Proposed Energy Function Research Articles

Interacting geometric priors for robust multimodel fitting.

Recent works on multimodel fitting are often formulated as an energy minimization task, where the energy function includes fitting error and regularization terms, such as low-level spatial smoothness and model complexity. In this paper, we introduce a novel energy with high-level geometric priors that consider interactions between geometric models, such that certain preferred model configurations may be induced.We argue that in many applications, such prior geometric properties are available and should be fruitfully exploited. For example, in surface fitting to point clouds, the building walls are usually either orthogonal or parallel to each other. Our proposed energy function is useful in dealing with unknown distributions of data errors and outliers, which are often the factors leading to biased estimation. Furthermore, the energy can be efficiently minimized using the expansion move method. We evaluate the performance on several vision applications using real data sets. Experimental results show that our method outperforms the state-of-the-art methods without significant increase in computation.

Model-based graph-cut method for automatic flower segmentation with spatial constraints

In this paper, we present an accelerated system for segmenting flower images based on graph-cut technique which formulates the segmentation problem as an energy function minimization. The contribution of this paper consists to propose an improvement of the classical used energy function, which is composed of a data-consistent term and a boundary term. For this, we integrate an additional data-consistent term based on the spatial prior and we add gradient information in the boundary term. Then, we propose an automated coarse-to-fine segmentation method composed mainly of two levels: coarse segmentation and fine segmentation. First, the coarse segmentation level is based on minimizing the proposed energy function. Then, the fine segmentation is done by optimizing the energy function through the standard graph-cut technique. Experiments were performed on a subset of Oxford flower database and the obtained results are compared to the reimplemented method of Nilsback et al. [1]. The evaluation shows that our method consumes less CPU time and it has a satisfactory accuracy compared with the mentioned method above [1].

High dynamic range compression and detail enhancement of infrared images in the gradient domain

To find the trade-off between providing an accurate perception of the global scene and improving the visibility of details without excessively distorting radiometric infrared information, a novel gradient-domain-based visualization method for high dynamic range infrared images is proposed in this study. The proposed method adopts an energy function which includes a data constraint term and a gradient constraint term. In the data constraint term, the classical histogram projection method is used to perform the initial dynamic range compression to obtain the desired pixel values and preserve the global contrast. In the gradient constraint term, the moment matching method is adopted to obtain the normalized image; then a gradient gain factor function is designed to adjust the magnitudes of the normalized image gradients and obtain the desired gradient field. Lastly, the low dynamic range image is solved from the proposed energy function. The final image is obtained by linearly mapping the low dynamic range image to the 8-bit display range. The effectiveness and robustness of the proposed method are analyzed using the infrared images obtained from different operating conditions. Compared with other well-established methods, our method shows a significant performance in terms of dynamic range compression, while enhancing the details and avoiding the common artifacts, such as halo, gradient reversal, hazy or saturation.

Dense depth image synthesis via energy minimization for three-dimensional video

Dense depth image is crucial for all three-dimensional (3D) video systems because it is utilized to record the space coordinates of all visible pixels for its corresponding color image. In asymmetric framework of 3D video system, depth image located at non-key-time is usually discarded for the sake of bandwidth saving. In order to construct a complete dynamic 3D scene, the method of dense depth image synthesis is needed. In this paper, we propose a synthesis method through energy minimization for the asymmetric framework of 3D video. Block-based label and metric function is processed in the proposed energy function, and a multi-label graph structure is also proposed to minimize the energy function. In the experiments, the proposed method is utilized in the asymmetric framework for verification. The experimental results show that our method can have better objective and subjective performance on all selected test sequences.

Regularization approach to scene-based nonuniformity correction

Various scene-based nonuniformity correction (SBNUC) methods have been proposed to diminish the residual nonuniformity (RNU) of the infrared focal plane array (IRFPA) sensors. Most existing SBNUC techniques require a relatively large number of image frames to reduce the RNU. In some applications, however, there is not enough time for capturing a large number of image frames prior to the camera operation, or only several image frames are available to users. A new scene-based approach that can correct the RNU using only several image frames is proposed. The proposed method formulates the SBNUC process as an energy minimization problem. In the proposed energy function, we introduce regularization terms for the parameter regarding the responsivity of the IRFPA as well as for the true scene irradiance. Correction results are obtained by minimizing the energy function using a numerical technique. Experimental results demonstrate the effectiveness of the proposed method.

A global energy optimization framework for 2.1D sketch extraction from monocular images

The 2.1D sketch is a layered image representation, which assigns a partial depth ordering of over-segmented regions in a monocular image. This paper presents a global optimization framework for inferring the 2.1D sketch from a monocular image. Our method only uses over-segmented image regions (i.e., superpixels) as input, without any information of objects in the image, since (1) segmenting objects in images is a difficult problem on its own and (2) the objective of our proposed method is to be generic as an initial module useful for downstream high-level vision tasks. This paper formulates the inference of the 2.1D sketch using a global energy optimization framework. The proposed energy function consists of two components: (1) one is defined based on the local partial ordering relations (i.e., figure-ground) between two adjacent over-segmented regions, which captures the marginal information of the global partial depth ordering and (2) the other is defined based on the same depth layer relations among all the over-segmented regions, which groups regions of the same object to account for the over-segmentation issues. A hybrid evolution algorithm is utilized to minimize the global energy function efficiently. In experiments, we evaluated our method on a test data set containing 100 diverse real images from Berkeley segmentation data set (BSDS500) with the annotated ground truth. Experimental results show that our method can infer the 2.1D sketch with high accuracy.

Detail Enhancement for High Dynamic Range Images Rendering Based on Multi-Level Decomposition

Various tone reproduction operators have been proposed to display high dynamic range images on low dynamic range (LDR) devices. Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In these techniques, its important to control the scale of the extracted details and it is often needed to manipulate details in order to avoid the appearance of visual artifacts. In this paper, a new method is proposed to preserve details for high dynamic range images tone reproduction using multi-level image decomposition. We show that current base-detail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Thus, we achieve detail enhancement by applying the bilateral filter iteratively, and this process is called multi-level decomposition. By minimizing the proposed energy function, we can choose the proper decomposition level. Simulation results demonstrate that the proposed method can acquire better visual quality in detail enhancement while make the base smoothness.

IMAGE FUSION BASED ON MULTI-OBJECTIVE OPTIMIZATION

This paper solves the image fusion problem by using a multi-object optimization strategy and a key energy function. The energy function mainly consists of two components. One ensures injection of more correlated detailed spatial information. The detailed information is extracted from gradient representation of the image to be fused. The other one guarantees that the spectral information is preserved by a data fitting term. By minimizing the proposed energy function, the fusion result can be obtained. Moreover, a key parameter is used in the energy function to adjust the weights of the spectral and spatial information during the image fusion. In this paper, a multi-object optimization is constructed to determine such a key parameter. The image fusion performance is evaluated through visional perception and some fusion indexes. Experimental results further demonstrate advantages of the proposed technique over the conventional fusion techniques.

Effective Video Retargeting With Jittery Assessment

This paper presents an effective video retargeting method with the assessment of jittery artifact. Our method firstly constructs a new energy function by including both spatial and temporal constrains. Then the retargeting processing can be performed effectively by minimizing our proposed energy function for each frame. We also propose a new objective measurement to assess temporal coherence after video retargeting, the accuracy of which is verified by psycho-visual tests. Experimental results show that our video retargeting method is able to provide comfortable resized videos in terms of subjective and objective measurements.

Energy Function for Power System With Detailed DC Model: Construction and Analysis

An energy function for AC/DC power system represented by a structure-preserving model is proposed considering detailed DC dynamics. The generators are described by one-axis model with excitation control. The DC line is modeled in details as load dependent on converter bus voltage and DC line current. The effects of mode shift and voltage dependent current limiter (VDCOL) are taken into account. The proposed energy function is theoretically shown to meet the major requirements of being an energy function. A numerical algorithm for constructing the energy function is presented. The monotonic decreasing property of the proposed energy function is numerically demonstrated on two AC/DC systems. It is clarified that the proposed energy function can be viewed as a generalization of the existing energy functions with less assumption.

Bayesian Method with Spatial Constraint for Retinal Vessel Segmentation

A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.

Exemplar-Based Image Inpainting Using Multiscale Graph Cuts

We present a novel formulation of exemplar-based inpainting as a global energy optimization problem, written in terms of the offset map. The proposed energy function combines a data attachment term that ensures the continuity of reconstruction at the boundary of the inpainting domain with a smoothness term that ensures a visually coherent reconstruction inside the hole. This formulation is adapted to obtain a global minimum using the graph cuts algorithm. To reduce the computational complexity, we propose an efficient multiscale graph cuts algorithm. To compensate the loss of information at low resolution levels, we use a feature representation computed at the original image resolution. This permits alleviation of the ambiguity induced by comparing only color information when the image is represented at low resolution levels. Our experiments show how well the proposed algorithm performs compared with other recent algorithms.

Energy Function Based SMES Controller for Transient Stability Enhancement

Superconducting Magnetic Energy Storage (SMES) which is characterized by its highly efficient energy storage, quick response, and power controllability, is expected to contribute to the transient stability of power system. The energy function approach is frequently used as a convenient way to control or analyse the power system. Based on the dynamic characteristic of SMES, the interaction between the SMES and the power system is analysed. According to the additional damping provided by SMES, energy function based control strategy is designed to improve the transient stability of power system. Then, the functional range of the SMES power control is analysed, which can be utilized to optimize the allocation of SMES in power system. Simulation tests are performed to evaluate the performance of the proposed energy function based control method.

Video Compression USING a New Active Mesh Based Motion Compensation Algorithm in Wavelet Sub-Bands

In this paper, a new mesh based algorithm is applied for motion estimation and compensation in the wavelet domain. The first major contribution of this work is the introduction of a new active mesh based method for motion estimation and compensation. The proposed algorithm is based on the mesh energy minimization with novel sets of energy functions. The proposed energy functions have appropriate features, which improve the accuracy of motion estimation and compensation algorithm. We employ the proposed motion estimation algorithm in two different manners for video compression. In the first approach, the proposed algorithm is employed for motion estimation of consecutive frames. In the second approach, the algorithm is applied for motion estimation and compensation in the wavelet sub-bands. The experimental results reveal that the incorporation of active mesh based motion-compensated temporal filtering into wavelet sub-bands significantly improves the distortion performance rate of the video compression. We also use a new wavelet coder for the coding of the 3D volume of coefficients based on the retained energy criteria. This coder gives the maximum retained energy in all sub-bands. The proposed algorithm was tested with some video sequences and the results showed that the use of the proposed active mesh method for motion compensation and its implementation in sub-bands yields significant improvement in PSNR performance.

Active contours with selective local or global segmentation property for multiobject image

In this paper, a novel region-based active contour model is proposed. Unlike the Chan-Vese model, the energy function of our model only uses information outside the evolving curve. A restriction matrix is introduced for restricting the curve to evolve in the desired region. By minimizing the proposed energy function, the corresponding level set function is obtained. The proposed method has four advantages over the traditional region-based level set methods. First, the level set function is no longer required to be initialized as a sign distance function. Second, due to the simplified level set formulation and its fast curve evolution speed, the level set function does not need to be re-initialized and the computational cost is low. Third, our model is applicable to multiobject images since it can segment objects by separating background from an image. Finally, the proposed model has the property of selective local or global segmentation; it can segment not only the desired object but also the other objects in an image. The advantages of our model are demonstrated using synthetic and real images. C 2011 Society of Photo-Optical

Dynamic Channel Assignment for Large-Scale Cellular Networks Using Noisy Chaotic Neural Network

This paper presents a novel dynamic channel assignment (DCA) technique for large-scale cellular networks (LCNs) using noisy chaotic neural network. In this technique, an LCN is first decomposed into many subnets, which are designated as decomposed cellular subnets (DCSs). The DCA process is independently performed in every subnet to alleviate the signaling overheads and to apportion the DCA computational load among the subnets. Then a novel energy function is formulated to avoid causing mutual interference among neighboring subnets based on the real-time interference channel table. In each subnet, the proposed energy function also satisfies three interference constraints among cells and the number of required channels of each cell, and simultaneously minimizes the total number of assigned channels to improve spectrum utilization. A typical 441-cell LCN with 70 available channels, which can be decomposed into nine 49-cell DCSs, is examined to demonstrate the validity of the proposed technique by blocking probability, including uniform and hot spot traffic patterns.

Low complexity content-aware video retargeting for mobile devices

In this paper, a low complexity content-aware video retargeting method suitable for mobile multimedia devices is presented. We propose two types of energy functions for intra- and inter-frames to obtain the pixel significance in the video frames. Since the energy functions are defined using only the compressed-domain information such as discrete cosine transform (DCT) coefficients and motion vectors (MVs), instead of fully decoded pixel domain information, the spatiotemporal energy map can be constructed without excessive computations. Based on the proposed energy function, we also introduce a fast seam carving method for mobile devices that carves out the one-dimensional (1-D) seams without annoying artifacts such as jitter and flicker, taking into account the object motion based on MVs. Experimental results show that the proposed video retargeting scheme can achieve high subjective quality with significantly reduced computational loads as compared with existing methods.

A shortest path routing algorithm using Hopfield neural network with an improved energy function

A shortest path routing algorithm using the Hopfield neural network with a modified Lyapunov function is proposed. The modified version of the Lyapunov energy function for an optimal routing problem is proposed for determining routing order for a source and multiple destinations. The proposed energy function mainly prevents the solution path from having loops and partitions. Experiments are performed on 3000 networks of up to 50 nodes with randomly selected link costs. The performance of the proposed algorithm is compared with several conventional algorithms including Ali and Kamoun's, Park and Choi's, and Ahn and Ramakrishna's algorithms in terms of the route optimality and convergence rate. The results show that the proposed algorithm outperforms conventional methods in all cases of experiments. The proposed algorithm particularly shows significant improvements on the route optimality and convergence rate over conventional algorithms when the size of the network approaches 50 nodes.

An adaptive field rule for non-parametric MRI intensity inhomogeneity estimation algorithm

A widely accepted magnetic resonance imaging (MRI) model states that the observed voxel intensity is a piecewise constant signal intensity function corresponding to the tissue spatial distribution, corrupted with multiplicative and additive noise. The multiplicative noise is assumed to be a smooth bias field, it is called intensity inhomogeneity (IIH) field. Our approach to IIH correction is based on the definition of an energy function that incorporates some smoothness constraints into the conventional classification error function of the IIH corrected image. The IIH field estimation algorithm is a gradient descent of this energy function relative to the IIH field. We call it adaptive field rule (AFR). We comment on the likeness of our approach to the self-organizing map (SOM) learning rule, on the basis of the neighboring function that controls the influence of the neighborhood on each voxel's IIH estimation. We discuss the convergence properties of the algorithm. Experimental results show that AFR compares well with state of the art algorithms. Moreover, the mean signal intensity corresponding to each class of tissue can be estimated from the image data applying the gradient descent of the proposed energy function relative to the intensity class means. We test several variations of this gradient descent approach, which embody diverse assumptions about available a priori information.

Color Texture Segmentation Based on the Modal Energy of Deformable Surfaces

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.