Advances in nanotechnology have resulted in a variety of exciting new nanomaterials, such as nanotubes,
nanosprings and suspended nanoparticles. Characterizing these materials is important for refining the manufacturing
process as well as for determining their optimal application. The scale of the nanocomponents makes
high-resolution imaging, such as electron microscopy, a preferred method for performing the analyses. This work
focuses on the specific problem of using transmission electron microscopy (TEM) and image processing techniques
to quantify the spatial distribution of nanoparticles suspended in a film. In particular, we focus on the
problem of determining whether the nanoparticles are located in a co-planar fashion or not. The correspondences
between particles in images acquired at different tilt angles is used as an estimate of co-planarity.
A fundamental challenge in analyzing spatial patterns in images is the notion of scale. Texture based analysis
typically characterizes spatial patterns only at the pixel level. Such small scale analysis usually fails to capture
spatial patterns that occur over larger scales. This paper presents a novel solution, termed hierarchical texture
motifs, to this texture-of-textures problem. Starting at the pixel level, spatial patterns are characterized using
parametric statistical models and unsupervised learning. Higher levels in the hierarchy use the same analysis to
characterize the motifs learned at the lower levels. This multi-level analysis is shown to outperform single-level
analysis in classifying a standard set of image textures.
Detection and tracking of moving objects is important in the analysis of video data. One approach is to maintain a background model of the scene and subtract it from each frame to detect the moving objects which can then be tracked using Kalman or particle filters. In this paper, we consider simple techniques based on salient points to identify moving objects which are tracked using motion correspondence. We focus on video with a large field of view, such as a traffic intersection with several buildings nearby. Such
scenes can contain several salient points, not all of which move between frames. Using public domain video and two types of salient points, we consider how to make these techniques computationally efficient for detection and tracking. Our early results indicate that salient regions obtained using the Lowe keypoints algorithm and the Scale-Saliency algorithm can be used successfully to track vehicles in moderate resolution video.
Shape and texture features have been used for some time for pattern recognition in datasets such as remote sensed imagery, medical imagery, photographs, etc. In this paper, we investigate shape and texture features for pattern recognition in simulation data. In particular, we explore which features are suitable for characterizing regions of interest in images resulting from fluid mixing simulations. Three texture features -- gray level co-occurrence matrices, wavelets, and Gabor filters -- and two shape features -- geometric moments and the angular radial transform -- are compared. The features are evaluated using a similarity retrieval framework. Our preliminary results indicate that Gabor filters perform the best among the texture features and the angular radial transform performs the best among the shape features. The feature which performs the best overall is dependent on how the groundtruth dataset is created.
Texture features have long been used in remote sensing applications to represent and retrieve image regions similar to a query region. Various representations of texture have been proposed based on the Fourier power spectrum, spatial co-occurrence, wavelets, Gabor filters, etc. These representations vary in their computational complexity and their suitability for representing different region types. Much of the work done thus far has focused on panchromatic imagery at low to moderate spatial resolutions, such as images from Landsat 1-7 which have a resolution of 15-30 m/pixel, and from SPOT 1-5 which have a resolution of 2.5-20 m/pixel. However, it is not clear which texture representation works best for the new classes of high resolution panchromatic (60-100 cm/pixel) and multi-spectral (4 bands for red, green, blue, and near infra-red at 2.4-4 m/pixel) imagery. It is also not clear how the different spectral bands should be combined. In this paper, we investigate the retrieval performance of several different texture representations using multi-spectral satellite images from IKONOS. A query-by-example framework, along with a manually chosen ground truth dataset, allows different combinations of texture representations and spectral bands to be compared. We focus on the specific problem of retrieving inhabited regions from images of urban and rural scenes. Preliminary results show that 1) the use of all spectral bands improves the retrieval performance, and 2) co-occurrence, wavelet and Gabor texture features perform comparably.
This paper presents an overview of our recent work on managing image and video data. The first half of the paper describes a representation for the semantic spatial layout of video frames. In particular, Markov random fields are used to characterize the spatial arrangement of frame tiles that are labeled using support vector machine classifiers. The representation is shown to support similarity retrieval at the semantic level as demonstrated in a prototype video management system. The second half of the paper describes a method for efficiently computing nearest neighbor queries in high-dimensional feature spaces in a relevance feedback framework.