure to guide the particles toward best solution where the rendered horizon from DEM perfectly aligns with the actual horizon from the image without even requiring its explicit detection. The effectiveness of the proposed geo-localization pipeline is evaluated on a decent sized data set

Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.Comment: Accepted for publication at German Conference on Pattern Recognition (GCPR) 2017. This research was supported by German Research Foundation DFG within Priority Research Programme 1894 "Volunteered Geographic Information: Interpretation, Visualisation and Social Computing

Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here

The Hydrogen Epoch of Reionization Array Dish I: Beam Pattern Measurements and Science Implications

The Hydrogen Epoch of Reionization Array (HERA) is a radio interferometer aiming to detect the power spectrum of 21 cm fluctuations from neutral hydrogen from the Epoch of Reionization (EOR). Drawing on lessons from the Murchison Widefield Array (MWA) and the Precision Array for Probing the Epoch of Reionization (PAPER), HERA is a hexagonal array of large (14 m diameter) dishes with suspended dipole feeds. Not only does the dish determine overall sensitivity, it affects the observed frequency structure of foregrounds in the interferometer. This is the first of a series of four papers characterizing the frequency and angular response of the dish with simulations and measurements. We focus in this paper on the angular response (i.e., power pattern), which sets the relative weighting between sky regions of high and low delay, and thus, apparent source frequency structure. We measure the angular response at 137 MHz using the ORBCOMM beam mapping system of Neben et al. We measure a collecting area of 93 m^2 in the optimal dish/feed configuration, implying HERA-320 should detect the EOR power spectrum at z~9 with a signal-to-noise ratio of 12.7 using a foreground avoidance approach with a single season of observations, and 74.3 using a foreground subtraction approach. Lastly we study the impact of these beam measurements on the distribution of foregrounds in Fourier space.Comment: 13 pages, 9 figures. Replaced to match accepted ApJ versio

A model-based approach for detection of objects in low resolution passive millimeter wave images

A model-based vision system to assist the pilots in landing maneuvers under restricted visibility conditions is described. The system was designed to analyze image sequences obtained from a Passive Millimeter Wave (PMMW) imaging system mounted on the aircraft to delineate runways/taxiways, buildings, and other objects on or near runways. PMMW sensors have good response in a foggy atmosphere, but their spatial resolution is very low. However, additional data such as airport model and approximate position and orientation of aircraft are available. These data are exploited to guide our model-based system to locate objects in the low resolution image and generate warning signals to alert the pilots. Also analytical expressions were derived from the accuracy of the camera position estimate obtained by detecting the position of known objects in the image