Object contour detection is a task of extracting the shape created by the boundaries between objects in an image. Conventional methods limit the detection targets to specific categories, or miss-detect edges of patterns inside an object. We propose a new method to represent a contour image where the pixel value is the distance to the boundary. Contour detection becomes a regression problem that estimates this contour image. A deep convolutional network for contour estimation is combined with stereo vision to detect unspecified object contours. Furthermore, thanks to similar inference targets and common network structure, we propose a network that simultaneously estimates both contour and disparity with fully shared weights. As a result of experiments, the multi-tasking network drew a good precision-recall curve, and F-measure was about 0.833 for FlyingThings3D dataset. L1 loss of disparity estimation for the dataset was 2.571. This network reduces the amount of calculation and memory capacity by half, and accuracy drop compared to the dedicated networks is slight. Then we quantize both weights and activations of the network to 3-bit. We devise a dedicated hardware architecture for the quantized CNN and implement it on an FPGA. This circuit uses only internal memory to perform forward propagation calculations, that eliminates high-power external memory accesses. This circuit is a stall-free pixel-by-pixel pipeline, and performs 8 rows, 16 input channels, 16 output channels, 3 by 3 pixels convolution calculations in parallel. The convolution calculation performance at the operating frequency of 250 MHz is 9 TOPs/s.

In this Letter, we propose a blind and robust multiple watermarking scheme using Contourlet transform and singular value decomposition (SVD). The host image is first decomposed by Contourlet transform. Singular values of Contourlet coefficient blocks are adopted to embed watermark information, and a fast calculation method is proposed to avoid the heavy computation of SVD. The watermark is embedded in both low and high frequency Contourlet coefficients to increase the robustness against various attacks. Moreover, the proposed scheme intrinsically exploits the characteristics of human visual system and thus can ensure the invisibility of the watermark. Simulation results show that the proposed scheme outperforms other related methods in terms of both robustness and execution time.

This letter proposes a new superpixel segmentation algorithm based on global similarity and contour region transformation. The basic idea is that pixels surrounded by the same contour are more likely to belong to the same object region, which could be easily clustered into the same superpixel. To this end, we use contour scanning to estimate the global similarity between pixels and corresponded centers. In addition, we introduce pixel's gradient information of contour transform map to enhance the pixel's global similarity to overcome the missing contours in blurred region. Benefited from our global similarity, the proposed method could adherent with blurred and low contrast boundaries. A large number of experiments on BSDS500 and VOC2012 datasets show that the proposed algorithm performs better than traditional SLIC.

In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.

Consider selecting points on a contour in the x-y plane. In shape analysis, this is frequently referred to as contour sampling. It is important to select the points such that they effectively represent the shape of the contour. Generally, the stroke order and number of strokes are informative for that purpose. Several effective methods exist for sampling contours drawn with a certain stroke order and number of strokes, such as the English alphabet or Arabic figures. However, many contours entail an uncertain stroke order and number of strokes, such as pictures of symbols, and little research has focused on methods for sampling such contours. This is because selecting the points in this case typically requires a large computational cost to check all the possible choices. In this paper, we present a sampling method that is useful regardless of whether the contours are drawn with a certain stroke order and number of strokes or not. Our sampling method thereby expands the application possibilities of contour processing. We formulate contour sampling as a discrete optimization problem that can be solved using a type of direct search. Based on a geometric graph whose vertices are the points and whose edges form rectangles, we construct an effective objective function for the problem. Using different shape datasets, we demonstrate that our sampling method is effective with respect to shape representation and retrieval.

Real-time weather radar imaging technology is required for generating short-time weather forecasts. Moreover, such technology plays an important role in critical-weather warning systems that are based on vast Doppler weather radar data. In this study, we propose a weather radar imaging method that uses multi-layer contour detection and segmentation based on MAP-MRF estimation. The proposed method consists of three major steps. The first step involves generating reflectivity and velocity data using the Doppler radar in the form of raw data images of sweep unit in the polar coordinate system. Then, contour lines are detected on multi-layers using the adaptive median filter and modified Canny's detector based on curvature consistency. The second step interpolates contours on the Cartesian coordinate system using 3D scattered data interpolation and then segments the contours based on MAP-MRF prediction and the metropolis algorithm for each layer. The final step involves integrating the segmented contour layers and generating PPI images in sweep units. Experimental results show that the proposed method produces a visually improved PPI image in 45% of the time as compared to that for conventional methods.

Efficient optic disc (OD) segmentation plays a significant role in retinal image analysis and retinal disease screening. In this paper, we present a full-automatic segmentation approach called double boundary extraction for the OD segmentation. The proposed approach consists of the following two stages: first, we utilize an unsupervised learning technology and statistical method based on OD boundary information to obtain the initial contour adaptively. Second, the final optic disc boundary is extracted using the proposed LSO model. The performance of the proposed method is tested on the public DIARETDB1 database and the experimental results demonstrate the effectiveness and advantage of the proposed method.

Precise estimation of coronary arteries from computed tomography angiography (CTA) data is one of the challenging problems. This study focuses on automatic delineation of coronary arteries from 3D CTA data that may assess the clinicians in identifying the coronary pathologies. In this work, we present a technique that effectively segments the complete coronary arterial tree under the guidance of initial vesselness response without relying on heavily manual operations. The proposed method isolates the coronary arteries with accuracy by using localized statistical energy model in two directions provided with an automated seed which ensures an optimal segmentation of the coronaries. The detection of seed is carried out by analyzing the shape information of the coronary arteries in three successive cross-sections. To demonstrate the efficiency of the proposed algorithm, the obtained results are compared with the reference data provided by Rotterdam framework for lumen segmentation and the level-set active contour based method proposed by Lankton et al. Results reveal that the proposed method performs better in terms of leakages and accuracy in completeness of the coronary arterial tree.

This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op2)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.

In this paper, we propose a fully automatic method for extracting carotid artery contours from ultrasound images based on an active contour approach. Several contour extraction techniques have been proposed to measure carotid artery walls for early detection of atherosclerotic disease. However, the majority of these techniques require a certain degree of user interaction that demands time and effort. Our proposal automatically detects the position of the carotid artery by identifying blood flow information related to the carotid artery, and an active contour model is employed that uses initial contours placed in the detected position. Our method also applies a global energy minimization scheme to the active contour model. Experiments on clinical cases show that the proposed method automatically extracts the carotid artery contours at an accuracy close to that achieved by manual extraction.

Television white spaces (TVWS) are locally and/or temporally unused portions of TV bands. After TVWS regulations were passed in the USA, more and more regulators have been considering efficient use of TVWS. Under the condition that the primary user, i.e., terrestrial TV broadcasting system, is not interfered, various secondary users (SUs) may be deployed in TVWS. In Japan, the TVWS regulations started with broadcast-type SUs and small-area broadcasting systems, followed by voice radio. This paper aims to provide useful insights for more efficient utilization of TVWS as one of the options to meet the continuously increasing demand for wireless bandwidth. TVWS availability in Japan is analyzed using graphs and maps. As per the regulations in Japan, for TV broadcasting service, a protection contour is defined to be 51dBµV/m, while the interference contour for SU is defined to be 12.3dBµV/m. We estimate TVWS availability using these two regulation parameters and the minimum separation distances calculated on the basis of the ITU-R P.1546 propagation models. Moreover, we investigate and explain the effect of two important factors on TVWS availability. One is the measures to avoid adjacent channel interference, while the other is whether the SU has client devices with interference ranges beyond the interference area of the master device. Furthermore, possible options to increase available TVWS channels are discussed.

In tooth contour extraction there is insufficient intensity difference in x-ray images between the tooth and dental bone. This difference must be enhanced in order to improve the accuracy of tooth segmentation. This paper proposes a method to improve the intensity between the tooth and dental bone. This method consists of an estimation of tooth orientation (intensity projection, smoothing filter, and peak detection) and PCA-Stacked Gabor with ellipse Gabor banks. Tooth orientation estimation is performed to determine the angle of a single oriented tooth. PCA-Stacked Gabor with ellipse Gabor banks is then used, in particular to enhance the border between the tooth and dental bone. Finally, active contour extraction is performed in order to determine tooth contour. In the experiment, in comparison with the conventional active contour without edge (ACWE) method, the average mean square error (MSE) values of extracted tooth contour points are reduced from 26.93% and 16.02% to 19.07% and 13.42% for tooth x-ray type I and type H images, respectively.

Vector field convolution (VFC) provides a successful external force for an active contour model. However, it fails to extract the complex geometries, especially the deep concavity when the initial contour is set outside the object or the concave region. In this letter, dynamically constrained vector field convolution (DCVFC) external force is proposed to solve this problem. In DCVFC, the indicator function with respect to the evolving contour is introduced to restrain the correlation of external forces generated by different edges, and the forces dynamically generated by complex concave edges gradually make the contour move to the object. On the other hand, traditional vector field, a component of the proposed DCVFC, makes the evolving contour stop at the object boundary. The connections between VFC and DCVFC are also analyzed. DCVFC maintains desirable properties of VFC, such as robustness to initialization. Experimental results demonstrate that DCVFC snake provides a much better segmentation than VFC snake.

In this paper, an efficient multi-modal medical image fusion approach is proposed based on local features contrast and bilateral sharpness criterion in nonsubsampled contourlet transform (NSCT) domain. Compared with other multiscale decomposition analysis tools, the nonsubsampled contourlet transform not only can eliminate the “block-effect” and the “pseudo-effect”, but also can represent the source image in multiple direction and capture the geometric structure of source image in transform domain. These advantages of NSCT can, when used in fusion algorithm, help to attain more visual information in fused image and improve the fusion quality. At the same time, in order to improve the robustness of fusion algorithm and to improve the quality of the fused image, two selection rules should be considered. Firstly, a new bilateral sharpness criterion is proposed to select the lowpass coefficient, which exploits both strength and phase coherence. Secondly, a modified SML (sum modified Laplacian) is introduced into the local contrast measurements, which is suitable for human vision system and can extract more useful detailed information from source images. Experimental results demonstrate that the proposed method performs better than the conventional fusion algorithm in terms of both visual quality and objective evaluation criteria.

In this paper, we propose a hybrid fuzzy geometric active contour method, which embeds the spatial fuzzy clustering into the evolution of geometric active contour. In every iteration, the evolving curve works as a spatial constraint on the fuzzy clustering, and the clustering result is utilized to construct the fuzzy region force. On one hand, the fuzzy region force provides a powerful capability to avoid the leakages at weak boundaries and enhances the robustness to various noises. On the other hand, the local information obtained from the gradient feature map contributes to locating the object boundaries accurately and improves the performance on the images with heterogeneous foreground or background. Experimental results on synthetic and real images have shown that our model can precisely extract object boundaries and perform better than the existing representative hybrid active contour approaches.

In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a 'Gaussian Mixture' model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a new thresholding technique to classify image pixels. We assign a distance feature vectors to each pixel and segment the liver by a K-means clustering scheme. This initial boundary of the liver is conditioned by the Fourier transform. Then, a Geodesic Active Contour algorithm uses the boundaries to find the final surface. The novelty in our method is the proper selection and combination of sub-algorithms so as to find the border of an object in a low-contrast image. The number of parameters in the proposed method is low and the parameters have a low range of variations. We applied our method to 30 datasets including normal and abnormal cases of low-contrast/high-contrast images and it was extensively evaluated both quantitatively and qualitatively. Minimum of Dice similarity measures of the results is 0.89. Assessment of the results proves the potential of the proposed method for segmentation in low-contrast images.

Stereo-view and multi-view video formats are heavily investigated topics given their vast application potential. Depth Image Based Rendering (DIBR) system has been developed to improve Multiview Video Coding (MVC). Depth image is introduced to synthesize virtual views on the decoder side in this system. Depth image is a piecewise image, which is filled with sharp contours and smooth interior. Contours in a depth image show more importance than interior in view synthesis process. In order to improve the quality of the synthesized views and reduce the bitrate of depth image, a contour based coding strategy is proposed. First, depth image is divided into layers by different depth value intervals. Then regions, which are defined as the basic coding unit in this work, are segmented from each layer. The region is further divided into the contour and the interior. Two different procedures are employed to code contours and interiors respectively. A vector-based strategy is applied to code the contour lines. Straight lines in contours cost few of bits since they are regarded as vectors. Pixels, which are out of straight lines, are coded one by one. Depth values in the interior of a region are modeled by a linear or nonlinear formula. Coefficients in the formula are retrieved by regression. This process is called interior painting. Unlike conventional block based coding method, the residue between original frame and reconstructed frame (by contour rebuilt and interior painting) is not sent to decoder. In this proposal, contour is coded in a lossless way whereas interior is coded in a lossy way. Experimental results show that the proposed Contour Based Depth map Coding (CBDC) achieves a better performance than JMVC (reference software of MVC) in the high quality scenarios.

We describe an efficient algorithm that extracts a connected component of an isosurface, or a contour, from a 3D rectilinear volume data. The efficiency of the algorithm is achieved by three factors: (i) directly working with rectilinear grids, (ii) parallel utilization of a multi-core CPU for extracting active cells, the cells containing the contour, and (iii) parallel utilization of a many-core GPU for computing the geometries of a contour surface in each active cell using CUDA. Experimental results show that our hybrid parallel implementation achieved up to 20x speedup over existing methods on an ordinary PC. Our work coupled with the Contour Tree framework is useful for quickly segmenting, displaying, and analyzing a feature of interest in 3D rectilinear volume data without being distracted by other features.

The purpose of this study was to clarify whether viewers can perceive a digitally deteriorated image while pursuing a speedily moving digitally compressed image. We studied the perception characteristics of false contours among the various digital deteriorations for the four types of displays i.e. CRT, PDP, EL, LCD by changing the gradation levels and the speed of moving image as parameters. It is known that 8 bits is not high enough resolution for still images, and it is assumed that 8 bits is also not enough for an image moving at less than 5 deg/sec since the tracking accuracy of smooth pursuit eye movement (SPEM) is very high for a target moving at less than 5 deg/sec. Given these facts, we focused on images moving at more than 5 deg/sec. In our results, the images deteriorated by a false contour at a gradation level less than 32 were perceived by every subject at almost all velocities, from 5 degrees/sec to 30 degrees/sec, for all four types of displays we used. However, the perception rate drastically decreased when the gradation levels reached 64, with almost no subjects detecting deterioration for gradation levels more than 64 at any velocity. Compared to other displays, LCDs yielded relatively high recognition rates for gradation levels of 64, especially at lower velocities.

This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.