A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery

In this paper we propose a hybrid classification method, adopting the best features extraction strategy for each land cover class on multidate ASTER data. To enable an effective comparison among images, Multivariate Alteration Detection (MAD) transformation was applied in the pre-processing phase, because of its high level of automation and reliability in the enhancement of change information among different images. Consequently, different features identification procedures, both spectral and object-based, were implemented to overcome problems of misclassification among classes with similar spectral response. Lastly, a post-classification comparison was performed on multidate ASTER-derived land cover (LC) maps to evaluate the effects of change in the study area

Advances towards behaviour-based indoor robotic exploration

215 p.The main contributions of this research work remain in object recognition by computer vision, by one side, and in robot localisation and mapping by the other. The first contribution area of the research address object recognition in mobile robots. In this area, door handle recognition is of great importance, as it help the robot to identify doors in places where the camera is not able to view the whole door. In this research, a new two step algorithm is presented based on feature extraction that aimed at improving the extracted features to reduce the superfluous keypoints to be compared at the same time that it increased its efficiency by improving accuracy and reducing the computational time. Opposite to segmentation based paradigms, the feature extraction based two-step method can easily be generalized to other types of handles or even more, to other type of objects such as road signals. Experiments have shown very good accuracy when tested in real environments with different kind of door handles. With respect to the second contribution, a new technique to construct a topological map during the exploration phase a robot would perform on an unseen office-like environment is presented. Firstly a preliminary approach proposed to merge the Markovian localisation in a distributed system, which requires low storage and computational resources and is adequate to be applied in dynamic environments. In the same area, a second contribution to terrain inspection level behaviour based navigation concerned to the development of an automatic mapping method for acquiring the procedural topological map. The new approach is based on a typicality test called INCA to perform the so called loop-closing action. The method was integrated in a behaviour-based control architecture and tested in both, simulated and real robot/environment system. The developed system proved to be useful also for localisation purpose

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Remote Sensing for Large-Area, Multi-Jurisdictional Habitat Mapping

A framework designed to guide the effective use of remote sensing in large-area, multi-jurisdictional habitat mapping studies has been developed. Based on hierarchy theory and the remote sensing scene model, the approach advocates (i) identifying the key physical attributes operating on the landscape; (ii) selecting a series of suitable remote sensing data whose spatial, spectral, radiometric, and temporal characteristics correspond to the attributes of interest; and (iii) applying an intelligent succession of scale-sensitive data processing techniques that are capable of delivering the desired information. The approach differs substantially from the single-map, classification-based strategies that have largely dominated the wildlife literature, and is designed to deliver a sophisticated, multi-layer information base that is capable of supporting a variety of management objectives. The framework was implemented in the creation of a multi-layer database composed of land cover, crown closure, species composition, and leaf area index (LAI) phenology over more than 100,000 km2 in west-central Alberta. Generated through a combination of object-oriented classification, conventional regression, and generalized linear models, the products represent a high-quality, flexible information base constructed over an exceptionally challenging multi-jurisdictional environment. A quantitative comparison with two alternative large-area information sources—the Alberta Vegetation Inventory and a conventional classification-based land-cover map—showed that the thesis database had the highest map quality and was best capable of explaining both individual—and population-level resource selection by grizzly bears

Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications

A binary tree-structured MRF model for multispectral satellite image segmentation

In this work we detail a tree-structured MRF (TS-MRF) prior model useful for segmentation of multispectral satellite images. This model allows a hierarchical representation of a 2-D field by the use of a sequence of binary MRFs, each corresponding to a node in the tree. In order to get good performances, one can fit the intrinsic structure of the data to the TS-MRF model, thereby defining a multi-parameter, flexible, MRF. Although a global MRF model is defined on the whole tree, optimization as well estimation can be carried out by working on a single node at a time, from the root down to the leaves, with a significant reduction in complexity. Indeed the overall algorithm is proved experimentally to be much faster than a comparable algorithm based on a conventional Ising MRF model, especially when the number of bands becomes very large. Thanks to the sequential optimization procedure, this model also addresses the cluster validation problem of unsupervised segmentation, through the use of a stopping condition local to each node. Experiments on a SPOT image of the Lannion Bay, a ground-truth of which is available, prove the superior performance of the algorithm w.r.t. some other MRF based algorithms for supervised segmentation, as well as w.r.t. some variational methods