https://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/rui99_cbir_survey.pdf

Image Retrieval

The problems of segmenting a noisy intensity image and tracking a nonrigid object in the plane are discussed. In evaluating these problems, a technique based on an active contour model commonly called a snake is examined. The technique is applied to cell locomotion and tracking studies. The snake permits both the segmentation and tracking problems to be simultaneously solved in constrained cases. A detailed analysis of the snake model, emphasizing its limitations and shortcomings, is presented, and improvements to the original description of the model are proposed. Problems of convergence of the optimization scheme are considered. In particular, an improved terminating criterion for the optimization scheme that is based on topographic features of the graph of the intensity image is proposed. Hierarchical filtering methods, as well as a continuation method based on a discrete sale-space representation, are discussed. Results for both segmentation and tracking are presented. Possible failures of the method are discussed. >

Tracking deformable objects in the plane using an active contour model

ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases

A new active contour model for finding and mapping the outer cortex in brain images is developed. A cross-section of the brain cortex is modeled as a ribbon, and a constant speed mapping of its spine is sought. A variational formulation, an associated force balance condition, and a numerical approach are proposed to achieve this goal. The primary difference between this formulation and that of snakes is in the specification of the external force acting on the active contour. A study of the uniqueness and fidelity of solutions is made through convexity and frequency domain analyses, and a criterion for selection of the regularization coefficient is developed. Examples demonstrating the performance of this method on simulated and real data are provided. >

/pdf/an-active-contour-model-for-mapping-the-cortex-11fpbtyg5d.pdf

An active contour model for mapping the cortex

This paper deals with semi-automatic linear feature extraction from digital images for GIS data capture, where the identification task is pe$ormed manually on a single image, while a special automatic digital module performs the high precision feature tracking in two-dimensional (2-0) image space or even three-dimensional (3-0) object space. A human operator identifies the object from an on-screen display of a digital image, selects the particular class this object belongs to, and provides a very few coarsely distributed seed points. subseq;ently, with th;?sk seed as an approximation of the ~osition and sham the linear feature will be extracted automatically by either a dynamic programming approach or by LSB-S~~~~S [Least-Squares E-spline Snakes). With dynamic programming, the optimization problem is set up as a discrete multistage decision process and is solved by a "timedelayed" algorithm. It ensures global optimality, is numerically stable, and allows for hard constraints to be enforced on the solution. In the least-squares approach, we combine three types of observation equations, one radiometric, formulating the matching of a generic object model with image data, and two that express the internal geometric constraints of a curve and the location of operator-given seed points. The solution is obtained by solving a pair of independent normal equations to estimate the parameters of the spline curve. Both techniques can be used in a monoplotting mode, which combines one image with its underlying DTM. The LSB-S~~~~S approach is also implemented in a multi-image mode, which uses multiple images simultaneously and provides for a robust and mathematically sound full 3D approach. These techniques are not restricted to aerial images. They can be applied to satellite and close-range images as well. The issues related to the mathematical modeling of the proposed methods are discussed and experimental results are shown in this paper too.

Semi-Automatic Linear Feature Extraction by Dynamic Programming and LSB-Snakes

The stability of active contour models or 'snakes' is studied. It is shown that the modification of snake parameters using adaptive systems improves both the stability of the snakes and the boundaries obtained. The adaptive snakes perform better with images of varying contrasts, noisy images and images with different curvatures along the boundaries. The computational costs at each iteration for the adaptive snakes is still of order N, where N is the number of points on the snakes. Comparisons of the results for non-adaptive and adaptive snakes are shown using both computer simulations and satellite images.

Adaptive snakes: control of damping and material parameters

The DE-1 satellite has gathered over 500,000 images of the Earth's aurora. This data set provides a realistic testbed for developing algorithms for scientific image databases. Scientists studying the aurora currently need to browse through large numbers of images to find events suitable for further scientific studies. They select or reject an image based on a variety of visual queues, including shape, size, and intensity. This paper describes a system currently under development for selecting interesting events based on image content. We use boundaries from the images to outline the aurora, and then to extract features that relate to shape, size, and intensity. These features are then input into a supervised decision tree classifier. The system retrieves images of potential interest to the user. The user makes the final decision regarding the use of the images retrieved. The algorithm is applied to hundreds of DE-1 satellite images to find `quiet' versus `active' auroras, after being initially trained by the user. The system's advantage over neural networks is that the scientists may inspect the event selection process by studying the decision tree generated.

Content-based event selection from satellite images of the aurora

Image-processing techniques for extracting the characteristics of lead and pressure ridge features in SAR images of sea ice are reported. The methods are applied to a SAR image of the Beaufort Sea collected from the Seasat satellite on October 3, 1978. Estimates of lead and ridge statistics are made, e.g., lead and ridge density (number of lead or ridge pixels per unit area of image) and the distribution of lead area and orientation as well as ridge length and orientation. The information derived is useful in both ice science and polar operations for such applications as albedo and heat and momentum transfer estimates, as well as ship routing and offshore engineering.

Extraction Of Lead And Ridge Characteristics From SAR Images Of Sea Ice

This paper describes a technique for interactive, computer-assisted boundary tracking from two-dimensional images. Once the boundaries of an object are known, it is possible to extract any number of features to be used for subsequent search or retrieval based on image content. The technique combines manual inputs from the user with machine inputs, generated from edge detection algorithms, for assisting in the extraction of the boundaries. This allows for the quick extraction of boundaries by combining the capabilities of the user and the computer. The user is adept at quickly locating an object of interest and at drawing a very rough outline of the object. The computer is adept at quickly making a large number of calculations that refine the rough outline generated by the user. The performance of the technique was tested using both computer simulations and real images. The performance of the technique degrades gracefully in the presence of noise.

Computer-assisted extraction of boundaries from images

An automated algorithm for finding the inner boundary based on recently proposed computer vision technique is described. The algorithm is analogous to solving the equations of motion for an elastic curve, where the forces are provided by the image. The resulting equilibrium position of the elastic curve provides an automated method for finding the shape and location of the inner boundary of the auroral oval. Two methods for the evaluation of the automated algorithm, both based on the comparisons with manual measurements, are developed. The first method compares the areas within the automated and the manual boundaries. The second method measures the overlap between the interiors of the two boundaries. The expected variation between two sets of manual measurements is used to set an upper bound to the allowed discrepancy between the automated results and a single set of manual measurements. The algorithm, when tested with 71 satellite images, is found to perform best for those images without overlap between the aurora and the dayside hemisphere.

Ramin Samadani

Papers

Adaptive snakes: control of damping and material parameters

Content-based event selection from satellite images of the aurora

Extraction Of Lead And Ridge Characteristics From SAR Images Of Sea Ice

Computer-assisted extraction of boundaries from images

Evaluation Of An Elastic Curve Technique For Finding The Auroral Oval From Satellite Images Automatically