Feature Extraction and Selection From the Perspective of Explosive Detection

Sengupta, S K

关键词: ASPECT RATIO;    ATTENUATION;    CLASSIFICATION;    COMPUTERIZED TOMOGRAPHY;    DETECTION;    DIELECTRIC MATERIALS;    ENERGY LEVELS;    EXPLOSIVES;    MODIFICATIONS;    ORIENTATION;    PATTERN RECOGNITION;    PERFORMANCE;    RESOLUTION;    ROTATION;    SAMPLING;    SHAPE;    TEMPERATURE MEASUREMENT;    TEXTURE;    TRANSFORMATIONS;   

DOI  :  10.2172/970667
RP-ID  :  LLNL-TR-416455
PID  :  OSTI ID: 970667
Others  :  TRN: US201003%%253

学科分类：工程和技术（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

Features are extractable measurements from a sample image summarizing the information content in an image and in the process providing an essential tool in image understanding. In particular, they are useful for image classification into pre-defined classes or grouping a set of image samples (also called clustering) into clusters with similar within-cluster characteristics as defined by such features. At the lowest level, features may be the intensity levels of a pixel in an image. The intensity levels of the pixels in an image may be derived from a variety of sources. For example, it can be the temperature measurement (using an infra-red camera) of the area representing the pixel or the X-ray attenuation in a given volume element of a 3-d image or it may even represent the dielectric differential in a given volume element obtained from an MIR image. At a higher level, geometric descriptors of objects of interest in a scene may also be considered as features in the image. Examples of such features are: area, perimeter, aspect ratio and other shape features, or topological features like the number of connected components, the Euler number (the number of connected components less the number of 'holes'), etc. Occupying an intermediate level in the feature hierarchy are texture features which are typically derived from a group of pixels often in a suitably defined neighborhood of a pixel. These texture features are useful not only in classification but also in the segmentation of an image into different objects/regions of interest. At the present state of our investigation, we are engaged in the task of finding a set of features associated with an object under inspection ( typically a piece of luggage or a brief case) that will enable us to detect and characterize an explosive inside, when present. Our tool of inspection is an X-Ray device with provisions for computed tomography (CT) that generate one or more (depending on the number of energy levels used) digitized 3-dimensional attenuation images with a voxel resolution of the order of one quarter of a milimeter. In the task of feature extraction and subsequent selection of an appropriate subset thereof, several important factors need to be considered. Foremost among them are: (1) Definition of the sampling unit from which the features will be extracted for the purpose of detection/ identification of the explosives. (2) The choice of features ( given the sampling unit) to be extracted that can be used to signal the existence / identity of the explosive. (3) Robustness of the computed features under different inspection conditions. To attain robustness, invariance under the transformations of translation, scaling, rotation and change of orientation is highly desirable. (4) The computational costs in the process of feature extraction, selection and their use in explosive detection/ identification In the search for extractable features, we have done a thorough literature survey with the above factors in mind and come out with a list of features that could possibly help us in meeting our objective. We are assuming that features will be based on sampling units that are single CT slices of the target. This may however change when appropriate modifications should be made to the feature extraction process. We indicate below some of the major types of features in 2- or 3-dimensional images that have been used in the literature on application of pattern recognition (PR) techniques in image understanding and are possibly pertinent to our study. In the following paragraph, we briefly indicate the motivation that guided us in the choice of these features, and identify the nature of the constraints. The principal feature types derivable from an image will be discussed in section 2. Once the features are extracted, one must select a subset of this feature set that will retain the most useful information and remove any redundant and irrelevant information that may have a detrimental effect on the classifier performance. This is discussed in section 3. Section 4 provides a brief summary.