Mapping Wide Row Crops with Video Sequences Acquired from a Tractor Moving at Treatment Speed

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight

Galaxy Zoo: Morphological Classification and Citizen Science

We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have produced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.Comment: 11 pages, 1 figure; to be published in Advances in Machine Learning and Data Mining for Astronom

Classification of Weather Situations on Single Color Images

Present vision based driver assistance systems are designed to perform under good-natured weather conditions. However, limited visibility caused by heavy rain or fog strongly affects vision systems. To improve machine vision in bad weather situations, a reliable detection system is necessary as a ground base. We present an approach that is able to distinguish between multiple weather situations based on the classification of single monocular color images, without any additional assumptions or prior knowledge. The proposed image descriptor clearly outperforms existing descriptors for that task. Experimental results on real traffic images are characterized by high accuracy, efficiency, and versatility with respect to driver assistance systems

Color television study Final report, Nov. 1965 - Mar. 1966

Color television camera for transmission from lunar and earth orbits and lunar surfac

Highly transparent poly(2-ethyl-2-oxazoline)-TiO2 nanocomposite coatings for the conservation of matte painted artworks

A nanocomposite coating based on TiO2 nanoparticles and poly(2-ethyl-2-oxazoline) is used as consolidant of matte painted surfaces (temperas, watercolors, modern paintings). The aim of this work is to provide advances in the conservation of these works of art, while preserving their optical appearance, in terms of colour and gloss. Fiber Optic Reflectance Spectroscopy (FORS) measurements of a painting-model (an acrylic black monochrome) treated with the nanocomposite coatings revealed that it is possible to match the optical appearance of the painted surface by tuning the amount of nanoparticles in the polymeric matrix. The requirement of retreatability of the material has been verified by removing the nanocomposite cast on the painted surface with aqueous solutions. FTIR and SEM/EDX measurements showed that almost no traces of the nanocomposite remained on the painted surface, allowing its use for the treatment of real paintings. Test were performed using a contemporary studio-model on canvas attributed to Agostino Bonalumi (1935–2013)

Polarimetric imaging for air accident investigation

We report a trial wherein a simple 4 CCD visible-band Polarimetric Imaging (PI) camera was fielded against aircraft debris distributed across an arid terrain, a littoral region and a small number of maritime debris targets A debris field realistically simulating an aircrash and a debris grid of aircraft remains were observed from an air platform flying in dry and sunny conditions. We report PI utility in support of air accident investigation by an enhanced ability to successfully locate small targets within the scene via the use of colour enhanced and decorrelated intensity PI products. Our results indicate that handheld PI capability may represent an effective low cost, upgrade and augmentation option for existing and future imaging systems that would support air accident investigators and assist in the cueing of more sophisticated assets and/or analyst attention

CCTV Technology Handbook

This CCTV Technology Handbook provides emergency responders, law enforcement security managers, and other security specialists with a reference to aid in planning, designing, and purchasing a CCTV system. This handbook includes a description of the capabilities and limitations of CCTV components used in security applications

Evaluating Visual Odometry Methods for Autonomous Driving in Rain

The increasing demand for autonomous vehicles has created a need for robust navigation systems that can also operate effectively in adverse weather conditions. Visual odometry is a technique used in these navigation systems, enabling the estimation of vehicle position and motion using input from onboard cameras. However, visual odometry accuracy can be significantly impacted in challenging weather conditions, such as heavy rain, snow, or fog. In this paper, we evaluate a range of visual odometry methods, including our DROIDSLAM based heuristic approach. Specifically, these algorithms are tested on both clear and rainy weather urban driving data to evaluate their robustness. We compiled a dataset comprising of a range of rainy weather conditions from different cities. This includes, the Oxford Robotcar dataset from Oxford, the 4Seasons dataset from Munich and an internal dataset collected in Singapore. We evaluated different visual odometry algorithms for both monocular and stereo camera setups using the Absolute Trajectory Error (ATE). Our evaluation suggests that the Depth and Flow for Visual Odometry (DF-VO) algorithm with monocular setup worked well for short range distances (< 500m) and our proposed DROID-SLAM based heuristic approach for the stereo setup performed relatively well for long-term localization. Both algorithms performed consistently well across all rain conditions.Comment: 8 pages, 4 figures, Accepted at IEEE International Conference on Automation Science and Engineering (CASE) 202