Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking

Montage is a portable software toolkit for constructing custom, science-grade mosaics by composing multiple astronomical images. The mosaics constructed by Montage preserve the astrometry (position) and photometry (intensity) of the sources in the input images. The mosaic to be constructed is specified by the user in terms of a set of parameters, including dataset and wavelength to be used, location and size on the sky, coordinate system and projection, and spatial sampling rate. Many astronomical datasets are massive, and are stored in distributed archives that are, in most cases, remote with respect to the available computational resources. Montage can be run on both single- and multi-processor computers, including clusters and grids. Standard grid tools are used to run Montage in the case where the data or computers used to construct a mosaic are located remotely on the Internet. This paper describes the architecture, algorithms, and usage of Montage as both a software toolkit and as a grid portal. Timing results are provided to show how Montage performance scales with number of processors on a cluster computer. In addition, we compare the performance of two methods of running Montage in parallel on a grid.Comment: 16 pages, 11 figure

Automatic Wide Field Registration and Mosaicking of OCTA Images Using Vascularity Information

[Abstract] Optical Coherence Tomography Angiography (OCTA) constitutes a novel ophthalmological image modality that is characterized for being a non-invasive capture technique that allows a profound analysis of the vascular characteristics of the eye fundus. Given the restricted field of view of the eye fundus that offers each scan, the specialists frequently capture several complementary images that may be simultaneously analyzed to offer a complete and accurate diagnosis of the patient. In this work, we propose a fully automatic method to register complementary OCTA images and provide compositions for the same patient, generating a wide field of representation that allows a simpler and more direct analysis than the traditional tedious manual procedures. To achieve this, we based our proposal in a robust combination of representative features that are filtered by an accurate identification of the main retinal vasculature. This way, given the characteristic high irregularity in the fundus of the OCTA images, we avoid many variable areas that may interfere in the registration process, restricting the analysis to the most representative and stable structure of this image modality, the main retinal vasculature. In particular, we use Speeded-Up Robust Features (SURF) algorithm to extract representative features in the main vascular region that is extracted using a method that combines the analysis of the Hessian matrix followed by an hysteresis threshold process. Then, using a K-NN model, we perform the registration of the resulting features from the different OCTA images to be analyzed. Finally, the Random sample consensus (RANSAC) method is exploited to produce the final target mosaic. The proposed method presented satisfactory results in the validation experiments, with accurate values for the MSE index of 1.2566 and 1.6725 pixels for the registration of paired images an mosaics, respectively.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047

Characterization of vineyard's canopy through fuzzy clustering and svm over color images

In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process

A Novel Fingerprint Encryption Based on Image and Feature Mosaic

Mobile smart devices in the digital era are enhancing personal information security by adopting fingerprint encryption technology, but due to the small size of mobile smart devices, the area of fingerprint image that can be detected is reduced, resulting in the lack of extractable fingerprint feature information, and traditional fingerprint encryption technology is difficult to apply to small area fingerprint images. To solve the application difficulties of small area fingerprint image encryption, a novel small area fingerprint encryption algorithm based on feature and image mosaic was proposed, and the encryption efficiency of the algorithm was verified using FVC2002 and XDFinger database. Results show that the small area fingerprint recognition algorithm based on feature and image mosaic is significantly improved in encryption efficiency, failure capture rate decreases from 36% to 7%, true acceptance rate increases from 44% to 68%, and the feasibility and reliability of the method is verified. Conclusions can promote the application of small area fingerprint encryption technology in mobile smart devices

Performance analysis on color image mosaicing techniques on FPGA

Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM

Automatic Seamless of Image Stitching

The objective of this paper is to implement image stitching by adopting feature-based alignment algorithm and blending algorithm to produce a high quality image, the images for stitching to create panorama are captured in a fixed linear spatial interval. The processing method involves feature extraction, image matching based on Harris corner detectors method as the feature detection and neighboring pairs alignment using RANSAC (RANdom Sample Consensus) algorithm. Linear blending is applied to remove the transition between the aligned images. The presented image stitching algorithm is successfully able to create panorama image. Keywords: Image stitching, Harris detectors, RANSAC algorithm, Linear blending