Patrick Nigri Happ

A Region-Growing Segmentation Algorithm for GPUs

This letter proposes a parallel version for graphics processing units (GPU) of a region-growing image segmentation algorithm widely used by the geographic object-based image analysis (GEOBIA) community. Initially, all image pixels are considered as seeds or primitive segments. Fine-grained parallel threads assigned to individual pixels merge adjacent segments iteratively always ensuring to minimize the overall heterogeneity increase. Besides spectral features the merging criterion considers morphological features that can be efficiently computed in the underlying GPU architecture. Two alternatives using different merging criteria are proposed and tested. An experimental analysis upon five different test images has shown that the parallel algorithm may run up to 19 times faster than its sequential counterpart.

A New Cloud Computing Architecture for the Classification of Remote Sensing Data

This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applications by researchers from different scientific investigation fields. The implementation of architecture is also discussed. A software prototype (available online), which runs machine learning routines implemented on the cloud using the Waikato Environment for Knowledge Analysis (WEKA), a popular free software licensed under the GNU General Public License, is used for validation. Performance issues are addressed through an experimental analysis in which two supervised classifiers available in WEKA were used: random forest and support vector machines. This paper further describes how to include other classification methods in the available software prototype

Towards distributed region growing image segmentation based on MapReduce

Image segmentation is a critical step in image analysis, and usually involves a high computational cost, especially when dealing with large volumes of data. Given the significant increase in the spatial, spectral and temporal resolutions of remote sensing imagery in the last years, current sequential and parallel solutions fail to deliver the expected performance and scalability. This work proposes a scalable and efficient segmentation method, capable of handling efficiently very large high resolution images. The proposed solution is based on the MapReduce model, which offers a highly scalable and reliable framework for storing and processing massive data in cloud computing environments. The solution was implemented and validated using the Hadoop platform. Experimental results attest the viability of performing region growing segmentation in the MapReduce framework.

A Set of Methods to Support Object-Based Distributed Analysis of Large Volumes of Earth Observation Data

The rapid increase in the number of aerial and orbital Earth observation systems is generating a huge amount of remote sensing data that need to be readily transformed into useful information for policy and decision makers. This exposes an urgent demand for image interpretation tools that can deal efficiently with very large volumes of data. In this work, we introduce a set of methods that support distributed processing of georeferenced raster and vector data in a computer cluster, which may be a virtual cluster provided by cloud computing infrastructure services. The set of methods comprise a particular technique for indexing distributed georeferenced datasets, as well as strategies for distributing efficiently the processing of spatial context-aware operations. They provide the means for the development of scalable applications, capable of processing large volumes of geospatial data. We evaluated the proposed methods in a remote sensing image interpretation application, built on the MapReduce framework, and executed in a cloud computing infrastructure. The experimental results corroborate the capacity of the methods to support efficient handling of very large earth observation datasets.

A Cloud Computing Strategy for Region-Growing Segmentation

This paper proposes a distributed strategy for region-growing segmentation of arbitrarily large images. The strategy is suited for most distributed architectures, including cloud-computing environments. It focuses on handling very large data and tackles the main problem of tiled-based segmentation: artifacts that occur in the segments adjacent to tile borders. Our proposal is to use a specific indexing mechanism and a hierarchical stitching method to suppress segmentation artifacts. The strategy was validated by a series of experiments carried out on a virtual cluster in a commercial cloud-computing infrastructure, over WorldView-2 images of different sizes. The experiments demonstrated that the proposed strategy is able to eliminate tile-based segmentation artifacts. The experimental analysis further showed that the strategy brings significant gains in terms of processing time, particularly for very large images, as more processing units are added to the distributed processing infrastructure.

On the architecture of a big data classification tool based on a map reduce approach for hyperspectral image analysis

Advances in remote sensors are providing exceptional quantities of large-scale data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package; a tool able to perform supervised classification procedures on huge amounts of data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKAs machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big data.

Single Sample Face Recognition from Video via Stacked Supervised Auto-Encoder

This work proposes and evaluates strategies based on Stacked Supervised Auto-Encoders (SSAE) for face representation in video surveillance applications. The study focuses on the identification task with a single sample per person (SSPP) in the gallery. Variations in terms of pose, facial expression, illumination and occlusion are approached in two ways. First, the SSAE extracts features from face images, which are robust to such variations. Second, we propose methods to exploit the multiple samples per persons probes (MSPPP) that can be extracted from video sequences. Three variants of the proposed method are compared upon HONDA/UCSD and VIDTIMIT video datasets. The experimental results demonstrate that strategies combining SSAE and MSPPP are able to outperform other SSPP methods, such a local binary patterns, in face recognition from video.

SPT 3.1: A free software for automatic tuning of segmentation parameters in optical, hyperspectral and SAR images

The Segmentation Parameter Tuner (SPT) is a tool designed for automatic tuning of segmentation parameters. In SPT, the goodness of a set of parameter values is given by the level of agreement between the segmentation result and a given reference (representing the desired outcome) quantified by a metric selected by the user (empirical discrepancy methods). This metric is used as the fitness function of an optimization algorithm that searches the parameter space for the minimum value, which is expected to correspond to the segmentation outcome most similar to the reference. SPT 3.1 offers many interesting features such as: five segmentation algorithms (for Optical, Hyperspectral and SAR images), four optimization algorithms (stochastic and direct search optimization methods) and seven discrepancy metrics (pixel and object-based). This paper describes the optimization procedure underlying SPT 3.1, the features added to this version as well as an experiment that illustrates the operation of the tool.

Symbiotic Tracker Ensemble with Feedback Learning

Visual tracking is a challenging task due to a number of factors, such as occlusions, deformations, illumination variations and abrupt motion changes present in a video sequence. Generally, trackers are robust to some of these factors, but do not achieve satisfactory results when dealing with multiple factors at the same time. More robust results when multiple factors are present can be obtained by combining the results of different trackers. In this paper we propose a multiple tracker fusion method, named Symbiotic Tracker Ensemble with Feedback Learning (SymTE-FL), which combines the results of a set of trackers to produce a unified tracking estimate. The novelty of the method consists in providing feedback to the individual trackers, so that they can correct their own estimates, thus improving overall tracking accuracy. The proposal is validated by experiments conducted upon a publicly available database. The results show that the proposed method delivered in average more accurate tracking estimates than those obtained with individual trackers running independently and with the original approach.

Metaheuristics for Supervised Parameter Tuning of Multiresolution Segmentation

This letter evaluates metaheuristics for the supervised parameter tuning of multiresolution-region-growing segmentation. Three groups of metaheuristics are tested in terms of convergence speed and solution quality. Generalized pattern search, mesh adaptive direct search, and Nelder-Mead represent the single-solution group. Differential evolution (DE) represents the population group. DE followed by each of the aforementioned single-solution metaheuristics represents the hybrid metaheuristic group. This letter reveals that the optimization objective functions typically have countless local minima, many of them leading to very poor solutions. Experiments on three data sets demonstrated that single-solution-based methods often lead to a solution with unacceptable quality. DE was less susceptible to be stuck in local minima when compared to single-solution methods, but it was slower in reaching the minima. Moreover, hybrid methods presented the best tradeoff between accuracy and convergence speed.