This letter presents a benchmarking study for aerial image segmentation. We construct an image data set consisting of various aerial scenes. Segmentations generated by different human subjects are used as ground truth. We analyze the consistency between segmentations from different subjects. We select six leading segmentation algorithms, which include not only the algorithms specifically designed for aerial images but also more generally applicable algorithms. We also select a recently proposed algorithm due to its promising performance in handling texture regions. We apply these algorithms to the aerial image data set and quantitatively evaluate their performance. We interpret the evaluation results based on the characteristics of algorithms, which provide general guidance for selecting proper algorithms in specific applications.

As a result of increasing geospatial image libraries, many algorithms are being developed to automatically extract and classify regions of interest from these images. However, limited work has been done to compare, validate and verify these algorithms due to the lack of datasets with high accuracy ground truth annotations. In this paper, we present an approach to generate a large number of synthetic images accompanied by perfect ground truth annotation via learning scene statistics from few training images through Maximum Entropy (ME) modeling. The ME model [1,2] embeds a Stochastic Context Free Grammar (SCFG) to model object attribute variations with Markov Random Fields (MRF) with the final goal of modeling contextual relations between objects. Using this model, 3D scenes are generated by configuring a 3D object model to obey the learned scene statistics. Finally, these plausible 3D scenes are captured by ray tracing software to produce synthetic images with the corresponding ground truth annotations that are useful for evaluating the performance of a variety of image analysis algorithms.

The design of benchmark imagery for validation of image an notation algorithms is considered. Emphasis is placed on imagery that contains industrial facilities, such as chemical re fineries. An application-level facility ontology is used as a means to define salient objects in the benchmark imagery. Instrinsic and extrinsic scene factors important for comprehensive validation are listed, and variability in the benchmarks discussed. Finally, the pros and cons of three forms of bench mark imagery: real, composite and synthetic, are delineated.

Identification and automatic labeling of facilities in high-resolution satellite images is a challenging task as the current thematic classification schemes and the low-level image features are not good enough to capture complex objects and their spatial relationships. In this paper we present a novel algorithm framework for automated semantic labeling of large image collections. The framework consists of various segmentation, feature extraction, vector quantization, and Latent Dirichlet Allocation modules. Initial experimental results show promise as well as the challenges in semantic classification technology development for nuclear proliferation monitoring.

With increasing understanding and availability of nuclear technologies, and increasing persuasion of nuclear technologies by several new countries, it is increasingly becoming important to monitor the nuclear proliferation activities. There is a great need for developing technologies to automatically or semi-automatically detect nuclear proliferation activities using remote sensing. Images acquired from earth observation satellites is an important source of information in detecting proliferation activities. High-resolution remote sensing images are highly useful in verifying the correctness, as well as completeness of any nuclear program. DOE national laboratories are interested in detecting nuclear proliferation by developing advanced geospatial image mining algorithms. In this paper we describe the current understanding of geospatial image mining techniques and enumerate key gaps and identify future research needs in the context of nuclear proliferation.

Conventional X-ray computed tomography (CT) imaging is based on the assumption that the entire cross-section of an object is illuminated with X-rays at each view angle. When imaging a large object, a large detector array is thus needed. As an alternative, we propose to offset a normal sized detector array such that slightly more than one half of the required projection data is acquired. During reconstruction, the missing data is accounted for by means of an interpolation and weighting scheme. This algorithmic approach for extending the field of view, which we here present in the context of the popular Feldkamp algorithm, is simple but effective. Supportive experimental results are provided based on simulated phantom data as well as real data obtained from a MicroCAT/sup TM/ which is a circular orbit microCT system for small animal imaging.

Effective image segmentation of a digitized scene into a set of recognizable objects requires the development of sophisticated scene analysis algorithms. Progress in this area has been made through the development of a statistical-based deformable model that improves upon existing point distribution models (PDMs) for boundary-based object segmentation. Existing PDM boundary finding techniques often suffer from the shortcoming that global shape and gray-level information are treated independently during boundary optimization. A deformable model algorithm is under development in which the objective function used during optimization of the boundary encompasses several important characteristics. Most importantly the objective function includes both shape and gray-level characteristics, so optimization occurs with respect to both pieces of information simultaneously. This algorithm has been applied to geometric test images and a simple industrial-type scene for which results are presented.

This paper describes the application of a new probabilistic shape and appearance model (PSAM) algorithm to the task of detecting polycystic kidney disease (PKD) in X-ray computed tomography images of laboratory mice. The genetically engineered PKD mouse is a valuable animal model that can be used to develop new treatments for kidney-related problems in humans. PSAM is a statistical-based deformable model that improves upon existing point distribution models for boundary-based object segmentation. This new deformable model algorithm finds the optimal boundary position using an objective function that has several unique characteristics. Most importantly, the objective function includes both global shape and local gray-level characteristics, so optimization occurs with respect to both pieces of information simultaneously. PSAM is employed to segment the mouse kidneys and then texture measurements are applied within kidney boundaries to detect PKD. The challenges associated with the segmentation non-rigid organs along with the availability of a priori information led to the choice of a trainable, deformable model for this application. In 103 kidney images that were analyzed as part of a preclinical animal study, the mouse kidneys and spine were segmented with an average error of 2.4 pixels per boundary point. In all 103 cases, the kidneys were successfully segmented at a level where PKD could be detected using mean-of-local-variance texture measurements within the located boundary.

The concept of content-based image compression (CBIC) has far-reaching effects in the areas of archiving and telecommunications. This paper presents some pilot-study results from the application of CBIC to mammography. Unlike traditional compression approaches, CBIC first analyzes the content of the data before the compression takes place. In this approach, prior to compression, the data is pre-processed and is segmented into two non-overlapping regions: (1) focus-of-attention regions (FARs) that contain the "important" segments of the data, and (2) background regions. Subsequently, the former regions are compressed using a lossless compression technique (maintaining fidelity), while the latter regions are compressed with the aid of a lossy technique (attaining large reductions in data). The intended result is an optimal balance between data reduction and data fidelity. In this case, compression ratios 5-6 times greater than that of lossless compression alone can be reached while preserving the important information.

A technique for intelligent dilation of objects in an image was developed that, when compared to standard isotropic techniques, can create a better representative resultant image for subsequent morphological analysis. Individual masses in an image are forced to grow in preferential directions with varying degrees of strength according to a set of morphological parameters. These parameters are determined by applying a model based on gravitational force to determine the strength and direction of the "attraction force" between objects. The result of this analysis is a collection of normalized dilation vectors for each object in the image, where each vector represents a potential direction for growth. Results of the application of this algorithm are shown on binary images of defect distributions on semiconductor wafers where a single defect (e.g., a scratch) can be made up of numerous, disconnected blobs.

The article is a work-in-progress report of a research endeavor that deals with the design and development of a novel approach to computer aided diagnosis (CAD) of mammographic images. With the initial emphasis being on the analysis of microcalcifications, the proposed approach defines a synergistic paradigm that utilizes new methodologies together with previously developed techniques. The new paradigm is intended to promote a higher degree of accuracy in CAD of mammograms with an increased overall throughput. The process of accomplishing these goals is initiated by the fractal encoding of the input image, which gives rise to the generation of focus-of-attention regions (FARs), that is, regions that contain anomalies. The primary thrust of this work is to demonstrate that by considering FARs, rather than the entire input image, the performances of the ensuing processes (i.e., segmentation, feature extraction, and classification) are enhanced in terms of accuracy and speed. An experimental study is included that demonstrates the impact of FAR generation on the process of microcalcification segmentation.