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Fusion image is obtained by processing information from various sources of images. Raw images are attained from different source or same source images. In the medical field, images are collected from several modalities like CT, MRI, etc., and merged into a single fused image. This fused image has significant features and information which can be used to diagnose easily. The fundamental or major task for medical image fusion is image registration. The image registration is done as a raw or distorted image. Distorted image registration methods are overviewed and empowering advancements in this field are done through this paper. The techniques and components for registering images are identified and given in an efficient way. The main impact of this paper is to present various techniques used for image registration in a systematic approach. [ABSTRACT FROM AUTHOR]

Active contours as variational techniques have been widely applied in image processing and computer vision. In this paper, a shape based active contour is proposed for segmenting circular object from the cluttered background. Being different from the general shape based active contours that estimate the shape parameters by gradient descent method, the proposed shape based active contour directly estimates the parameters of the circular shape without any iteration, which overcomes the difficulty in the parameter initialisation. Therefore, the estimation of the circular shape parameter is robust. The experimental results show the good performance of the proposed shape based active contour on circle detection. [ABSTRACT FROM AUTHOR]

This paper presents an image segmentation method named Context-based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image Segmentation (CHUMSIS), which uses superpixels as the operation units instead of pixels. Based on the Gestalt laws, three rules that realize a new and natural way to manage different kinds of features extracted from SAR images are proposed to represent superpixel context. The rules are prior knowledge from cognitive science and serve as top-down constraints to globally guide the superpixel merging. The features, including brightness, texture, edges, and spatial information, locally describe the superpixels of SAR images and are bottom-up forces. While merging superpixels, a hierarchical unequal merging algorithm is designed, which includes two stages: 1) coarse merging stage and 2) fine merging stage. The merging algorithm unequally allocates computation resources so as to spend less running time in the superpixels without ambiguity and more running time in the superpixels with ambiguity. Experiments on synthetic and real SAR images indicate that this algorithm can make a balance between computation speed and segmentation accuracy. Compared with two state-of-the-art Markov random field models, CHUMSIS can obtain good segmentation results and successfully reduce running time. [ABSTRACT FROM PUBLISHER]

In this paper, we present two related multigrid algorithms for multiphase image segmentation. Algorithm I solves the model by Vese-Chan. We first generalize our recently developed multigrid method to this multiphase segmentation model (MG1); we also give a local Fourier analysis for the local smoother which leads to a new and more effective smoother. Although MG1 is found many magnitudes faster than the fast method of additive operator splitting (AOS), both algorithms are not robust with regard to the initial guess. To overcome this dependence on the initial guess, we consider a hierarchical segmentation model which achieves multiphase segmentation by repeated use of the Chan-Vese two-phase model; our Algorithm II solves this model by a multigrid algorithm (MG2). Numerical experiments show that both algorithms are efficient and in particular MG2 is more robust than MG1 with respect to initial guesses. AMS subject classifications: 68U10, 65F10, 65K10. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data. This formulation allows direct incorporation of prior information concerning the target boundary, target ranges, and background ranges into an optimal reconstruction process. Curve evolution techniques and a generalized expectation-maximization algorithm are jointly employed as an efficient solver for minimizing the objective energy, resulting in a coupled pair of object and intensity optimization tasks. The method directly and optimally extracts the target boundary, avoiding a suboptimal two-step process involving image smoothing followed by boundary extraction. Experiments are presented demonstrating that the proposed approach is robust to anomalous pixels (missing data) and capable of producing accurate estimation of the target boundary and range values from noisy data. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of image segmentation by means of active contours, whose evolution is driven by the gradient flow derived from an energy functional that is based on the Bhattacharyya distance. In particular, given the values of a photo- metric variable (or of a set thereof), which is to be used for classifying the image pixels, the active contours are designed to converge to the shape that results in maximal discrepancy between the empirical distributions of the photometric variable inside and outside of the contours. The above discrepancy is measured by means of the Bhattacharyya distance that proves to be an extremely useful tool for solving the problem at hand. The proposed methodology can be viewed as a generalization of the segmentation methods, in which active contours maximize the difference between a finite number of empirical moments of the ‘inside’ and ‘outside’ distributions. Furthermore, it is shown that the proposed methodology is very versatile and flexible in the sense that it allows one to easily accommodate a diversity of the image features based on which the segmentation should be performed. As an additional contribution, a method for automatically adjusting the smoothness properties of the empirical distributions is proposed. Such a procedure is crucial in situations when the number of data samples (supporting a certain segmentation class) varies considerably in the course of the evolution of the active contour. In this case, the smoothness properties of the empirical distributions have to be properly adjusted to avoid either over-or underestimation artifacts. Finally, a number of relevant segmentation results are demonstrated and some further research directions are discussed. [ABSTRACT FROM AUTHOR]

Although simple and efficient, traditional feature-based texture segmentation methods usually suffer from the intrinsical less inaccuracy, which is mainly caused by the oversimplified assumption that each textured subimage used to estimate a feature is homogeneous. To solve this problem, an adaptive segmentation algorithm based on the coupled Markov random field (CMRF) model is proposed in this paper. The CMRF model has two mutually dependent components: one models the observed image to estimate features, and the other models the labeling to achieve segmentation. When calculating the feature of each pixel, the homogeneity of the subimage is ensured by using only the pixels currently labeled as the same pattern. With the acquired features, the labeling is obtained through solving a maximum a posteriori problem. In our adaptive approach, the feature set and the labeling are mutually dependent on each other, and therefore are alternately optimized by using a simulated annealing scheme. With the gradual improvement of features' accuracy, the labeling is able to locate the exact boundary of each texture pattern adaptively. The proposed algorithm is compared with a simple MRF model based method in segmentation of Brodatz texture mosaics and real scene images. The satisfying experimental demonstrate that the proposed approach can differentiate textured images more accurately. [ABSTRACT FROM AUTHOR]

Abstract: Perceptual approaches have been widely used in many areas of visual information processing. This paper presents an overview of perceptual based approaches for image enhancement, segmentation and coding. The paper also provides a brief review of image quality assessment (IQA) methods, which are used to evaluate the performance of visual information processing techniques. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on few topics that have been extensively researched and developed over the past few decades. The goal is to present a perspective as broad as possible on this actively evolving domain due to relevant advances in vision research and signal processing. Therefore, for each topic, we identify the main contributions of perceptual approaches and their limitations, and outline how perceptual vision has influenced current state-of-the-art techniques in image enhancement, segmentation, coding and visual information quality assessment. [Copyright &y& Elsevier]

The development of 3-D ultrasonic probes and 3-D ultrasound (3DUS) imaging offers new functionalities that call for specific image processing developments. In this paper, we propose an original method for the segmentation of the utero-fetal unit (UFU) from 3DUS volumes, acquired during the first trimester of gestation. UFU segmentation is required for a number of tasks, such as precise organ delineation, 3-D modeling, quantitative measurements, and evaluation of the clinical impact of 3-D imaging. The segmentation problem is formulated as the optimization of a partition of the image into two classes of tissues: the amniotic fluid and the fetal tissues. A Bayesian formulation of the partition problem integrates statistical models of the intensity distributions in each tissue class and regularity constraints on the contours. An energy functional is minimized using a level set implementation of a deformable model to identify the optimal partition. We propose to combine Rayleigh, Normal, Exponential, and Gamma distribution models to compute the region homogeneity constraints. We tested the segmentation method on a database of 19 antenatal 3DUS images. Promising results were obtained, showing the flexibility of the level set formulation and the interest of learning the most appropriate statistical models according to the idiosyncrasies of the data and the tissues. The segmentation method was shown to be robust to different types of initialization and to provide accurate results, with an average overlap measure of 0.89 when comparing with manual segmentations. [ABSTRACT FROM PUBLISHER]

This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is completely free of the costly reinitialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then solve the diffusion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly reinitialization procedure is completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variational level set method and partial differential equation-based level set method. The RD-LSE method shows very good performance on boundary antileakage. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach. [ABSTRACT FROM PUBLISHER]

A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e., motion field). To this end, we develop a framework based on the appearance and motion modes. For the appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for the motion mode, we use the optical flow and the local temporal texture descriptor to represent the temporal variations of the DT. In addition, for the optical flow, we use the histogram of oriented optical flow (HOOF) to organize them. To compute the distance between two HOOFs, we develop a simple effective and efficient distance measure based on Weber's law. Furthermore, we also address the problem of threshold selection by proposing a method for determining thresholds for the segmentation method by an offline supervised statistical learning. The experimental results show that our method provides very good segmentation results compared to the state-of-the-art methods in segmenting regions that differ in their dynamics. [ABSTRACT FROM PUBLISHER]

When the object is similar to the background, it is difficult to segment the completed human body object from video images. To solve the problem, this paper proposes an object segmentation algorithm based on guided layering from video images. This algorithm adopts the structure of advance by degrees, including three parts altogether. Each part constructs the different energy function in terms of the spatiotemporal information to maximize the posterior probability of segmentation label. In part one, the energy functions are established, respectively, with the frame difference information in the first layer and second layer. By optimization, the initial segmentation is solved in the first layer, and then the amended segmentation is obtained in the second layer. In part two, the energy function is built in the interframe with the shape feature as the prior guiding to eliminate the interframe difference of the segmentation result. In art three, the segmentation results in the previous two parts are fused to suppress or inhibit the over-repairing segmentation and the object shape variations in the adjacent two-frame. The results from the compared experiment indicate that this algorithm can obtain the completed human body object in the case of the video image with similarity between object and background. [ABSTRACT FROM AUTHOR]

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the stateof- the-art fire detection method. [ABSTRACT FROM AUTHOR]

Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost. [ABSTRACT FROM AUTHOR]

The use of high-performance computers in computer simulations of physical phenomena has resulted in the generation of massive amounts of data. The analysis of these data can be challenging given their size and the complexity of the structures observed. In this paper, we describe how we can use image processing techniques to identify and count coherent structures in three-dimensional simulations of the Rayleigh-Taylor instability, which occurs when one fluid is being accelerated by a second fluid. As the two fluids mix, coherent structures are formed which grow, shrink, change shape, split, and merge with time. We describe the challenges faced in the analysis and show how we can combine image processing techniques with problem-specific characteristics to successfully analyze terabyte-sized data sets. [ABSTRACT FROM AUTHOR]

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Image segmentation is to divide the image into disjoint homogenous regions or classes. In this paper, a new approach is proposed to the RG B (Red Green Blue) images by determining edges and seed regions by using two-dimension wavelet transform The region growing algorithm applied to extract regions and then coloring the segmented regions with a specific color. Gaussian filter used in enhancement as a preprocessing step to merge similar pixels with their neighbors. The two-dimensional Symlet family used wavelet transform to extract edges of the red, green and blue bands. The morphological algorithms applied to remove noisy pixels from image edges. The edges are connected together to extract the seed regions and these seed regions are the region growing algorithm to grow the segmented regions. The proposed algorithm applied on the three bands of the RGB images, and each band gave different segments, which depends on the information carried in the processed band. The algorithm tested on images from different sources and gave good results. [ABSTRACT FROM AUTHOR]

Image-based rendering has been successfully used to display 3-D objects for many applications. A well-known example is the object movie, which is an image-based 3-D object composed of a collection of 2-D images taken from many different viewpoints of a 3-D object. In order to integrate image-based 3-D objects into a chosen scene (e.g., a panorama), one has to meet a hard challenge—to efficiently and effectively remove the background from the foreground object. This problem is referred to as multiview images (MVIs) segmentation. Another task requires MVI segmentation is image-based 3-D reconstruction using multiview images. In this paper, we propose a new method for segmenting MVI, which integrates some useful algorithms, including the well-known graph-cut image segmentation and volumetric graph-cut. The main idea is to incorporate the shape prior into the image segmentation process. The shape prior introduced into every image of the MVI is extracted from the 3-D model reconstructed by using the volumetric graph cuts algorithm. Here, the constraint obtained from the discrete medial axis is adopted to improve the reconstruction algorithm. The proposed MVI segmentation process requires only a small amount of user intervention, which is to select a subset of acceptable segmentations of the MVI after the initial segmentation process. According to our experiments, the proposed method can provide not only good MVI segmentation, but also provide acceptable 3-D reconstructed models for certain less-demanding applications. [ABSTRACT FROM AUTHOR]

The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Shah functional. The core of our model is an energy functional composed by three complementary terms. The first one is based on a shape model which constrains the active contour to get a shape of interest. The second term detects object boundaries from image gradients. And the third term drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical images. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a rose analysis and recognition system and presents the main principles used to realize the recognition system. The major principles presented in this paper are the mathematical description methods for rose features such as shape, size and color of the flower, petal, leaf, etc, and the object-oriented pattern recognition (OOPR) approach which mathematically deals with how to comprehensively use all different rose features rationally in the recognition scheme. The recognition system is described and some of its experimental results are given which demonstrate the efficiency of our methods. [Copyright &y& Elsevier]

Herbal leaves are used widely in local medication. But now a day, an ordinary person has little knowledge about those herbs and he may not identify such herbals easily. As a first step of computer based recognition of herbs, an analysis has been made to identify the best method for segmenting leaves object from its background. This type of segmentation is a preprocessing step required in identification of species of leaves or plants. Several methods are available for detecting the objects based on global and local features of an image. In this paper we are examining various object detection techniques for segmenting leaves based on color, shape and texture. Features like local adaptive mean color, evidence based color model, color histogram techniques are used. Boundary structure model is used to detect the leaves based on boundary descriptors of an image and Chan-Vese algorithm is used to segment the leaves from complex background. To extract leaves from texture background, edge focusing algorithm is used. From our experimentation analysis, shape is the powerful characteristics of segmenting leaf images and Chan-Vese algorithm provides better results compared to other techniques without affecting the leaf colors, texture etc. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a region-based model for the object and background extraction with application to the image with thick or complex boundary. Based on region information of the image, we employ two curves to extract the object and background, respectively, regardless of the boundary. The first curve is used to extract the object. Correspondingly, the second curve is used to extract the background. By employing two level set functions to represent the two curves, we propose a new region-based energy functional. In the proposed model, a distance constraint term is incorporated, which effectively avoid that the two level set functions too away from each other and keep their similar shapes well. Besides, we present a penalty term to maintain the accurate computation and stability evolution. Experiment results demonstrate the desirable performance of the proposed model with application to synthetic and real-world images. [Copyright &y& Elsevier]

Image segmentation divides an image into regions or categories, which correspond to different objects or parts of objects presented into image. In image processing, edges represent and mark boundaries and, moreover, edge detection allows the identification and localization of sharp discontinuities in an image. As matter of fact, the intensity inhomogeneity is the main drawback of segmentation an image into homogeneous objects and various edge detection operators are available, dedicated and sensitive to certain types of edges. This paper presents a study of image edge detection technique and investigates the performance of Canny, Sobel and Prewitt operators. [ABSTRACT FROM AUTHOR]

Abstract: Inspired by Krige'' variogram and the multi-channel filtering theory for human vision information processing, this paper proposes a novel algorithm for segmenting the textures based on experimental semi-variogram function (ESVF), which can simultaneously describe structural property and statistical property of textures. The single variogram function value (SVFV) and the variance distance obtained by ESVF are used as texture feature description for segmenting textures. The feasibility and effectiveness of the proposed method are demonstrated by testing on some texture images. The computational complexity of the proposed approach depends neither on the number of the textures nor on the number of the gray levels, and only on the size of the image blocks. We have proved theoretically that the algorithm has the advantages of direction invariability and a higher sensitivity to different textures and can detect almost all kinds of the boundaries of the shape textures. Experimental results on the Brodatz texture databases show that the performance of this algorithm is superior to the traditional techniques such as texture spectrum, SIFT, k-mean method, and Gabor filters. The proposed approach is found to be robust, efficient, and satisfactory. [Copyright &y& Elsevier]

Artists use different means of stylization to control the focus on different objects in the scene. This allows them to portray complex meaning and achieve certain artistic effects. Most prior work on painterly rendering of videos, however, uses only a single painting style, with fixed global parameters, irrespective of objects and their layout in the images. This often leads to inadequate artistic control. Moreover, brush stroke orientation is typically assumed to follow an everywhere continuous directional field. In this paper, we propose a video painting system that accounts for the spatial support of objects in the images or videos, and uses this information to specify style parameters and stroke orientation for painterly rendering. Since objects occupy distinct image locations and move relatively smoothly from one video frame to another, our object-based painterly rendering approach is characterized by style parameters that coherently vary in space and time. Space-time-varying style parameters enable more artistic freedom, such as emphasis/de-emphasis, increase or decrease of contrast, exaggeration or abstraction of different objects in the scene in a temporally coherent fashion. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a general object boundary extraction model for piecewise smooth images, which incorporates local intensity distribution information into an edge-based implicit active contour. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different region-based operators: a Gaussian mixture model (GMM)-based intensity distribution estimator and the Hueckel operator. We propose the local distribution fitting model for more accurate segmentation, which incorporates the operator outcomes into the recent local binary fitting (LBF) model. The GMM and the Hueckel model parameters are estimated before contour evolution, which enables the use of the proposed model without the need for initial contour selection, i.e., the level set function is initialized with a random constant instead of a distance map. Thus our model essentially alleviates the initialization sensitivity problem of most active contours. Experiments on synthetic and real images show the improved performance of our approach over the LBF model. [Copyright &y& Elsevier]

Abstract: Within the last decade increasing computing power and the scientific advancement of algorithms allowed the analysis of various aspects of human faces such as facial expression estimation , head pose estimation , person identification or face model fitting . Today, computer scientists can use a bunch of different techniques to approach this challenge . However, each of them still has to deal with non-perfect accuracy or high execution times. This is mainly because the extraction of descriptive features is challenging in real-world scenarios to any image understanding application. In this paper, we consider the extraction of more descriptive information from the image for face analysis tasks. Our approach automatically determines a set of characteristics that describe the conditions of the entire image. They are based on semantic information that describes the location of facial components, such as skin, lips, eyes, and brows. From these image characteristics, pixel features are determined that are highly tuned to the task of interpreting images of human faces. The extracted features are applied to train pixel-based classifiers, which is the straight-forward approach because this task suffers from high intra-class and small inter-class color variations due to changing context conditions such as the person’s ethnic group or lighting condition. In contrast, more elaborate classifiers that additionally consider shape or region features are not real-time capable. The success of this approach relies on the fact that we do not manually select the calculation rules but we provide a multitude of features of various kinds, both color-related and space-related. A Machine Learning algorithm then decides which of them are important and which are not rendering the approach fast due to its pixel-based nature and accurate due to the highly descriptive features the same time. [Copyright &y& Elsevier]

Abstract: A new region-based active contour model that embeds the image local information is proposed in this paper. By introducing the local image fitting (LIF) energy to extract the local image information, our model is able to segment images with intensity inhomogeneities. Moreover, a novel method based on Gaussian filtering for variational level set is proposed to regularize the level set function. It can not only ensure the smoothness of the level set function, but also eliminate the requirement of re-initialization, which is very computationally expensive. Experiments show that the proposed method achieves similar results to the LBF (local binary fitting) energy model but it is much more computationally efficient. In addition, our approach maintains the sub-pixel accuracy and boundary regularization properties. [Copyright &y& Elsevier]

In this paper, a modified algorithm for object segmentation of binary images is presented, and denoted as 2D modified chain code algorithm. The 2D modified chain code algorithm can be applied to color images after being binarized. The segmented object is used to derive the chain code in the image. The definition of the 2D-modified chain code algorithm is valid for shapes composed of triangular, rectangular, and hexagonal cells. The 2D modified chain code preserves information and allows computing geometric dimension. The results demonstrate that the 2D modified chain code algorithm could extract the coordinates of the shapes at lower computational cost when compared to the classical chain code. Here, a considerable improvement in accuracy (20. 1-57.2%) over what is possible with the classical chain code has been achieved at the expense of slight increase in computational cost (10-20%). [ABSTRACT FROM AUTHOR]

Active contours, or so-called snakes, require some parameters to determine the form of the external force or to adjust the tradeoff between the internal forces and the external forces acting on the active contour. However, the optimal values of these parameters cannot be easily identified in a general sense. The usual way to find these required parameters is to run the algorithm several times for a different set of parameters, until a satisfactory performance is obtained. Our nonparametric formulation translates the problem of seeking these unknown parameters into the problem of seeking a good edge probability density estimate. Density estimation is a well-researched field, and our nonparametric formulation allows using well-known concepts of density estimation to get rid of the exhaustive parameter search. Indeed, with the use of kernel density estimation these parameters can be defined locally, whereas, in the original snake approach, all the shape parameters are defined globally. We tested the proposed method on synthetic and real images and obtained comparatively better results. [ABSTRACT FROM AUTHOR]

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM. [ABSTRACT FROM AUTHOR]

This paper presents an active contour model for fast object segmentation called FACE. A novel energy term that takes into account the computational complexity of the active contour is introduced together with related constraints and minimization procedure. The described process is based on the regularization and optimization of the active contour control points position. The trade-off between computational complexity and final contour accuracy is based on curvature estimation. The result is a fast active contour convergence towards desired object boundaries. This method can be combined with most of the other active contour approaches presented in literature, thanks to the independence between the computational minimization process and the classical active contour minimization process. The object segmentation procedure can be automatic or semiautomatic depending on the original image complexity. Several tests and experiments have been realized. Results show improvements in terms of computational time reduction when compared with other similar active contour models. [Copyright &y& Elsevier]

This paper proposes a segmentation method based on region information and edge information. The method consists of three steps: (1) a connected component which is a pan of an object is extracted as a seed by using a simple process such as a histogram analysis; (2) this seed is expanded repeatedly, and (3) to evaluate the possibility of the expansion, the uniformity of gray levels (region information) and checking me presence of border pixels (edge information) are used. Although the method can be considered as a kind of region expansion, the significant features are that it contains the growth of a seed and the edge information (a part of control information) to expand the region repeatedly. The former gives preliminary information on approximate gray levels of an object and extracts the object as a connected component, and the Inner makes the edge information more useful. Although the usefulness of the edge information in a conventional region-and-edge integrated method such as the superslice algorithm depends greatly on its region segmentation, the use of such methods can be avoided in the proposed method, since both the region and edge information are used simultaneously for the control conditions of the region expansion. The method is justified experimentally by using synthetic images and actual images in two and three dimensions. [ABSTRACT FROM AUTHOR]

We introduce a fast and efficient variational framework for Simultaneous Registration and Segmentation (SRS) applicable to a wide variety of image sequences. We demonstrate that a dense correspondence map (between consecutive frames) can be reconstructed correctly even in the presence of partial occlusion, shading, and reflections. The errors are efficiently handled by exploiting their sparse nature. In addition, the segmentation functional is reformulated using a dual Rudin-Osher-Fatemi (ROF) model for fast implementation. Moreover, nonparametric shape prior terms that are suited for this dual-ROF model are proposed. The efficacy of the proposed method is validated with extensive experiments on both indoor, outdoor natural and biological image sequences, demonstrating the higher accuracy and efficiency compared to various state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As depth cameras become more popular, pixel depth information becomes easier to obtain. This information can clearly enhance many image processing applications. However, combining depth and color information is not straightforward as these two signals can have different noise characteristics, differences in resolution, and their boundaries do not generally agree. We present a technique that combines depth and color image information from real devices in synergy. In particular, we focus on combining them to improve image segmentation. We use color information to fill and clean depth and use depth to enhance color image segmentation. We demonstrate the utility of the combined segmentation for extracting layers and present a novel image retargeting algorithm for layered images. [ABSTRACT FROM AUTHOR]

We propose an approach to automatically extracting foreground regions. This is a novel method for segmenting salient objects from still images by background elimination. To extract foreground regions, a new method of background elimination based on multiscale segmentation is proposed to detect candidate object regions. To this end, we use a trimap consisting of foreground, background, and undefined regions and a region adjacency graph. A graph-cut technique is finally used to extract exact foreground regions from the candidates. Experimental results have shown that the proposed method yields a better foreground extraction than Kim's method under various environments containing multiple objects and clutter backgrounds in natural images. [ABSTRACT FROM AUTHOR]

This article introduces a novel class of active contour models for image segmentation. It makes use of nonlocal comparisons between pairs of patches within each region to be segmented. The corresponding variational segmentation problem is implemented using a level set formulation that can handle an arbitrary number of regions. The pairwise interaction of features constrains only the local homogeneity of image features, which is crucial in capturing regions with smoothly spatially varying features. This segmentation method is generic and can be adapted to various segmentation problems by designing an appropriate metric between patches. We instantiate this framework using several classes of features and metrics. Piecewise smooth grayscale and color images are handled using L² distance between image patches. We show examples of efficient segmentation of natural color images. Locally oriented textures are segmented using the L² distance between patches of Gabor coefficients. We use a Wasserstein distance between local empirical distributions for locally homogeneous random textures. A correlation metric between local motion signatures is able to segment piecewise smooth optical flows. [ABSTRACT FROM AUTHOR]

Optic Disc (OD) localization is an important pre-processing step that significantly simplifies subsequent segmentation of the OD and other retinal structures. Current OD localization techniques suffer from impractically-high computation times (few minutes per image). In this work, we present a fast technique that requires less than a second to localize the OD. The technique is based upon obtaining two projections of certain image features that encode the x- and y- coordinates of the OD. The resulting 1-D projections are then searched to determine the location of the OD. This avoids searching the 2-D image space and, thus, enhances the speed of the OD localization process. Image features such as retinal vessels orientation and the OD brightness are used in the current method. Four publicly-available databases, including STARE and DRIVE, are used to evaluate the proposed technique. The OD was successfully located in 330 images out of 340 images (97%) with an average computation time of 0.65 s. [ABSTRACT FROM AUTHOR]

Shape models (SMs), capturing the common features of a set of training shapes, represent a new incoming object based on its projection onto the corresponding model. Given a set of learned SMs representing different objects classes, and an image with a new shape, this work introduces a joint classification-segmentation framework with a twofold goal. First, to automatically select the SM that best represents the object, and second, to accurately segment the image taking into account both the image information and the features and variations learned from the online selected model. A new energy functional is introduced that simultaneously accomplishes both goals. Model selection is performed based on a shape similarity measure, online determining which model to use at each iteration of the steepest descent minimization, allowing for model switching and adaptation to the data. High-order SMs are used in order to deal with very similar object classes and natural variability within them. Position and transformation invariance is included as part of the modeling as well. The presentation of the framework is complemented with examples for the difficult task of simultaneously classifying and segmenting closely related shapes, such as stages of human activities, in images with severe occlusions. [ABSTRACT FROM AUTHOR]

We introduce a novel method for storing and retrieving binary geometrical shapes in quantum mechanical systems. In contrast to standard procedures in classical computer science in which image reconstruction requires not only the storage of light parameters (like light frequency) but also the storage and use of additional information like correlation and pixel spatial disposition, we show that the employment of maximally entangled qubits allows to reconstruct images without using any additional information. Moreover, we provide a concrete application of our proposal in the field of image recognition and briefly explore potential experimental realizations. Our proposal could be employed to enable emergent quantum technology to be used in high-impact scientific disciplines in which extensive use of image processing is made. [ABSTRACT FROM AUTHOR]

Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate. [ABSTRACT FROM AUTHOR]

Pyramid image representations via tree structures are recognized methods for region-based image analysis. Binary partition trees can be applied which document the merging process with small details found at the bottom levels and larger ones close to the root. Hindsight of the merging process is stored within the tree structure and provides the change histories of an image, property from the leaf to the root node. In this work, the change histories are modelled by evolvement functions and their second order statistics are analyzed by using a knee function. Knee values show the reluctancy of each merge. We have systematically formulated these findings to provide a novel framework for binary partition tree analysis, where tree simplification is demonstrated. Based on an evolvement function, for each upward path in a tree, the tree node associated with the first reluctant merge is considered as a pruning candidate. The result is a simplified version providing a reduced solution space and still complying with the definition of a binary tree. The experiments show that image details are preserved whilst the number of nodes is dramatically reduced. An image filtering tool also results which preserves object boundaries and has applications for segmentation. [ABSTRACT FROM AUTHOR]

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications. [ABSTRACT FROM AUTHOR]