Contours For Image Segmentation Research Articles

Noise Elimination and Contour Detection Based on Innovative Target Image Contour Coding Algorithm

At present, the image mining is mainly based on its local and key features, which focuses on its texture and statistical grayscale features, but it focuses on its edge and shape features rarely. However, the contour is also an important feature for image shape recognition. In this paper, a good target image contour coding algorithm was adopted, and an LCV segmentation model with good image boundary acquisition capability that can reflect the target image contour features was selected for the original image contour segmentation. The detailed features analysis of the contour coding algorithm was carried out through the experiments; the experimental results showed that the algorithm was a significant technological breakthrough in image feature extraction and recognition.

The algorithm for contour segmentation of images with hierarchical objects on the basis of the Canny method with use of wavelet-transformation

The algorithm for contour segmentation of images with hierarchical objects on the basis of the Canny method with use of wavelet-transformation

CANNY-BASED METHOD OF IMAGE CONTOUR SEGMENTATION

The paper presents modification of the image contour segmentation method based on Canny method. A distinctive feature of proposed approach is the usage of wavelet-functions as underlining transformation. It allows to obtain a sequence of hierarchical object contour preparations with an adjusted detailing. This approach is a part of the object recognition information technology that significantly reduces the size of information processing of video surveillance system.

Active contours for image segmentation using complex domain‐based approach

A complex domain-based approach of active contour model has been proposed for image segmentation which deforms iteratively to partition an image into various useful regions. A new region-based complex pressure force function has been designed which gives shape to the dynamic active contour using image forces, and efficiently control the propagation of moving interface. This model makes the level set function binary, uses Gaussian smoothing kernel to regulate and avoid re-initialisation procedure. The working scheme of the model is as: the real image data is converted into complex data by iota (i) times and the average iota (i) times of horizontal and vertical components of gradient is inserted in the model to develop complete complex gradient. The proposed model can be implemented by simple finite difference scheme. The efficiency and robustness of the authors model have been verified.

Double regularization control based on level set evolution for tablet packaging image segmentation

This paper proposes a novel double regularization control (DRC) method which is used for tablet packaging image segmentation. Since the intensities of tablet packaging images are inhomogenous, it is difficult to make image segmentation. Compared to methods based on level set, the proposed DRC method has some advantages for tablet packaging image segmentation. The local regional control term and the rectangle initialization contour are first employed in this method to quickly segment uneven grayscale images and accelerate the curve evolution rate. Gaussian filter operator and the convolution calculation are then adopted to remove the effects of texture noises in image segmentation. The developed penalty energy function, as regularization term, increases the constrained conditions based on the gradient flow conditions. Since the potential function is embedded into the level set of evolution equations and the image contour evolutions are bilaterally extended, the proposed method further improves the accuracy of image contours. Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy, and achieves better results for image contour segmentation compared to other level set methods.

Image contour segmentation in hardware

The use of Behavioural Synthesis for hardware generation of a contour-based image segmentation method is considered. The segmentation method chosen, is a well-known, state-of-the-art, robust, efficient and fast-converging one, that combines functionals depending on the curve geometry and image properties in a level-set framework. The cost function sought to be minimized, is formulated as a weighted sum of three integral measures; a robust alignment term that leads the evolving surface to the edges of the desired object, a minimal variance term that measures the homogeneity inside and outside the object, and a geodesic active surface term that is used mainly for regularization. The algorithm is initially implemented in MatLab and ADA and subsequently, it is ported to our Behavioural Synthesis tool, the CCC HLS framework, which is capable of delivering correct-by-construction RTL VHDL implementations of computation-intensive applications. This way, behavioural ADA specifications are transformed into RTL micro-architectures which then can be easily implemented by commercial RTL synthesizers.

Creeping Contours: A Multilabel Image Segmentation Method for Extracting Boundary Surfaces of Parts in Volumetric Images

In this work, we introduce a method named creeping contours for image segmentation into component parts for the purpose of extracting the boundary surfaces of these parts. Creeping contours are contours that expand following a speed function defined by the gradient and curvature at contour points, starting from an initial contour position defined either manually or automatically. Contours in the image creep simultaneously at different speeds, while labels are assigned to contour pixels by the defined creeping condition. We also demonstrate the effectiveness of the proposed method by segmenting 2D grayscale images and 3D volumetric computed tomography images of mechanical parts into multiple segments and generating the boundary surfaces of these parts.

Generalized Newton-Type Methods for Energy Formulations in Image Processing

Many problems in image processing are addressed via the minimization of a cost functional. The most prominently used optimization technique is gradient-descent, often used due to its simplicity and applicability where other techniques, e.g., those coming from discrete optimization, cannot be applied. Yet, gradient-descent suffers from slow convergence, and often to just local minima which highly depend on the initialization and the condition number of the functional Hessian. Newton-type methods, on the other hand, are known to have a faster, quadratic convergence. In its classical form, the Newton method relies on the $L^2$-type norm to define the descent direction. In this paper, we generalize and reformulate this very important optimization method by introducing Newton-type methods based on more general norms. Such norms are introduced both in the descent computation (Newton step) and in the corresponding stabilizing trust-region. This generalization opens up new possibilities in the extraction of the Newton step, including benefits such as mathematical stability and the incorporation of smoothness constraints. We first present the derivation of the modified Newton step in the calculus of variation framework needed for image processing. Then, we demonstrate the method with two common objective functionals: variational image deblurring and geometric active contours for image segmentation. We show that in addition to the fast convergence, norms adapted to the problem at hand yield different and superior results.

Contour evolution scheme for variational image segmentation and smoothing

An algorithm, based on the Mumford-Shah (M-S) functional, for image contour segmentation and object smoothing in the presence of noise is proposed. However, in the proposed algorithm, contour length minimisation is not required and it is demonstrated that the M-S functional without contour length minimisation becomes an edge detector. Optimisation of this nonlinear functional is based on the method of calculus of variations, which is implemented by using the level set method. Fourier and Legendre's series are also employed to improve the segmentation performance of the proposed algorithm. The segmentation results clearly demonstrate the effectiveness of the proposed approach for images with low signal-to-noise ratios.