Human activity recognition using limb component extraction

in the field of human activity recognition has existed for quite sometime, but has gained popularity in recent years for use in many areas of application. In the security industry, suspicious activities could be detected in high-profile areas. In the medical industry, systems could be trained to detect patterns of motion indicating distress or to detect a lack of motion if a person had fallen and was unable to move. However, algorithms with reliable accuracy are difficult to implement in a real-time environment due to computational complexity. This thesis developed a new way of extracting and using data from a human figure in a video frame to determine what type of activity the subject is performing. Following background subtraction, a thinning algorithm operating on the silhouette offered a more robust limb extraction method, while a six-segment representation of the human figure offered more accuracy in deriving limb parameters, or components, such as distance from torso, and angle of displacement from the vertical axis. Neural networks or nearest neighbor classifiers used the limb components to identify a number of activities, such as walking, running, waving and jumping. This entire human activity recognition system was tested with both a MATLAB implementation (non real-time) and a C++ implementation in OpenCV (real-time). The algorithm achieved 96% classification accuracy in video feeds, which is only slightly lower than that of intensive, non real-time systems

Efficient 1D and 2D barcode detection using mathematical morphology

Barcode technology is essential in automatic identification, and is used in a wide range of real-time applications. Different code types and applications impose special problems, so there is a continuous need for solutions with improved performance. Several methods exist for code localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need reliable automatic barcode localization, e.g. conveyor belts and automated production, where missed detections cause loss of profit. Our goal is to detect automatically, rapidly and accurately the barcode location with the help of extracted image features. We propose a new algorithm variant, that outperforms in both accuracy and efficiency other detectors found in the literature using similar ideas, and also improves on the detection performance in detecting 2D codes compared to our previous algorithm

Deconstructing Approximate Offsets

We consider the offset-deconstruction problem: Given a polygonal shape Q with n vertices, can it be expressed, up to a tolerance \eps in Hausdorff distance, as the Minkowski sum of another polygonal shape P with a disk of fixed radius? If it does, we also seek a preferably simple-looking solution P; then, P's offset constitutes an accurate, vertex-reduced, and smoothened approximation of Q. We give an O(n log n)-time exact decision algorithm that handles any polygonal shape, assuming the real-RAM model of computation. A variant of the algorithm, which we have implemented using CGAL, is based on rational arithmetic and answers the same deconstruction problem up to an uncertainty parameter \delta; its running time additionally depends on \delta. If the input shape is found to be approximable, this algorithm also computes an approximate solution for the problem. It also allows us to solve parameter-optimization problems induced by the offset-deconstruction problem. For convex shapes, the complexity of the exact decision algorithm drops to O(n), which is also the time required to compute a solution P with at most one more vertex than a vertex-minimal one.Comment: 18 pages, 11 figures, previous version accepted at SoCG 2011, submitted to DC

A Novel Method for Barcode Localization in Image Domain

Barcode localization is an essential step of the barcode reading process. For industrial environments, having high-resolution cameras and eventful scenarios, fast and reliable localization is crucial. Images acquired in those setups have limited parameters, however, they vary at each application. In earlier works we have already presented various barcode features to track for localization process. In this paper, we present a novel approach for fast barcode localization using a limited set of pixels in image domain

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model

Rainfall retrieval using Spinning Enhanced Visible and Infrared Imager (SEVIRI-MSG) and Cloud Physical Properties (CPP) algorithm : validation over Belgium and applications

Precipitation is the main variable of the water cycle and the water resources availability. Despite numerous available methods, precipitation measurements are still insufficient to quantify with certainty ongoing changes and to provide data for numerical models validation. Roebeling & Holleman (2009) presented the Cloud Physical Properties algorithm using data from the SEVIRI instrument on board Meteosat Second Generation. The goal of present study is to extend previous validations and verify the algorithm performances throughout yearly and daily cycles in order to identify possible use and applications. A seven-years data set of parallax-shift corrected clouds and precipitation data over Western Europe have therefore been processed using CPP algorithm. Results are encouraging for both precipitation areas delimitation and rain rates assessment. However, rain rates estimation are strongly affected by sun zenith angle with increasing overestimation for sza above 60°. Systematic errors also affect the retrieval of cloud properties for very thick clouds with an overestimation of extreme precipitation events

Patterns and Collective Behavior in Granular Media: Theoretical Concepts

Granular materials are ubiquitous in our daily lives. While they have been a subject of intensive engineering research for centuries, in the last decade granular matter attracted significant attention of physicists. Yet despite a major efforts by many groups, the theoretical description of granular systems remains largely a plethora of different, often contradicting concepts and approaches. Authors give an overview of various theoretical models emerged in the physics of granular matter, with the focus on the onset of collective behavior and pattern formation. Their aim is two-fold: to identify general principles common for granular systems and other complex non-equilibrium systems, and to elucidate important distinctions between collective behavior in granular and continuum pattern-forming systems.Comment: Submitted to Reviews of Modern Physics. Full text with figures (2Mb pdf) avaliable at http://mti.msd.anl.gov/AransonTsimringReview/aranson_tsimring.pdf Community responce is appreciated. Comments/suggestions send to [email protected]