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Two strategies for fusing information from multiple sources when generating predictive models in the domain of pesticide classification are investigated: i) fusing different sets of features (molecular descriptors) before building a model and ii) fusing the classifiers built from the individual descriptor sets. An empirical investigation demonstrates that the choice of strategy can have a significant...
This paper studies the feasibility of information analysis processing technology, which fuses speech and image together in the real-time monitoring system. It emphasizes particularly on speech analysis and fuses these two technologies in terms of scoring strategy. It also makes some improvement on MFCC feature extraction and proposes a quick MFCC algorithm. The proposed algorithm can reach the requirement...
This paper proposes an automatic semantic video content indexing and retrieval system based on fusing various low level visual and shape descriptors. Extracted features from region and sub-image blocks segmentation of video shots key-frames are described via IVSM signature (Image Vector Space Model) in order to have a compact and efficient description of the content. Static feature fusion based on...
Feature ranking, due to its simplicity and computational efficiency, is a widely used dimensionality reduction technique, especially for large dataset where other methods are computationally too expensive. Conventionally feature ranking is done based on a single ranking criterion. One drawback associated with the conventional, single-criterion ranking is that the ranking order of the features is very...
Vision-guided autonomous platforms require inertial stabilization of the imaging sensor. This is typically achieved by using a gimbaled system with inertial rate sensors, such as gyros. Using low-cost gyros requires estimation of their error parameters, such as bias and scale-factor. This paper presents a motion model-based method for robust estimation of these parameters via fusing the inertial measurements...
Multi-sensor systems in automotive safety applications and sensor data fusion have become very popular in recent years. Sensors on board cars and active safety applications are increasing in number and the need to define a common method for object extraction and serving these applications has been recognized. Authors propose a high level fusion approach suitable for automotive sensor networks with...
The improvement in sensor technologies over the recent years is providing the earth observation community with datacubes of several hundreds of spectral bands which are both an incredible opportunity for phenomenology understanding and material characterization but also pose a serious challenge for their exploitation. We propose in this paper to eliminate spectral redundancy and noise with the minimum...
This paper presents a contour-based multisensor image registration algorithm. The characteristic of this approach is that the registration parameters are calculated according to the centroids and the long axes of matched contour pairs in the images to be registered It overcomes the difficulties of control point detection and correspondence in feature- based registration techniques. The geometrical...
In this paper we present a comparison of three AFE tools used in the context of ship and vehicle detection based on high resolution data. The three tools (Genie Pro - Los Alamos National Laboratory, Feature Analyst - Visual Learning Systems and eCognition - Definiens AG) were chosen because they were defined as promising and were to be analyzed by NGA in the framework of the STAR program. The comparison...
This paper presents a new approach using multimodal information fusion for automated recognition of complex agitation behavior among persons with dementia. In particular, we present a hierarchical information fusion framework to model complex agitation behaviors based on a clinical agitation scale widely used in hospitals. We present the detailed features extraction and selection to represent low...
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