Algorithms for the Detection of Resolved and Unresolved Targets in the Infrared Bands

This dissertation proposes algorithms for the detection of both resolved and unresolved targets in the infrared bands. Recent breakthroughs in deep learning have spurred major advancements in computer vision, but most of the attention and progress has been focused on RGB imagery from the visual band. The infrared bands such as Long Wave Infrared (LWIR), Medium Wave Infrared (MWIR), Short Wave Infrared (SWIR) and Near Infrared (NIR) each respond differently to physical phenomena, providing information that can be used to better understand the environment. The first task addressed is that of detecting vehicles in heavy clutter in MWIR imagery. A specialized network using a combination of analytically derived filters and a convolutional neural network trained using a novel objective function based on a target to clutter ratio is proposed which shows significant advantages in probability of detection and false alarm rate. The next task is that of domain adaptation where the network is deployed in a scenario different from that for which it was trained. The previously described network is adapted on the fly to improve results for new clutter data. Next, the task of hostile fire detection is considered where the unresolved image of an anti-tank guided missile launch is detected. An analysis of the relative utility of the IR bands is conducted, and data driven and parametric learning algorithms are presented which achieve a high probability of detection with a very low false alarm rate on a multi-spectral data set created by combining real IR video with radiometrically correct, synthesized missile launches at varying ranges. Finally, two methods for classifiers in the field to estimate the actual class probabilities of their environment to improve results are presented

Designing a symmetric classifier for image annotation using multi-layer sparse coding

Automatic annotation of images with descriptive words is a challenging problem with vast applications in the areas of image search and retrieval. This problem can be viewed as a label-assignment problem by a classifier dealing with a very large set of labels, i.e., the vocabulary set. We propose a novel annotation method that employs two layers of sparse coding and performs coarse-to-fine labeling. Themes extracted from the training data are treated as coarse labels. Each theme is a set of training images that share a common subject in their visual and textual contents. Our system extracts coarse labels for training and test images without requiring any prior knowledge. Vocabulary words are the fine labels to be associated with images. Most of the annotation methods achieve low recall due to the large number of available fine labels, i.e., vocabulary words. These systems also tend to achieve high precision for highly frequent words only. On the other hand, text mining literature discusses a general trend where relatively rare/moderately frequent words are more important for search retrieval process than the extremely frequent words. Our system not only outperforms various previously proposed annotation systems, but also achieves symmetric response in terms of precision and recall. Our system scores and maintains high precision for words with a wide range of frequencies. Such behavior is achieved by intelligently reducing the number of available fine labels or words for each image based on coarse labels assigned to it

Designing A Symmetric Classifier For Image Annotation Using Multi-Layer Sparse Coding

Automatic annotation of images with descriptive words is a challenging problem with vast applications in the areas of image search and retrieval. This problem can be viewed as a label-assignment problem by a classifier dealing with a very large set of labels, i.e., the vocabulary set. We propose a novel annotation method that employs two layers of sparse coding and performs coarse-to-fine labeling. Themes extracted from the training data are treated as coarse labels. Each theme is a set of training images that share a common subject in their visual and textual contents. Our system extracts coarse labels for training and test images without requiring any prior knowledge. Vocabulary words are the fine labels to be associated with images. Most of the annotation methods achieve low recall due to the large number of available fine labels, i.e., vocabulary words. These systems also tend to achieve high precision for highly frequent words only. On the other hand, text mining literature discusses a general trend where relatively rare/moderately frequent words are more important for search retrieval process than the extremely frequent words. Our system not only outperforms various previously proposed annotation systems, but also achieves symmetric response in terms of precision and recall. Our system scores and maintains high precision for words with a wide range of frequencies. Such behavior is achieved by intelligently reducing the number of available fine labels or words for each image based on coarse labels assigned to it