True Object Boundaries Research Articles

Sparse Multi-Bending Snakes.

Active contour models are one of the most emblematic algorithms of computer vision. Their strong theoretical foundations and high user interoperability turned them into a reference approach for object segmentation and tracking tasks. A high number of modifications have already been proposed in order to overcome the known problems of traditional snakes, such as initialization dependence and poor convergence to concavities. In this paper, we address the scenario where the user wants to segment an object that has multiple dynamic regions but some of them do not correspond to the true object boundary. We propose a novel parametric active contour model, the Sparse Multi-Bending snake, which is capable of dividing the contour into a set of contiguous regions with different bending properties. We derive a new energy function that induces such behavior and presents a group optimization strategy that can be used to find the optimal bending resistance parameter for each point of the contour. We show the flexibility of our model in a set of synthetic images. In addition, we consider two real applications, lung segmentation in Computerized Tomography data and hand segmentation in depth images. We show how the proposed method is able to improve the segmentations obtained in both applications, when compared with other active contour models.

A fractional order derivative based active contour model for inhomogeneous image segmentation

• A new hybrid framework of adaptive-weighting active contour model is proposed. • The global term enhances the image contrast and accelerates the convergence rate. • The local term integrates fractional order differentiation and difference image information. • An adaptive weighting strategy and a termination criterion are employed. • Measures include the dice similarity coefficient and gray-leveled contrast. Segmenting intensity inhomogeneous images is a challenging task for both local and global methods. Some hybrid methods have great advantages over the traditional methods in inhomogeneous image segmentation. In this paper, a new hybrid method is presented, which incorporates image gradient, local environment and global information into a framework, called adaptive-weighting active contour model. The energy or level set functions in the framework mainly include two parts: a global term and local term. The global term aims to enhance the image contrast, and it can also accelerate the convergence rate when minimizing the energy function. The local term integrates fractional order differentiation, fractional order gradient magnitude, and difference image information into the well-known local Chan–Vese model, which has been shown to be effective and efficient in modeling the local information. The local term can also enhance low frequency information and improve the inhomogeneous image segmentation. An adaptive weighting strategy is proposed to balance the actions of the global and local terms automatically. When minimizing the level set functions, regularization can be imposed by applying Gaussian filtering to ensure smoothness in the evolution process. In addition, a corresponding stopping criterion is proposed to ensure the evolving curve automatically stops on true boundaries of objects. Dice similarity coefficient is employed as the comparative quantitative measures for the segmented results. Experiments on synthetic images as well as real images are performed to demonstrate the segmentation accuracy and computational efficiency of the presented hybrid method.

A new combination active contour model for segmenting texture image with low contrast and high illumination variations

This paper presents a novel combined energy functional based on edge and region information for active contour model, which can be applied to segment textured images containing low contrast and high illumination variations. In the proposed method, the edge features are calculated based on the phase-based approaches derived from the monogenic signal, which are robust to illumination variations in the image. These feature values are used in an edge energy functional to assist the active contour evolving towards the true object boundaries. To extract the region features, at first, we compute the normalized accumulated short-term autocorrelation (NASTA) values for the image, which suppress background clutter and enhance dissimilarities between objects and background. Next, a local cumulative distribution function (LCDF) of NASTA is calculated for every pixel in a local window around it and is used as a region feature for that pixel. Then, the obtained features are employed to define a new localized region-based energy functional that can correctly segment regions with same intensity mean and variance. The proposed edge and region energy terms are integrated and a regularization term is added to them to form our combined active contour. Experimental results indicate remarkable advantages of proposed method comparing to existing combination models.

A Novel Fuzzy Level Set Approach for Image Contour Detection

The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

Interactive Image Segmentation Based on Level Sets of Probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method.

Automatic Object Extraction Over Multiscale Edge Field for Multimedia Retrieval

In this work, we focus on automatic extraction of object boundaries from Canny edge field for the purpose of content-based indexing and retrieval over image and video databases. A multiscale approach is adopted where each successive scale provides further simplification of the image by removing more details, such as texture and noise, while keeping major edges. At each stage of the simplification, edges are extracted from the image and gathered in a scale-map, over which a perceptual subsegment analysis is performed in order to extract true object boundaries. The analysis is mainly motivated by Gestalt laws and our experimental results suggest a promising performance for main objects extraction, even for images with crowded textural edges and objects with color, texture, and illumination variations. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed object extraction method and subsequent shape description in the context of multimedia indexing and retrieval. A promising retrieval performance is achieved, and especially in some particular examples, the experimental results show that the proposed method presents such a retrieval performance that cannot be achieved by using other features such as color or texture.

Lazy snapping

In this paper, we present Lazy Snapping , an interactive image cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy . Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel image segmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.

Longitudinal emission computed tomography: A critical analysis

Longitudinal emission computed tomography (LECT), is shown to generate images with a reconstruction error independent of both the reconstruction algorithm and the physical performance of the viewing system. In general, activity will be predicted to extend into space beyond the true object boundary and the predicted, relative distribution of activity will be incorrect. A method is described which identifies the direction involving the maximum reconstruction error and gives a measure of the maximum error volume; its application in the optimisation of some common LECT systems is demonstrated.