This paper presents a new local edge-based level set model that does not use initial contours. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different localized region-based operators: a Gaussian mixture model-based intensity distribution estimator and the Hueckel operator. We incorporate the operator outcomes into the recently proposed local binary fitting (LBF) model as local distribution fitting (LDF) model, which enables a model without the initial contour selection, i.e., the level set function can be initialized with a random constant instead of a distance map. Thus our model overcomes the initialization sensitivity problem of most active contours. In addition, with region-based edge detection, the proposed LDF model provides more accurate and robust segmentation. Experiments on both synthetic and real images show the improved performance of our proposed model over the LBF model.

This paper presents a new image denoising model for real color photo noise removal. Our model is implemented in the hue, saturation and intensity (HSI) space. The hue and saturation denoising are combined and implemented as a complex total variation (TV) diffusion. The intensity denoising is based on a diffusion flow to minimize a new energy functional, which is constructed with intensity component statistics. Besides the common gradient-based edge stopping functions for anisotropic diffusion, specifically for color photo denoising, we incorporate an intensity-based brightness adjusting term in the new energy, which corresponds to the noise disturbance with respect to photo intensity. In addition, we use the gradient vector flow (GVF) as the new diffusion directions for more accurate and robust denoising. Compared with previous diffusion flows only based on regular image gradients, this model provides more accurate image structure and intensity noise characterization for better denoising. Comprehensive quantitative and qualitative experiments on color photos demonstrate the improved performance of the proposed model when compared with 14 recognized approaches and 2 commercial software.

This paper presents a novel anisotropic diffusion framework for color image denoising. Unlike previous approaches, our method is based on separating a color image into hue, saturation and intensity (HSI) components, and then diffusing each component with spatially varying weighted partial differential equations (PDE). The hue denoising is implemented by a new weighted orientation diffusion, and the saturation diffusion is a modified curvature flow. The intensity diffusion PDE is a combination of a gradient vector flow (GVF)-based filter and a fourth-order filter. This combined technique provides a robust and accurate denoising process, i.e., it preserves edges well and at the same time overcomes the staircase effect in smooth regions. The denoising experiments on a set of standard color images have shown satisfactory results.

This paper presents a robust and efficient skeleton-based graph matching method for object recognition and recovery applications. The novel feature is to unify both object recognition and recovery components into an image understanding system architecture, in which a complementary feedback structure can be incorporated to alleviate processing difficulties of each component alone. The idea is firstly to recognize the preliminary extracted object from a set of models using the new skeleton graph matching method, then to apply the a priori shape information of the identified model for accurate object recovery. The output of the system is the recognized and segmented object. The skeleton graph matching method is illustrated by recognizing a set of tool and animal silhouette examples with the presence of geometric transformations (translation, rotation, scaling, reflection), shape deformations and noise. Experiments of object recovery using MR knee images, have shown satisfactory results

This paper proposes a novel thresholding method for real time perspective image segmentation based on an image gray level probability distribution model. A perspective image can be modeled as a mixture of the background and the object in the image, which are farther modeled as two independent Gaussian distributions. Therefore, given an input perspective image, our method firstly derives the gray level distributions of the background and the object, respectively, based on the image histogram analysis. Then the optimum threshold can be obtained from the intersection of the derived distributions of the background and the object. Experimental results on currency watermark image segmentation for real time printing quality inspection are rather encouraging.

A divide and conquer deformable contour method is presented with an initial inside closed contour being divided into arbitrary segments, and these segments are allowed to deform separately preserving the segments' connectivity. A maximum area threshold, A/sub max/, is used to stop these outward contour segments' marching. Clear and blur contour points are then identified to partition the whole contour into clear and blur segments. A bi-directional searching method is then recursively applied to each blur segment including a search for contour-within-contour segment to reach a final close contour. Further improvements are provided by a model based searching algorithm. It is a two-step process with step 1 being a linked contour model matching operation where landmarks are extracted, and step 2 being a posteriori probability model matching and correction operation where large error segments are fine tuned to obtain the final results. The experiments include ultrasound images of pig heart, MRI brain images, MRI knee images having complex shapes with or without gaps, and inhomogeneous interior and contour region brightness distributions. These experiments have shown that the method has the capability of moving a contour into the neighboring region of the desired boundary by overcoming inhomogeneous interior, and by adapting each contour segment searching operation to different local difficulties, through a contour partition and repartition scheme in searching for a final solution.

A robust skeleton-based shape matching method for model-based shape recovery applications is presented. The object model consists of both a skeleton model and a contour segments model, which are used in tandem and in a complementary manner. Initially, the skeleton of the contour, provided by a deformable contour method (DCM), is matched against a set of object skeleton models to select a candidate model and determine the corresponding landmarks on the contours. Segments obtained from these landmarks are then matched against the detected model segments for errors. For any large segment mismatch error, a fine-tuning process, which is formulated as a maximization of a posteriori probability, given the contour segments model and image features, is performed for the final result. The skeleton matching algorithm is illustrated by using a set of animal profile examples. Experimental results of shape recovery from practical applications, such as an MR knee image, are very encouraging.

The improvements in structured lighting based 3D optical camera measurement systems have made these non-contact inspection systems more applicable. In this paper, a basic calibration problem, caused by systematical errors particular to structured lighting based 3D optical camera systems, is addressed. The analysis of these errors is discussed and two model-based approaches, line model (LM) and area model (AM), are proposed to model the pattern of systematical error. The main step of either approach is to build a lookup table based on collected planar data at predetermined working distances and orientation angles. The lookup table is then used to correct the systematical error existing in the inspected data. The experimental results show that the proposed approaches effectively improve inspection accuracy.

In this paper, a deformable contour method is formulated and derived in a constrained optimization framework. The constraint is reflected in the resulting contour evolution equation. This constraint element can be used to characterize any interior property and/or structure including the added "balloon force" used in geodesic snake as a special case, which has the potential of having expanded applications to far more complex interior. An implementation of the method is applied to a MRI brain image and the results are favorable.

A two-step model based approach to a contour extraction problem is developed to provide a solution to more challenging contour extraction problems of biomedical images. A biomedical contour image is initially processed by a deformable contour method to obtain a first order approximation of the contour. The two-step model includes a linked contour model and a posteriori probability model. Initially, the output contour from the deformable contour method is matched against the linked contour model for both model detection and corresponding landmark contour points identification. Segments obtained from these landmarks are matched for errors. Larger error are then passed on to a regionalized a posteriori probability model for further fine tuning to obtain a final result. Experiments on both MR brain images are most encouraging.