KEYWORDS: Mining, Land mines, General packet radio service, Sensors, Detection and tracking algorithms, Metals, Performance modeling, Prototyping, Algorithm development, Data modeling
The Edge Histogram Detector (EHD) is a landmine detection algorithm that has been developed for ground
penetrating radar (GPR) sensor data. It has been tested extensively and has demonstrated excellent performance.
The EHD consists of two main components. The first one maps the raw data to a lower dimension using edge
histogram based feature descriptors. The second component uses a possibilistic K-Nearest Neighbors (pK-NN)
classifier to assign a confidence value. In this paper we show that performance of the baseline EHD could
be improved by replacing the pK-NN classifier with model based classifiers. In particular, we investigate two
such classifiers: Support Vector Regression (SVR), and Relevance Vector Machines (RVM). We investigate the
adaptation of these classifiers to the landmine detection problem with GPR, and we compare their performance
to the baseline EHD with a pK-NN classifier. As in the baseline EHD, we treat the problem as a two class
classification problem: mine vs. clutter. Model parameters for the SVR and the RVM classifiers are estimated
from training data using logarithmic grid search. For testing, soft labels are assigned to the test alarms. A
confidence of zero indicates the maximum probability of being a false alarm. Similarly, a confidence of one
represents the maximum probability of being a mine. Results on large and diverse GPR data collections show
that the proposed modification to the classifier component can improve the overall performance of the EHD
significantly.
KEYWORDS: Prototyping, Mining, General packet radio service, Land mines, Sensors, Detection and tracking algorithms, Feature extraction, Metals, Environmental sensing, Control systems
The Edge Histogram Detector (EHD) is a landmine detection algorithm for sensor data generated by ground penetrating
radar (GPR). It uses edge histograms for feature extraction and a possibilistic K-Nearest Neighbors (K-NN) rule for confidence
assignment. To reduce the computational complexity of the EHD and improve its generalization, the K-NN classifier
uses few prototypes that can capture the variations of the signatures within each class. Each of these prototypes is assigned
a label in the class of mines and a label in the class of clutter to capture its degree of sharing among these classes. The
EHD has been tested extensively. It has demonstrated excellent performance on large real world data sets, and has been
implemented in real time versions in hand-held and vehicle mounted GPR. In this paper, we propose two modifications to
the EHD to improve its performance and adaptability. First, instead of using a fixed threshold to decide if the edge at a
certain location is strong enough, we use an adaptive threshold that is learned from the background surrounding the target.
This modification makes the EHD more adaptive to different terrains and to mines buried at different depths. Second, we
introduce an additional training component that tunes the prototype features and labels to different environments. Results
on large and diverse GPR data collections show that the proposed adaptive EHD outperforms the baseline EHD. We also
show that the edge threshold can vary significantly according to the edge type, alarm depth, and soil conditions.
We present a novel method for fusing the results of multiple semantic video indexing algorithms that use different
types of feature descriptors and different classification methods. This method, called Context-Dependent Fusion
(CDF), is motivated by the fact that the relative performance of different semantic indexing methods can vary
significantly depending on the video type, context information, and the high-level concept of the video segment
to be labeled. The training part of CDF has two main components: context extraction and algorithm fusion.
In context extraction, the low-level audio-visual descriptors used by the different classification algorithms are
combined and used to partition the descriptors space into groups of similar video shots, or contexts. The
algorithm fusion component identifies a subset of classification algorithms (local experts) for each context based
on their relative performance within the context. Results on the TRECVID-2002 data collections show that the
proposed method can identify meaningful and coherent clusters and that different labeling algorithms can be
identified for the different contexts. Our initial experiments have indicated that the context-dependent fusion
outperforms the individual algorithms. We also show that using simple visual descriptors and a simple K-NN
classifier, the CDF approach provides results that are comparable to the state-of-the-art methods in semantic
indexing.
KEYWORDS: Computed tomography, 3D modeling, Data modeling, Reconstruction algorithms, Visual process modeling, Visualization, Volume rendering, Software development, 3D image processing, 3D image reconstruction
The purpose of this study was to develop a 3D volume reconstruction model for volume rendering and apply this model to abdominal CT data. The model development includes two steps: (1) interpolation of given data for a complete 3D model, and (2) visualization. First, CT slices are interpolated using a special morphing algorithm. The main idea of this algorithm is to take a region from one CT slice and locate its most probable correspondence in the adjacent CT slice. The algorithm determines the transformation function of the region in between two adjacent CT slices and interpolates the data accordingly. The most probable correspondence of a region is obtained using correlation analysis between the given region and regions of the adjacent CT slice. By applying this technique recursively, taking progressively smaller subregions within a region, a high quality and accuracy interpolation is obtained. The main advantages of this morphing algorithm are 1) its applicability not only to parallel planes like CT slices but also to general configurations of planes in 3D space, and 2) its fully automated nature as it does not require control points to be specified by a user compared to most morphing techniques. Subsequently, to visualize data, a specialized volume rendering card (TeraRecon VolumePro 1000) was used. To represent data in 3D space, special software was developed to convert interpolated CT slices to 3D objects compatible with the VolumePro card. Visual comparison between the proposed model and linear interpolation clearly demonstrates the superiority of the proposed model.
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