A Hybrid Method for Automatic and Highly Precise VHD Background Removal

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.

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.

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

Interactive curved planar reformation based on snake model

The traditional method to extract target contour from aerial target image is changing the aerial image into a gray level image with multiple thresholds or binary image with single threshold. From the edge of target, contour can be extracted according to the changed value. The traditional method is useful only when contrast between target and background is in the proper degree. Snakes are curves defined within an image domain that can move under the influence of internal force coming from within the curve itself and external forces are defined so that the snake will conform to an object boundary or other desired features within an image. Snakes have been proved an effective method and widely used in image processing and computer vision. Snakes synthesize parametric curves within an image domain and allow them to move toward desired edges. Particular advantages of the GVF(Gradient Vector Flow) snakes over a traditional snakes are its insensitivity to initialization and its ability to move into boundary concavities. Its initializations can be inside, outside, or across the object’s boundary. The GVF snake does not need prior knowledge about whether to shrink or expand toward the boundary. This increased capture range is achieved through a diffusion process that does not blur the edges of themselves. Affected by the light from different incident angle, the brightness of aerial target surface changed greatly in a complicate mode. So the GVF snakes is not fast, accurate and effective all the time for this kind of images. A new contour extracting method, GVF Snakes Combined with wavelet multi-resolution Analysis is proposed in this paper. In this algorithm, bubble wavelet is used iteratively to do the multi resolution analysis in the order of degressive scale before GVF Snakes is used every time to extract accurate contour of target. After accurate contour is extracted, polygon approximation is used to extract characteristics to realize the recognition of aerial target. The process is in the following: Step 1: use bubble wavelet filter to cut big part of the noises, weakening false edges. Step 2: initialize active contour and control the contour’s move according to GVF to get a new contour. Step 3: decrease the scale of filter, and use the new contour as the initial contour and control the contour’s move to get new contour again. Step 4: repeat step 3 till the set scale is reached. The last new contour is the final contour. Step 5: find the center determine an axis by calculate distance between every point on the final contour to the center. Step 6: adjust the distance threshold and combine the points until the contour is changed into a polygon with fixed angle number which is best fit the target recognition demand. Step 7: use the polygon to match the target plate to recognize target. Applied the new algorithm to aerial target images of a helicopter and a F22 battleplan, the contour extraction and polygon approximation results show that targets can be matched and recognized successfully. This paper mainly focuses on contour extraction and polygon approximation in the recognition area.

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.

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to pre-process the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing state-of- the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean technique proposed by Yap et. al., in proving the proposed method's robustness and accuracy.

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.

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.

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.

Human eye is the most sophisticated organ, with perfectly interrelated subsystems such as retina, pupil, iris cornea, lens and optic nerve. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. Optic disc helps to identify the different stages of DR, and glaucoma. In this paper, a novel automated, reliable and efficient optic disc localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the optic disc (OD) due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy histon (A-IFSH) based segmentation to segment optic disc in retinal fundus images. Optic disc pixel intensity and column wise neighbourhood operation is employed to locate and isolate the optic disc. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous and 31 DR images. Our proposed method yielded precision-0.93, recall-0.91, F-score-0.92 and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with Otsu and Gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.

An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method, and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally, an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake, and by using K-means and the EPF algorithm, multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.

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.