This letter proposes a novel design approach for optimal contourlet filter banks based on the parametric 9/7 filter family. The Laplacian pyramid decomposition is replaced by optimal 9/7 filter banks with rational coefficients, and directional filter banks are activated using a pkva 12 filter in the contourlets. Moreover, based on this optimal 9/7 filter, we present an image denoising approach using a contourlet domain hidden Markov tree model. Finally, experimental results show that our approach in denoising images with texture detail is only 0.20 dB less compared to the method of Po and Do, and the visual quality is as good as for their method. Compared with the method of Po and Do, our approach has lower computational complexity and is more suitable for VLSI hardware implementation.

A new method of segmentation for Synthetic Aperture Radar (SAR) images using the skewness wavelet energy has been presented. The skewness is the third order cumulant which measures the local texture along the region-based active contour. Nonlinearity in intensity inhomogeneities often occur in SAR images due to the speckle noise. In this paper we propose a region-based active contour model that is able to use the intensity information in local regions and to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use a wavelet coefficients energy distribution to analyze the SAR image texture in each sub-band. A fitting energy called skewness wavelet energy is defined in terms of a contour and a functional so that, the regions and their interfaces will be modeled by level set functions. A functional relationship has been calculated on these level sets in terms of the third order cumulant, from which an energy minimization is derived. Minimizing the calculated functions derives the optimal segmentation based on the texture definitions. The results of the implemented algorithm on the test images from the Radarsat SAR images of agricultural and urban regions show a desirable performance of the proposed method.

In this paper, we propose a novel active contour method for image segmentation using a local regional information on extendable search line. We call it the LRES active contour. Our active contour uses the intensity values along a set of search lines that are perpendicular to the contour front. These search lines are used to inform the contour front toward which direction to move in order to find the object's boundary. Unlike other methods, none of these search lines have a predetermined length. Instead, their length increases gradually until a boundary of the object is found. We compare the performance of our LRES active contour to other existing active contours, both edge-based and region-based. The results show that our method provides more desirable segmentation outcomes, particularly on some images where other methods may fail. Not only is our method robust to noise and able to reach into a deep concave shape, it also has a large capture range and performs well in segmenting heterogeneous textured objects.

The key problem of object-based attention is the definition of objects, while contour grouping methods aim at detecting the complete boundaries of objects in images. In this paper, we develop a new contour grouping method which shows several characteristics. First, it is guided by the global saliency information. By detecting multiple boundaries in a hierarchical way, we actually construct an object-based attention model. Second, it is optimized by the grouping cost, which is decided both by Gestalt cues of directed tangents and by region saliency. Third, it gives a new definition of Gestalt cues for tangents which includes image information as well as tangent information. In this way, we can improve the robustness of our model against noise. Experiment results are shown in this paper, with a comparison against other grouping model and space-based attention model.

This paper proposes a contourlet based adaptive watermarking for color images (CAWCI). A color image with RGB space is firstly converted to its YCbCr space equivalent; a luminance (Y) image and two chrominance (Cb and Cr) images are subsequently transformed into contourlet domain respectively; the watermark is embedded into the contourlet coefficients of the largest detail subbands of three images lastly. On the one hand, the embedded watermark is imperceptible because contrast sensitivity function and watermark visual mask are adopted in our CAWCI. On the other hand, the embedded watermark is very robust due to the spread specialty of Laplacian pyramid (LP) in contourlet transform. The corresponding watermarking detection algorithm is proposed to decide whether the watermark is present or not by exploiting the unique transform structure of LP. Experimental results show the validity of CAWCI in terms of both watermarking invisibility and watermarking robustness.

Boundary detection is one of the most studied problems in computer vision. It is the foundation of contour grouping, and initially affects the performance of grouping algorithms. In this paper we propose a novel boundary detection algorithm for contour grouping, which is a selective attention guided coarse-to-fine scale pyramid model. Our algorithm evaluates each edge instead of each pixel location, which is different from others and suitable for contour grouping. Selective attention focuses on the whole saliency objects instead of local details, and gives global spatial prior for boundary existence of objects. The evolving process of edges through the coarsest scale to the finest scale reflects the importance and energy of edges. The combination of these two cues produces the most saliency boundaries. We show applications for boundary detection on natural images. We also test our approach on the Berkeley dataset and use it for contour grouping. The results obtained are pretty good.

In this paper, a novel design method of critically sampled contourlet transform (CSCT) is proposed. The original CT which consists of Laplacian pyramid and directional filter bank provides efficient frequency plane partition for image representation. However its overcompleteness is not suitable for some applications such as image coding, its critical sampling version has been studied recently. Although several types of the CSCT have been proposed, they have problems on their realization or unnatural frequency plane partition which is different from the original CT. In contrast to the way in conventional design methods based on a "top-down" approach, the proposed method is based on a "bottom-up" one. That is, the proposed CSCT decomposes the frequency plane into small directional subbands, and then synthesizes them up to a target frequency plane partition, while the conventional ones decompose into it directly. By this way, the proposed CSCT can design an efficient frequency division which is the same as the original CT for image representation can be realized. In this paper, its effectiveness is verified by non-linear approximation simulation.

Contourlet transform (CT) is a new image representation method, which can efficiently represent contours and textures in images. However, CT is a kind of overcomplete transform with a redundancy factor of 4/3. If it is applied to image compression straightforwardly, the encoding bit-rate may increase to meet a given distortion. This fact baffles the coding community to develop CT-based image compression techniques with satisfactory performance. In this paper, we analyze the distribution of significant contourlet coefficients in different subbands and propose a new contourlet-based embedded image coding (CEIC) scheme on low bit-rate. The well-known wavelet-based embedded image coding (WEIC) algorithms such as EZW, SPIHT and SPECK can be easily integrated into the proposed scheme by constructing a virtual low frequency subband, modifying the coding framework of WEIC algorithms according to the structure of contourlet coefficients, and adopting a high-efficiency significant coefficient scanning scheme for CEIC scheme. The proposed CEIC scheme can provide an embedded bit-stream, which is desirable in heterogeneous networks. Our experiments demonstrate that the proposed scheme can achieve the better compression performance on low bit-rate. Furthermore, thanks to the contourlet adopted in the proposed scheme, more contours and textures in the coded images are preserved to ensure the superior subjective quality.

A new mesh reconstruction technique, called dividing virtual belt algorithm (DVBA), is proposed for approximating the surface from a set of wire-frame contours. DVBA decomposes the branching region into a set of virtual belts and virtual canyons. A tiling technique based on the divide-and-conquer strategy is also introduced to approximate the surface from the virtual belt, and the virtual canyons are covered by a conventional polygon triangulation technique. The experimental result shows that our method works well even though there are many complicated branches in the object.

Bone Mineral Density (BMD) is an indicator of osteoporosis that is an increasingly serious disease, particularly for the elderly. To calculate BMD, we need to measure the volume of the femur in a noninvasive way. In this paper, we propose a noninvasive bone volume measurement method using x-ray attenuation on radiography and medical knowledge. The absolute thickness at one reference pixel and the relative thickness at all pixels of the bone in the x-ray image are used to calculate the volume and the BMD. First, the absolute bone thickness of one particular pixel is estimated by the known geometric shape of a specific bone part as medical knowledge. The relative bone thicknesses of all pixels are then calculated by x-ray attenuation of each pixel. Finally, given the absolute bone thickness of the reference pixel, the absolute bone thickness of all pixels is mapped. To evaluate the performance of the proposed method, experiments on 300 subjects were performed. We found that the method provides good estimations of real BMD values of femur bone. Estimates shows a high linear correlation of 0.96 between the volume Bone Mineral Density (vBMD) of CT-SCAN and computed vBMD (all P<0.001). The BMD results reveal 3.23% difference in volume from the BMD of CT-SCAN.

In this letter, we propose a new approach to incremental coding of the subfield codes for plasma display panels (PDPs). The proposed approach suppresses the halftone noise of the PDPs, while completely eliminating false contour noise, as do existing incremental subfield codes, by selecting an optimal incremental subfield code adaptively for a given input image. The proposed method maps the problem of selecting the optimal incremental subfield code onto a special-case shortest path problem. Results of experiment using 109 sample images illustrated that the proposed method improved the average peak signal-to-noise ratio by 4.4-6.2 dB in halftone noise compared with existing incremental subfield coding methods.

This paper proposed a region-based curve control function to detect the brain ventricle area by utilizing a geodesic active contour model. This is based on the average brightness of the brain ventricle area which is brighter in MRI images. Compared numerically by using various types of measurements, the proposed method can detect the brain ventricle area better than the existing methods.

In this paper, a method of feeding point analysis is proposed for microstrip antenna that is based on the probe current compensation (PCC) method and the overlapping-grid technique (OGT) in FDTD. Generally, in the Maxwell and Ampere's differential curl equation-based FDTD, calculated error occurs in computation of the feeding point current. By applying the PCC method, the current of the feeding point can be compensated. This paper also analyzes the proposed feeding point model with cylindrical shape. When feeding point model is analyzed by rectangular coordinate, contour path error occurs. Therefore, the OGT is proposed to solve the contour path error. In the OGT, the cylindrical coordinate is applied for modeling of feeding point. In the case of using the PCC method and the OGT, the calculated error and contour path error are reduced and improved.

Intelligent scissors is an interactive image segmentation algorithm which allows a user to select piece-wise globally optimal contour segment corresponding to a desired object boundary. However, the intelligent scissors is too sensitive to a noise and texture patterns in an image since it utilizes the gradient information concerning the pixel intensities. This paper describes a new intelligent scissors based on the concept of the separability in order to improve the object boundary extraction performance. The effectiveness of the proposed method has been confirmed by some experiments for actual images acquired by an ordinary digital camera.

This paper presents a motion-based boundary tracking method for a moving deformable object in an image sequence using a parametric active contour model. Deciding the local converging directions of the contour points is essential for correctly extracting the boundary of a moving deformable object. Thus, a new energy function for a parametric active contour model is proposed based on the addition of a directional energy term using a frame difference map to the greedy snake. The frame difference map is used to obtain motion information on an object with fast and non-rigid motion. Plus, updating rules for the frame difference map are also developed to encourage the stable convergence of the contour points. Experiments on a set of synthetic and real image sequences show that the proposed method could fully track a speedy deformable object while exactly extracting the boundary of the object in every frame.

We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.

In this paper, we propose a new vertex adjustment method which is based on the size ratio of an object and that of a polygon. In the conventional polygonal approximation methods, the sizes of an object and an approximating polygon are quite different, therefore there are so many error pixels between them. The proposed method reduces the size of error regions by adjusting the size of the polygon to that of an object. Simulation results show outstanding performance of the proposed method.

This letter proposes an efficient method to find the optimum subfield code, which minimizes the visual artifacts on the motion pictures of the plasma display panel (PDP). Existing codes were constructed to reduce dynamic false contour (DFC) only, and they are fixed codes used for every image. In contrast, the proposed method aims to minimize the total artifacts by DFC and halftone noise (HN), and it finds the best code for a given image, dynamically. First, this letter presents the novel models to estimate the effect of DFC and HN for given codewords and a given image. Then, it presents an efficient method that finds the optimum code for a given image using the well-known shortest-path algorithm. Experimental results, using 459 HDTV images, illustrated that the proposed approach improved the average PSNR by 0.713 dB and 7.004 dB in DFC and HN, respectively, when compared with Gravity Centre Code [1].

To detect lung area, this paper proposes curve stopping function that is based on CT coefficient of area of lung parenchyma instead of existing edge indication function. The proposed method was compared numerically using various measures and this method can detect better lung parenchyma region than existing methods. In addition, detecting procedure of the area of lung parenchyma was visually verified in lung images.

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.