Aerial target recognition using MRA, GVF snakes, and polygon approximation

The gradient vector flow (GVF) snake shows high performance at concavity convergence and initialization insensitivity, but the two components of GVF field are treated isolatedly during diffusion, this leads to the failure of GVF snake at weak edge preserving and deep and narrow concavity convergence. In this study, a novel external force for active contours named gradient vector flow over manifold (GVFOM) is proposed that couples the two components during diffusion by generalizing the Laplacian operator from flat space to manifold. The specific operator is Beltrami operator. This proposed GVFOM snake has been assessed on synthetic and real images; experimental results show that the GVFOM snake behaves similarly to the GVF snake in terms of capture range enlarging, initialization insensitivity, while provides much better results than GVF snake for weak edge preserving, objects separation, narrow and deep concavity convergence.

Active contours also known as snakes became a familiar and widely used in the field of image segmentation and restoration of historical documents in last few decades. Gradient Vector Flow (GVF) snake successes in overcome of converge to boundary concavities which represents the drawback of traditional snakes. Deep concavity problem it has become Obstacle faced GVF snake when restoring broken characters of historical documents. In this study we proposed algorithm to use genetic algorithm with GVF snake algorithm in order to optimize snake points to get right positions in deep concavity boundaries, also adding a Divergence factor as the third force to enhance the restoring and recognizing results. The experimental results show that our proposed algorithm has more capture than GVF alone.

Gradient Vector Flow (GVF) Snake has been extensively utilized in handling image segmentation and classification problems. However, GVF Snake has a drawback in that it sometimes may fail to stop the snake at weak edges, especially at the locations where a weak edge is very close to a strong one or some noise. To guide the snake toward the appropriate edges, we have added gradient directional information to the external image force to create a “directional snake”. Experiments prove that the new model limits the influence of false edge, avoid the edge leak, and obtains desired segmentation results.

Snakes, or active contours, have been widely used in image processing applications. An external force for snakes called gradient vector flow (GVF) attempts to address traditional snake problems of initialization sensitivity and poor convergence to concavities, while generalized GVF (GGVF) aims to improve GVF snake convergence to long and thin indentations (LTIs). In this paper, we find and show that both GVF and GGVF snakes essentially yield the same performance in capturing LTIs of odd widths, and generally neither can converge to even-width LTIs. Based on a thorough investigation of the GVF and GGVF fields within the LTI during their iterative processes, we identify the crux of the convergence problem, and accordingly propose a novel external force termed as component-normalized GGVF (CN-GGVF) to eliminate the problem. CN-GGVF is obtained by normalizing each component of initial GGVF vectors with respect to its own magnitude. Experimental results and comparisons against GGVF snakes show that the proposed CN-GGVF snakes can capture LTIs regardless of odd or even widths with a remarkably faster convergence speed, while preserving other desirable properties of GGVF snakes with lower computational complexity in vector normalization.

In this paper, we propose an improved gradient vector flow (GVF) snake to address the problem of poor convergence of GVF snake for the deep concavities in image segmentation. The major contribution is that new external force field is proposed combining the properties of GVF snake model and magnetostatic active contour (MAC) model, and the new external force field can help move the snake contour into deep boundary concavities. The proposed algorithm has been tested with experiments on various types of images, and significant improvement has been achieved in the convergence for approaching boundary concavities compared with the existing GVF snake method.

Foreground extraction is an important and challenging problem in many applications of computer vision. Most existing algorithms either require user intervention as hard constraints or demand special inputs for extra information. Thus, an automatic foreground extraction algorithm from a single image is proposed in this paper. A Gaussian image pyramid is constructed and the gradient vector flow (GVF) snake is adopted at the coarsest level to generate a rough contour of the object which is then upsampled and serves as the initial input to GVF snake in the next level. This process repeats until the estimated contour is propagated to the finest level. Based on the result in the finest level, a binary mask can be generated accordingly and becomes the initial constraint in the segmentation. The proposed segmentation step includes two novel schemes, which simulate the Latent Dirichlet Allocation (LDA) generative model and a probabilistic stochastic process called Chinese restaurant process. With these mechanisms, the Gaussian Mixture Models adaptively determine the number of components for foreground and background individually. As a result, the proposed method is expected to not only produce a satisfactory result of foreground extraction automatically with more robustness and adaptation but also serve as a good preprocessing to improve the performance and accuracy for tasks in computer vision.

X-Ray inspection is used in food industry for safety inspection against foreign objects and especially effective in terms of metallic and non-metallic foreign body detection. But few work has been done for the recognition of foreign objects found in food, this is partly due to the inability to precisely obtain their contours. In this attempt to solve the problem, a method of X-Ray image segmentation based on gradient vector flow (GVF) snake theory is developed for the inspection of foreign objects in metallically packaged food. The objective of the segmentation is to extract the contour of foreign object from the radiographic image as precisely as possible.

Starch is the most important carbohydrate in the human diet and contained in many staple foods such as rice, wheat, corn, potatoes and cassava, etc. Currently, microscopic imaging technology is one of the most effective ways to investigate and analyze the structure of starches. Segmentation of starch granules is a necessary step for starch granule structure analysis such as starch granules detection, shape recognition, and size computation. This article investigates a new method based on GVF (gradient vector flow) snake deformable model for starch granules segmentation. The proposed method focuses on full automatic segmentation on granules, especially on separation of adjacent and contacted starch granules, which occur widely in microscopic images. A novel energy function based on position and intensity is introduced into the directional gradient computation, thus the directional gradient is used to obtain the directional GVF snake, which drives the deforming contours to the real contours of multiple granules. To demonstrate the good ability of the proposed method, we segment 30 starch granule images and compare it with the level set method, experimental results show that the new method can separate multiple starch granules successfully; especially it works much better on overlapping objects segmentation than the level set method.

International educational institutions teaching all subjects in English, from elementary to graduate schools, are proliferating across Asia. Many Asian school personnel, both local and foreign, have amassed a plethora of expertise and experience re: teaching english to speakers of other languages (TESOL) and even Business English Teaching (BET), but the relatively new area of business and technology teaching in english (BTTIE) remains <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">terra</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incognita</i> . Typically, at the average Asian educational institution: the foreign Native English Speaking Teachers (NESTs) possess neither extensive business/technology backgrounds nor advanced degrees in related subjects; the local and foreign business/technology teachers either aren't proficient in English or are capable but not considered NESTs. Newcomers might be both content and teaching experts, but it's unlikely they'll know how to teach effectively in Asia. This paper: describes the characteristics and qualifications re: the ideal BTTIE educators for Asian international schools; suggests alternative sources and processes for obtaining teachers due to the current/future shortage of such professionals. Educational technology utilization is touted as a necessary factor for both teaching and teachers. The interstate new teacher assessment and support consortium (INTASC) Standards are cited and adapted to the BTTIE-specific educator/education discussion.

Medical image segmentation is one of the key technologies in computer aided diagnosis. Due to the complexity and diversity of medical images, the wavelet multi-scale analysis is introduced into GVF (gradient vector flow) snake model. The modulus values of each scale and phase angle values are calculated using wavelet transform, and the local maximum points of modulus values, which are the contours of the object edges, are obtained along phase angle direction at each scale. Then, location of the edges of the object and segmentation is implemented by GVF snake model. The experiments on some medical images show that the improved algorithm has small amount of computation, fast convergence and good robustness to noise.

Ankle cartilage surface segmentation is a prestep to compute the cartilage thickness which is used to evaluate the treatment of ankle joint diseases. Because of the un-ideal imaging condition, the acquired ankle images are often noised and the cartilage parts in the images are often disconnected, thus traditional techniques such as gradient-based edge detection are not suitable for the segmentation of these surfaces. In this paper, we developed an active contour model based ankle cartilage surface segmentation algorithm. The algorithm used gradient vector flow (GVF) as external forces. In order to make the GVF snake more stable and to converge at the correct surfaces, directional gradient is used to produce the gradient vector flow field. Experimental results indicate that the new approach utilizing directional gradient is more robust than the traditional GVF snake.

Multi-layer segmentation framework for cell nuclei using improved GVF Snake model, Watershed, and ellipse fitting

In the field of cardiac MR image segmentation, active contour models, or snakes have been extensively used, owing to their promising results and to the numerous extensions proposed to improve their performance. This paper explores a methodology for evaluating cardiac MR image segmentation algorithms, which assesses the distance between computer-generated and the observer's hand-outlined boundaries. This metric was applied to various external force extensions of the traditional snake, since no systematic comparison has been performed. Cardiac MRI from six patients were analyzed. Imaging was performed on a 1.5 T MR scanner with ECG-gated balanced steady-state free precession (b-SSFP) sequences. Segmentation performances were established for traditional snake, gradient vector flow snake, standard- and guided- pressure force-based snake. The use of a pre-treatment with non-linear anisotropic filtering was also compared to non-filtered images. Agreement between manual and segmentation algorithms was satisfactory for ejection fraction for every segmentation scheme. However end-systolic and end-diastolic volumes were systematically underestimated. The developed regional error metric provided a more rigorous evaluation of the segmentation schemes in comparison to the classical derived parameters based on left ventricle volume estimation, usually used in functional cardiac MR studies. These derived parameters can furthermore mask local segmentation errors.

Interactive curved planar reformation based on snake model

A supervised segmentation algorithm based on ant colony for putamen region in brain MRI is proposed. Since the variance of the putamen template and the searching contour is adopted as the object function, the solution process for the supervised ant colony algorithm model proposed is transformed as the process of the minimum of the object function, or as the optimal searching path problem in the search space. A new scheme for finding search space is proposed, and discusses how to decide the optimal searching scheme. By a general hypothesis for the template, the solution process for the problem is described in detail. A great deal of experimental results show that the supervised segmentation algorithm based on ant colony proposed is better than the Fuzzy c-Mean segmentation, region growth segmentation, GVF(Gradient Vector Flow) Snake model segmentation and the basic ant colony segmentation in the shape of the real template, the shape comparability between adjoining slices and the continuity in single slice. Moreover, the convergence speed of the proposed algorithm is the fastest than the others.