Anqi Ye

Detection filters and algorithm fusion for ATR

Detection involves locating all candidate regions of interest (objects) in a scene independent of the object class with object distortions and contrast differences, etc., present. It is one of the most formidable problems in automatic target recognition, since it involves analysis of every local scene region. We consider new detection algorithms and the fusion of their outputs to reduce the probability of false alarm P(FA) while maintaining high probability of detection P(D). Emphasis is given to detecting obscured targets in infrared imagery.

Wavelet and Gabor transforms for detection

We consider wavelet and Gabor transforms for detection of candidate regions of interest in a 2-D scene. We generate wavelet and Gabor coefficients for each spatial region of a scene using new linear combination optical filters to reduce the output dimensionality and to simplify postprocessing. We use two sets of wavelet coefficients as indicators of edge activity to suppress background clutter. The Gabor coefficients are found to be excellent for object detection and robust to object distortions and contrast differences. We provide insight into the selection of the Gabor parameters.

Optical correlation filter fusion for object detection

We consider the detection of candidate objects (regions of interest) in a scene containing high clutter, multiple objects in different classes, independent of aspect view, with hot, cold, bimodal, and partial object variations and with high and low contrast targets. We use three different filters with each designed to produce high probability of detection (PD). We fuse the results from different outputs to reduce the probability of false alarms (PFA). All filters are realizable on an optical correlator.

Morphological and wavelet transforms for object detection and image processing

We consider the problem of detecting multiple distorted objects in an input scene with clutter. The input scenes contain different types of background clutter and multiple objects in different classes, with different object aspect views, different object representations, hot/cold/bimodal/partial object variations, and high/low contrast object variations. Several new optical morphological operations for use in the above detection problem and in other general low-level image-processing applications are described, and several examples of their use are provided. For difficult detection problems in which high detection rates and low false-alarm rates are required we combine morphological operations and optical wavelet transforms to reduce clutter and improve object detection. The details of this set of filters and initial testresults are given. The most computationally demanding operations required in all cases are realizable on an optical correlator.

Optical morphological processors: gray scale with binary structuring elements, detection, and clutter reduction

We consider morphological processing for clutter reduction and object detection. For detection, we compare a binary and gray-scale Hit-Miss Transform and find that the binary operator is preferable. For clutter reduction, we find gray-scale morphology to be preferable. We present a new gray-scale clutter reduction morphological algorithm for low clutter cases and a new algorithm for high clutter cases. In all morphological processing, we find binary structuring elements to be adequate; this is very attractive for our gray-scale morphology decomposition algorithm and its optical implementation.

Optical Gabor and wavelet transforms for scene analysis

Recent development in vision and image understanding related study reveals that a signal decomposition before processing may provide enormous useful information about the signal. Various signal decomposition models, such as the Gabor and wavelet transforms have been proposed. While the Gabor signal expansion creates a fixed resolution space-frequency signal representation, the wavelet transform provides a multi-resolution signal space-scale decomposition. Digital implementation of these transforms are computationally intensive both because of the nature of the coordinate-doubling of the transforms and due to the large quantity of convolution/correlation operations to be performed. Optics with its inherent parallel processing capability has been applied to many useful linear signal and image transformations for feature analysis and extraction. This paper is intended to study the suitability of using optical processing techniques for the signal Gabor and wavelet analysis. Gabor and wavelet transforms of both one- and two-dimensional signals and images are discussed. System parameters and limitation are analyzed. Preliminary experimental results are presented.

Correlation filter fusion for detection: morphological, wavelet, and Gabor methods

We consider the detection of candidate objects (regions of interest) in a scene containing high clutter, multiple objects in different classes, independent of aspect view, with hot/cold/bimodal/partial object variations, and with low contrast targets. We use three different filters with each designed to produce high probability of detection (PD). We fuse the results from different outputs to reduce false alarms (PFA). All filters are realizable on a correlator.

Detection filters using wavelets, Gabor, morphology, and fusion

We consider detection (location of all likely regions of interest (ROIs) in a scene where objects may be) for multiple classes of objects in 3D distortions with contrast differences and severe clutter present. Two different algorithms using Gabor basis function (GBF) filters and morphological wavelet transform (MWT) filtering are considered. New final algorithm parameters are noted. We detail: the morphological portion of the MWT algorithm, our new fusion method to combine the morphological and Gabor wavelet clutter map portions of the MWT algorithm, and the MWT threshold selection technique. Initial results using a new peak sorting scoring method and new fusion scores for multiple algorithms to reduce false alarms are noted.

Morphological wavelet transform for distortion-invariant object detection in clutter

We developed an approach combining morphological processing and wavelet transforms to detect multiple objects in an input scene. The input scene contains different types of background clutter regions and multiple objects in different classes, with different object aspect views, different object representations, hot/cold/bimodal/partial object variations, and high/low object contrast variations. Our approach provides high detection rates and low false alarm rates. The most computationally demanding operations required are realizable on an optical correlator.

Algorithm fusion for detection with reduced P FA

We consider detection (locating all objects in a scene) independent of object distortions and contrast differences and in the presence of clutter. We employ several different new detection algorithms; to reduce false alarms. We fuse (combine) the outputs from different detection algorithms. We describe a new peak sorting detection scoring algorithm and 3 different fusion algorithms to combine the results from different algorithms: binary, analog, and hierarchial fusion. Quantitative data on a distortion-invariant six object class is presented; the objects have a wide range of object contrasts including obscured objects and the objects are present in severe clutter.