Estimating the camera direction of a geotagged image using reference images

Millions of smart phones and GPS-equipped digital cameras sold each year, as well as photo-sharing websites such as Picasa and Panoramio have enabled personal photos to be associated with geographic information. It has been shown by recent research results that the additional global positioning system (GPS) information helps visual recognition for geotagged photos by providing valuable location context. However, the current GPS data only identifies the camera location, leaving the camera viewing direction uncertain within the possible scope of 360°. To produce more precise photo location information, i.e. the viewing direction for geotagged photos, we utilize both Google Street View and Google Earth satellite images. Our proposed system is two-pronged: (1) visual matching between a user photo and any available street views in the vicinity can determine the viewing direction, and (2) near-orthogonal view matching between a user photo taken on the ground and the overhead satellite view at the user geo-location can compute the viewing direction when only the satellite view is available. Experimental results have shown the effectiveness of the proposed framework.     

Estimating life-form cover fractions in California sage scrub communities using multispectral remote sensing

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems is known for its high biodiversity and is home to a large number of rare, threatened, and endangered species. Because of extensive urban development in the past fifty years, this ecologically significant community type is highly degraded and fragmented. To conserve endangered CSS communities, monitoring internal conditions of communities is as crucial as monitoring distributions of the community type in the region. Vegetation type mapping and field sampling of individual plants provide ecologically meaningful information about CSS communities such as spatial distribution and species compositions, respectively. However, both approaches only provide spatially comprehensive information but no information about internal conditions or vice versa. Therefore, there is a need for monitoring variables which fill the information gap between vegetation type maps and field-based data. A number of field-based studies indicate that life-form fractional cover is an effective indicator of CSS community health and habitat quality for CSS-obligated species. This study investigates the effectiveness of remote sensing approaches for estimating fractional cover of true shrub, subshrub, herb, and bare ground in CSS communities of southern California. Combinations of four types of multispectral imagery ranging from 0.15 m resolution scanned color infrared aerial photography to 10 m resolution SPOT 5 multispectral imagery and three image processing models - per-pixel, object-based, and spectral mixture models - were tested.An object-based image analysis (OBIA) routine consistently yielded higher accuracy than other image processing methods for estimating all cover types. Life-form cover was reliably predicted, with error magnitudes as low as 2%. Subshrub and herb cover types required finer spatial resolution imagery for more accurate predictions than true shrub and bare ground types. Positioning of sampling grids had a substantial impact on the reliability of accuracy assessment, particularly for cover estimates predicted using multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery. Of the approaches tested in this study, OBIA using pansharpened QuickBird imagery is one of the most promising approaches because of its high accuracy and processing efficiency and should be tested for more heterogeneous CSS landscapes. MESMA applied to SPOT imagery should also be examined for effectiveness in estimating factional cover over more extensive habitat areas because of its low data cost and potential for conducting retrospective studies of vegetation community conditions.     

Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat

ABSTRACT

The nitrogen nutrition index (NNI) is a quantitative and reliable indicator of the nitrogen nutrition distribution or status of crops. The timely and accurate estimation of the NNI is crucial in agriculture management. In this study, the quantitative analysis and hyperspectral remote sensing modelling of the NNI were conducted, in which the hyperspectral remote sensing data and NNI data at different growth stages of winter wheat were measured using ground and unmanned aerial vehicle (UAV) carrying high spectrometer equipment. First, the NNIs of the four growth stages of winter wheat were calculated and statistically analyzed. Then, the hyperspectral characteristics at different growth stages and various NNIs were examined. Second, the representation wavebands of the hyperspectral data, which were sensitive to the NNI of winter wheat, were acquired and evaluated. In addition, hyperspectral models were established and comparatively assessed for the NNI estimation. Finally, the hyperspectral characteristics and the remote sensing estimation of the NNIs were determined on the basis of UAV-based hyperspectral data. The results are as follows. (1) As the NNIs of winter wheat changed, the characteristic of the red shift, the variations in the red edge position, and the near-infrared waveband range of the hyperspectral data became apparent. (2) The green band, red edge, and near-infrared were sensitive to the NNIs of winter wheat, and they could be effectively used for estimating the NNI. Moreover, the multiple statistical regression models, which were based on representative wavebands, performed well in estimating the NNI results for the different growth stages of winter wheat.     

Estimating fractional snow cover from MODIS using the normalized difference snow index

Snow-cover information is important for a wide variety of scientific studies, water supply and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) provides improved capabilities to observe snow cover from space and has been successfully using a normalized difference snow index (NDSI), along with threshold tests, to provide global, automated binary maps of snow cover. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible MODIS spectral bands to identify snow versus other features in a scene. This study has evaluated whether there is a “signal” in the NDSI that could be used to estimate the fraction of snow within a 500 m MODIS pixel and thereby enhance the use of the NDSI approach in monitoring snow cover. Using Landsat 30-m observations as “ground truth,” the percentage of snow cover was calculated for 500-m cells. Then a regression relationship between 500-m NDSI observations and fractional snow cover was developed over three different snow-covered regions and tested over other areas. The overall results indicate that the relationship between fractional snow cover and NDSI is reasonably robust when applied locally and over large areas like North America. The relationship offers advantages relative to other published fractional snow cover algorithms developed for global-scale use with MODIS. This study indicates that the fraction of snow cover within a MODIS pixel using this approach can be provided with a mean absolute error less than 0.1 over the range from 0.0 to 1.0 in fractional snow cover.     

Edge detection in multispectral images using the self-organizing map

In this paper, two new methods for edge detection in multispectral images are presented. They are based on the use of the self-organizing map (SOM) and a grayscale edge detector. With the 2-dimensional SOM the ordering of pixel vectors is obtained by applying the Peano scan, whereas this can be omitted using the 1-dimensional SOM. It is shown that using the R-ordering based methods some parts of the edges may be missed. However, they can be found using the proposed methods. Using them it is also possible to find edges in images which consist of metameric colors. Finally, it is shown that the proposed methods find the edges properly from real multispectral airplane images. The size of the SOM determines the amount of found edges. If the SOM is taught using a large color vector database, the same SOM can be utilized for numerous images.     

Estimating 40 years of nitrogen deposition in global biomes using the SCIAMACHY NO2 column

Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO₂ columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO₂ columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NO_x emission data to expand the density of NO₂ columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO₂ columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m^–2 year^–1 in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets.     

Estimating the position of a moving object based on test disturbance of camera position

The problem of estimating the coordinates of a moving object based on visual data arises in numerous applications, starting from robotic and ending with the consumer market of portable devices. Traditional algorithms for solving this problem require either additional devices or significant constraints on the possible motion of the object. In this work, we present a new approach to tracking the object that lets us estimate its position under sufficiently general conditions. The method is based on randomizing the camera location independently of the object’s motion; since the test disturbance we choose is independent, it lets us construct a feasible iterative pseudogradient estimation algorithm.     

People tracking using a network-based PTZ camera

In this paper, we propose a method for online upper body tracking using an IP PTZ camera. This type of camera uses a built-in Web server resulting in variable response times when sending control commands. Furthermore, communicating with a Web server involves network delays. Thus, because the camera is inside a control loop, the effective frame rate that can be processed by a computer vision method is irregular and in general low (2–6 fps). Our tracking method has been specifically designed to perform in such conditions. It detects, at every frame, candidate blobs using motion detection, region sampling, and region color appearance. The target is detected among candidate blobs using a fuzzy classifier. Then, a movement command is sent to the camera using the target position and speed. The proposed method can cope with low frame rate, and thus with large motion of the target, even in the case of a fast walk. Results show that our system has a good target detection precision (>88%) and low track fragmentation, and the target is almost always localized within 1/6th of the image diagonal from the image center.     

Episode detection in videos captured using a head-mounted camera

With the advent of wearable computing, personal imaging, photojournalism and personal video diaries, the need for automated archiving of the videos captured by them has become quite pressing. The principal device used to capture the human-environment interaction with these devices is a wearable camera (usually a head-mounted camera). The videos obtained from such a camera are raw and unedited versions of the visual interaction of the wearer (the user of the camera) with the surroundings. The focus of our research is to develop post-processing techniques that can automatically abstract videos based on episode detection. An episode is defined as a part of the video that was captured when the user was interested in an external event and paid attention to record it. Our research is based on the assumption that head movements have distinguishable patterns during an episode occurrence and these patterns can be exploited to differentiate between an episode and a non-episode. Here we present a novel algorithm exploiting the head and body behaviour for detecting the episodes. The algorithms performance is measured by comparing the ground truth (user-declared episodes) with the detected episodes. The experiments show the high degree of success we achieved with our proposed method on several hours of head-mounted video captured in varying locations.     

Estimating grassland biomass and leaf area index using ground and satellite data

We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAl and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth-Thomas greenness (from TM), LAI, biomass and the various terrain variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous landcover the inclusion of categorical terrain data in calibration procedures is a useful technique.     

Egomotion estimation of a range camera using the space envelope

In this paper we present a method to compute the egomotion of a range camera using the space envelope. The space envelope is a geometric model that provides more information than a simple segmentation for correspondences and motion estimation. We describe a novel variation of the maximal matching algorithm that matches surface normals to find correspondences. These correspondences are used to compute rotation and translation estimates of the egomotion. We demonstrate our methods on two image sequences containing 70 images. We also discuss the cases where our methods fail, and additional possible methods for exploiting the space envelope.     

Estimating the number of clusters using a windowing technique

Clustering is the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters). A fundamental and unresolved issue in cluster analysis is to determine how many clusters are present in a given set of patterns. In this paper, we present the z-windows clustering algorithm, which aims to address this problem using a windowing technique. Extensive empirical tests that illustrate the efficiency and the accuracy of the propsoed method are presented. The text was submitted by the authors in English. Basilis Boutsinas. Received his diploma in Computer Engineering and Informatics in 1991 from the University of Patras, Greece. He also conducted studies in Electronics Engineering at the Technical Education Institute of Piraeus, Greece, and Pedagogics at the Pedagogical Academy of Lamia, Greece. He received his PhD on Knowledge Representation from the University of Patras in 1997. He has been an assistant professor in the Department of Business Administration at the University of Patras since 2001. His primary research interests include data mining, business intelligence, knowledge representation techniques, nonmonotonic reasoning, and parallel AI. Dimitris K. Tasoulis received his diploma in Mathematics from the University of Patras, Greece, in 2000. He attained his MSc degree in 2004 from the postgraduate course “Mathematics of Computers and Decision Making” from which he was awarded a postgraduate fellowship. Currently, he is a PhD candidate in the same course. His research activities focus on data mining, clustering, neural networks, parallel algorithms, and evolutionary computation. He is coauthor of more than ten publications. Michael N. Vrahatis is with the Department of Mathematics at the University of Patras, Greece. He received the diploma and PhD degree in Mathematics from the University of Patras in 1978 and 1982, respectively. He was a visiting research fellow at the Department of Mathematics, Cornell University (1987–1988) and a visiting professor to the INFN (Istituto Nazionale di Fisica Nucleare), Bologna, Italy (1992, 1994, and 1998); the Department of Computer Science, Katholieke Universiteit Leuven, Belgium (1999); the Department of Ocean Engineering, Design Laboratory, MIT, Cambridge, MA, USA (2000); and the Collaborative Research Center “Computational Intelligence” (SFB 531) at the Department of Computer Science, University of Dortmund, Germany (2001). He was a visiting researcher at CERN (European Organization of Nuclear Research), Geneva, Switzerland (1992) and at INRIA (Institut National de Recherche en Informatique et en Automatique), France (1998, 2003, and 2004). He is the author of more than 250 publications (more than 110 of which are published in international journals) in his research areas, including computational mathematics, optimization, neural networks, evolutionary algorithms, and artificial intelligence. His research publications have received more than 600 citations. He has been a principal investigator of several research grants from the European Union, the Hellenic Ministry of Education and Religious Affairs, and the Hellenic Ministry of Industry, Energy, and Technology. He is among the founders of the “University of Patras Artificial Intelligence Research Center” (UPAIRC), established in 1997, where currently he serves as director. He is the founder of the Computational Intelligence Laboratory (CI Lab), established in 2004 at the Department of Mathematics of University of Patras, where currently he serves as director.     

Fall detection based on the gravity vector using a wide-angle camera

Falls in elderly people are becoming an increasing healthcare problem, since life expectancy and the number of elderly people who live alone have increased over recent decades. If fall detection systems could be installed easily and economically in homes, telecare could be provided to alleviate this problem. In this paper we propose a low cost fall detection system based on a single wide-angle camera. Wide-angle cameras are used to reduce the number of cameras required for monitoring large areas. Using a calibrated video system, two new features based on the gravity vector are introduced for fall detection. These features are: angle between the gravity vector and the line from feet to head of the human and size of the upper body. Additionally, to differentiate between fall events and controlled lying down events the speed of changes in the features is also measured. Our experiments demonstrate that our system is 97% accurate for fall detection.     

On the classification of multispectral satellite images using the multilayer perceptron

For classifying multispectral satellite images, a multilayer perceptron (MLP) is trained using either (i) ground truth data or (ii) the output of a K-means clustering program or (iii) both, as applied to certain representative parts of the given data set. In the second case, different sets of clustered image outputs, which have been checked against actual ground truth data wherever available, are used for testing the MLP. The cover classes are, typically, different types of (a) vegetation (including forests and agriculture); (b) soil (including mountains, highways and rocky terrain); and (c) water bodies (including lakes). Since the extent of ground truth may not be sufficient for training neural networks, the proposed procedure (of using clustered output images) is believed to be novel and advantageous. Moreover, it is found that the MLP offers an accuracy of more than 99% when applied to the multispectral satellite images in our library. As importantly, comparison with some recent results shows that the proposed application of the MLP leads to a more accurate and faster classification of multispectral image data.     

Realistic surface geometry reconstruction using a hand-held RGB-D camera

In this paper, we have proposed a novel approach for the reconstruction of real object/scene with realistic surface geometry using a hand-held, low-cost, RGB-D camera. To achieve accurate reconstruction, the most important issues to consider are the quality of the geometry information provided and the global alignment method between frames. In our approach, new surface geometry refinement is used to recover finer scale surface geometry from depth data by utilizing high-quality RGB images. In addition, a weighted multi-scale iterative closest point method is exploited to align each scan to the global model accurately. We show the effectiveness of the proposed surface geometry refinement method by comparing it with other depth refinement methods. We also show both the qualitative and quantitative results of reconstructed models by comparing it with other reconstruction methods.     

Hierarchical multispectral galaxy decomposition using a MCMC algorithm with multiple temperature simulated annealing

We present a new method for the parametric decomposition of barred spiral galaxies in multispectral observations. The observation is modelled with a realistic image formation model and the galaxy is composed of physically significant parametric structures. The model also includes a parametric filtering to remove non-desirable aspects of the observation. Both the model and the filter parameters are estimated by a robust Monte Carlo Markov chain (MCMC) algorithm. The algorithm is based on a Gibbs sampler combined with a novel strategy of simulated annealing in which several temperatures allow to manage efficiently the simulation effort. Besides, the overall decomposition is performed following an original framework: a hierarchy of models from a coarse model to the finest one is defined. At each step of the hierarchy the estimate of a coarse model is used to initialize the estimation of the finer model. This leads to an unsupervised decomposition scheme with a reduced computation time. We have validated the method on simulated and real 5-band images: the results showed the accuracy and the robustness of the proposed approach.     

Estimating discretionary accruals using a grouping genetic algorithm

A number of different models have been suggested for detecting earnings management but the linear regression-based model presented by Jones (1991) is the most frequently used. The underlying assumption with the Jones model is that earnings are managed through accounting accruals. Typically, the companies for which earnings management is studied are grouped based on their industries. It is thus assumed that the accrual generating process for companies within a specific industry is similar. However, some studies have recently shown that this assumption does not necessarily hold. An alternative approach which returns a grouping which is, if not optimal, at least very close to optimal is the use of genetic algorithms. The purpose of this study is to assess the performance of the cross-sectional Jones accrual model when the data set firms are grouped using a grouping genetic algorithm. The results provide strong evidence that the grouping genetic algorithm method outperforms the various alternative grouping methods.     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Walking-in-place for omnidirectional VR locomotion using a single RGB camera

Virtual Reality - Locomotion is a fundamental interaction element allowing navigation inside the virtual environment, and the walking-in-place (WIP) techniques have been actively developed as a...     

采用FPGA的高速CCD相机的时钟发生器

采用IL－E2 TDI CCD做为传感器，与计算机构成了成像系统，并在计算机CRT上显示出图像。主要介绍高速CCD相机的工作时钟产生电路的设计，采用大规模集成电路FPGA实现了该工作时钟驱动电路，采用AHDL语言对工作时钟驱动电路进行了硬件描述，并利用Max Plus2软件对所设计的工作时钟驱动电路进行了仿真，最后对FPGA器件进行了编程和硬件电路调试，进而实现了整个CCD相机的控制。