Clothing-invariant human gait recognition using an adaptive outlier detection method

Multimedia Tools and Applications - Human gait as a behavioral biometric identifier has received much attention in recent years. But there are some challenges which hinder using this biometric in...     

OBCD-HH: an object-based change detection approach using multi-feature non-seed-based region growing segmentation

There is an increasing need to get updated information regarding the changes on earth’s surface. The information obtained can be used in a wide range of applications including disaster management, land-use investigation etc. The high-resolution remote sensing images obtained from satellites provide us with an opportunity to detect changes on earth’s surface between various time intervals. In this paper, an unsupervised object-based change detection (OBCD) method is proposed to detect changes in high resolution bi-temporal satellite images. To detect changes, a novel multi-feature non-seed-based region growing (MF-NSRG) algorithm is proposed for image segmentation based on heterogeneity minimization that uses textural heterogeneity along with spectral and spatial heterogeneity during region growing. The performance of MF-NSRG algorithm is further improved by using Harris Hawk, a recently proposed metaheuristic algorithm, which is used to obtain optimal values of segmentation parameters. Finally, the feature maps extracted from the pre-change and post-change segmented images are analysed using histogram trend similarity (HTS) approach to detect changes. The proposed approach is known as object-based change detection using Harris Hawk (OBCD-HH). The proposed OBCD-HH approach is applied on two datasets: xBD and Onera Satellite Change Detection (OSCD) dataset. Its performance is compared with existing state-of-the-art algorithms and results show the superiority of the proposed approach.

     

Optimal clustering based outlier detection and cluster center initialization algorithm for effective tone mapping

Multimedia Tools and Applications - The high dynamic range (HDR) imaging and displaying a wide range of imaging levels in the imaging industry is found in the world using devices with limited...     

KAGO: an approximate adaptive grid-based outlier detection approach using kernel density estimate

Pattern Analysis and Applications - Outlier detection approaches show their efficacy while extracting unforeseen knowledge in domains such as intrusion detection, e-commerce, and fraudulent...     

Scaling-based forest structural change detection using an inverted geometric-optical model in the Three Gorges region of China

We use the Li-Strahler geometric-optical model combined with a scaling-based approach to detect forest structural changes in the Three Gorges region of China. The physical-based Li-Strahler model can be inverted to retrieve forest structural properties. One of the main input variables for the inverted model is the fractional component of sunlit background, which is calculated by using pure reflectance spectra (endmembers) of surface components. In this study, we extract these endmembers from moderate spatial resolution MODIS data using two scaling-based methods (namely, a regional based linear unmixing and a purest-pixel approach) relying on corresponding high spatial resolution Landsat TM images. Then, the forest structural property crown closure (CC) is estimated by inverting the Li-Strahler model based on the extracted endmembers. Changes in CC are mapped using MODIS mosaics dated 2002 and 2004 for the whole Three Gorges region. Validation of the estimated CC using 25 sample sites indicates that the regional scaling-based endmembers extracted using linear unmixing are more suitable to be used in combination with the inverted Li-Strahler model for monitoring the forest CC than the purest-pixel approach, and results in significantly better estimates in both years (R²₂₀₀₂ = 0.614, RMSE₂₀₀₂ = 6%, R²₂₀₀₄ = 0.631 and RMSE₂₀₀₄ = 5.2%). A change detection map of the model derived CC in 2002 and 2004 shows a decrease in CC in the eastern counties of the Three Gorges region located close to the Three Gorges Dam. An increase in CC has been observed in other counties of the Three Gorges region, implying a preliminary positive feedback on certain policy measures taken safeguarding forest structure.     

Classifying U.S. manufacturing plant locations using an artificial neural network

This paper examines the application of an artificial neural network (ANN) for classifying the contiguous states in the USA according to their manufacturing climate. The application uses a self-organizing paradigm, the ART2. The performance of the ANN was very encouraging. Its results were compared to those obtained from a scoring model that ranked the states utilizing the same criteria. Future work is intended to further improve the network's performance and develop an Artificial Neural System (ANS) that will assist decision makers in selecting the best locations for manufacturing facilities in the U.S.A. The ANS is seen as a key component of an intelligent decision support system for improving and enhancing plant location decision making.     

An adaptive parcel-based technique for unsupervised change detection

A novel unsupervised technique for the detection of changes in multi-temporal remote sensing images is presented. It adaptively exploits the spatial-contextual information contained in the neighbourhood of each pixel to reduce the effects of noise and hence to increase change-detection accuracy. In addition, the proposed definition of an adaptive pixel neighbourhood allows a precise location of the borders of changed areas.     

A minimum-cost thresholding technique for unsupervised change detection

We propose an automatic thresholding technique for difference images in unsupervised change detection. Such a technique takes into account the different costs that may be associated with commission and omission errors in the selection of the decision threshold. This allows the generation of maps in which the overall change-detection cost is minimized, i.e. the more critical kind of error is reduced according to end-user requirements.     

Automatic outlier detection for time series: an application to sensor data

In this article we consider the problem of detecting unusual values or outliers from time series data where the process by which the data are created is difficult to model. The main consideration is the fact that data closer in time are more correlated to each other than those farther apart. We propose two variations of a method that uses the median from a neighborhood of a data point and a threshold value to compare the difference between the median and the observed data value. Both variations of the method are fast and can be used for data streams that occur in quick succession such as sensor data on an airplane. Martin Meckesheimer has been a member of the Applied Statistics Group at Phantom Works, Boeing since 2001. He received a Bachelor of Science Degree in Industrial Engineering from the University of Pittsburgh in 1997, and a Master's Degree in Industrial and Systems Engineering from Ecole Centrale Paris in 1999. Martin earned a Doctorate in Industrial Engineering from The Pennsylvania State University in August 2001, as a student of Professor Russell R. Barton and Dr. Timothy W. Simpson. His primary research interests are in the areas of design of experiments and surrogate modeling. Sabyasachi Basu received his Ph.D. is Statistics from the University of Wisconsin at Madison in 1990. Since his Ph.D., he has worked in both academia and in industry. He has taught and guided Ph.D. students in the Department of Statistics at the Southern Methodist University. He has also worked as a senior marketing statistician at the J. C. Penney Company. Dr. Basu is also an American Society of Quality certified Six Sigma Black Belt. He is currently an Associate Technical Fellow in Statistics and Data Mining at the Boeing Company. In this capacity, he works as a researcher and a technical consultant within Boeing for data mining, statistics and process improvements. He has published more than 20 papers and technical reports. He has also served as journal referee for several journals, organized conferences and been invited to present at conferences.     

Land-use and land-cover mapping and change detection in Tripura using satellite LANDSAT data

In the present paper efforts have been made by the authors to study the changes in land use and land cover in Tripura using LANDSAT images of two different dates and to see how well data obtained help in the study of geographical phenomena with special reference to land use and land cover. The methodology for land-use mapping and limitations of LANDSAT data have been described in detail. The LANDSAT computer compatible tapes (CCTs) were analysed on the sophisticated interactive Multispectral Data Analysis System (MDAS) with the help of training sets of each category collected during field visits. Ninety per cent accuracy of these categories has been achieved when compared with existing data compiled on ground surveys by the working plan of the Division of Forest Department. The area of each land-use category was also calculated for monitoring land-use changes.     

Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains

Improved and up-to-date land use/land cover (LULC) data sets that classify specific crop types and associated land use practices are needed over intensively cropped regions such as the U.S. Central Great Plains, to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The Moderate Resolution Imaging Spectroradiometer (MODIS) holds considerable promise for detailed, large-area crop-related LULC mapping in this region given its global coverage, unique combination of spatial, spectral, and temporal resolutions, and the cost-free status of its data. The objective of this research was to evaluate the applicability of time-series MODIS 250 m normalized difference vegetation index (NDVI) data for large-area crop-related LULC mapping over the U.S. Central Great Plains. A hierarchical crop mapping protocol, which applied a decision tree classifier to multi-temporal NDVI data collected over the growing season, was tested for the state of Kansas. The hierarchical classification approach produced a series of four crop-related LULC maps that progressively classified: 1) crop/non-crop, 2) general crop types (alfalfa, summer crops, winter wheat, and fallow), 3) specific summer crop types (corn, sorghum, and soybeans), and 4) irrigated/non-irrigated crops. A series of quantitative and qualitative assessments were made at the state and sub-state levels to evaluate the overall map quality and highlight areas of misclassification for each map.The series of MODIS NDVI-derived crop maps generally had classification accuracies greater than 80%. Overall accuracies ranged from 94% for the general crop map to 84% for the summer crop map. The state-level crop patterns classified in the maps were consistent with the general cropping patterns across Kansas. The classified crop areas were usually within 1-5% of the USDA reported crop area for most classes. Sub-state comparisons found the areal discrepancies for most classes to be relatively minor throughout the state. In eastern Kansas, some small cropland areas could not be resolved at MODIS' 250 m resolution and led to an underclassification of cropland in the crop/non-crop map, which was propagated to the subsequent crop classifications. Notable regional areal differences in crop area were also found for a few selected crop classes and locations that were related to climate factors (i.e., omission of marginal, dryland cropped areas and the underclassification of irrigated crops in western Kansas), localized precipitation patterns (overclassification of irrigated crops in northeast Kansas), and specific cropping practices (double cropping in southeast Kansas).     

Caustics mapping: an image-space technique for real-time caustics

In this paper, we present a simple and practical technique for real-time rendering of caustics from reflective and refractive objects. Our algorithm, conceptually similar to shadow mapping, consists of two main parts: creation of a caustic map texture, and utilization of the map to render caustics onto nonshiny surfaces. Our approach avoids performing any expensive geometric tests, such as ray-object intersection, and involves no precomputation; both of which are common features in previous work. The algorithm is well suited for the standard rasterization pipeline and runs entirely on the graphics hardware     

Procedures for change detection using Landsat digital data

Resource management agencies are interested in knowing when and how satellite data can be effectively used to monitor environmental change and what information can be expected from remote sensing techniques. In this paper the procedures that might be followed in applying the complementary methods of band ratioing and post-classification change detection to monitor a large remote area are outlined. Examples from research in the Peace-Athabasca Delta are used to illustrate the procedures and expected results.     

Determining target detection limits and accuracy delineation using an incremental technique

Classification of terrestrial materials using remotely sensed imagery is becoming an increasingly popular method to assess natural systems. This study uses predictions derived from remotely sensed hyperspectral images to determine minimum target detection thresholds and to statistically constrain classification accuracies. Minimum detection thresholds are determined using an iterative regression breakpoint technique, and accuracy is delineated by quantifying the variance above the correlation breakpoint. Assessing confidence and determining detection limits generates a greater understanding of the classification process and adds significant utility to the classified product.     

A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection

This article presents a novel semi-supervised change detection approach for very-high-resolution (VHR) remote-sensing images. The proposed approach aims at extracting the change information by making full use of the context-sensitive relationships among pixels in the images. This is accomplished via a context-sensitive image representation technique based on hypergraph model. First, each temporal image is modelled as a hypergraph that utilizes a set of hyperedges to capture the context-sensitive properties of pixels in the image. Second, the difference in the bi-temporal images is measured by both the similarity and the consistency between the two hypergraphs. Finally, the changes are separated from the unchanged ones by a hypergraph-based semi-supervised classifier on the difference image. Experimental results obtained on different VHR remote-sensing data sets demonstrate the effectiveness of the proposed approach.     

Modeling interurban mentioning relationships in the U.S. Twitter network using geo-hashtags

Twitter users mention cities in the context of tourist attractions or events, such as protests or games, thus forming a network between cities from which they tweet and cities that they tweet about. This study tackles the challenge of explaining why users tweet about cities outside of their own by analyzing an underlying network of city mentions on Twitter. It applies graph theory as well as various measures of network connectivity such as indegree, hub score, and authority score to examine the prominence of individual cities in the Twitter landscape and the connection patterns between cities. Closely related to communication ties is the sentiment of tweets about other cities, which can be extracted from the text of tweets that contain geohashtags, i.e., hashtags with names of other cities. The effect of distance between cities on user sentiments towards cities will be explored. Furthermore, Quadratic Assignment Procedure (QAP) network regression will be used to build a general socio-demographic and geographic model that helps to identify which characteristics of city pairs, e.g. separation distance, or similarity in employment data or population, increase or decrease the likelihood of mentions between those cities. Findings show that distance and network size (compactness) are major determinants in communication ties between cities. City popularity, when measured by indegree, follows a power-law distribution, and is closely tied to population, GDP, or visitor numbers. Larger cities reveal a higher percentage of self-mentions than smaller cities, showing the high level of attention these metropolitan areas attract from Twitter users due to the many opportunities, events, and sights offered. Future research in the field of analysis of geotagged tweets can further extend the network regression model with new covariates.     

Cloud detection for the Far East region using NOAA AVHRR images

A new automatic cloud detection method to process the large data sets required for long-term trends in AVHRR time series images is described and tested over the Far East region. In the cloud detection method, a simple NDVI test was used to roughly separate land from ocean. After the NDVI test we carried out two tests: a visible or near-infrared test and an infrared test. Results from the cloud detection method over the Far East region are presented. In order to assess the cloud detection method, a comparison was carried out between the method presented and the Saunders and Kriebel daytime procedure using the N-Land database. We found that in general, the method presented has better performance than the Saunders and Kriebel procedure by about 0.5% to 10.5%.     

Sensing the city with Instagram: Clustering geolocated data for outlier detection

Early detection of unusual events in urban areas is a priority for city management departments, which usually deploy specific complex video-based infrastructures typically monitored by human staff. However, and with the emergence and quick popularity of Location-based social networks (LBSNs), detecting abnormally high or low number of citizens in a specific area at a specific time could be done by an expert system that automatically analyzes the public geo-tagged posts. Our approach focuses exclusively on the location information linked to these posts. By applying a density-based clustering algorithm, we obtain the pulse of the city (24 h–7 days) in a first training phase, which enables the detection of outliers (unexpected behaviors) on-the-fly in an ulterior test or monitoring phase. This solution entails that no specific infrastructure is needed since the citizens are the ones who buy, maintain, carry the mobile devices and freely disclose their location by proactively sharing posts. Besides, location analysis is lighter than video analysis and can be automatically done. Our approach was validated using a dataset of geo-tagged posts obtained from Instagram in New York City for almost six months with good results. Actually, not only all the already previously known events where detected, but also other unknown events where discovered during the experiment.