Contour Tracking Method Research Articles

Active contour model with improved second-order differential driven term

Driven terms in active contour models (ACMs) play a significant role in edge identification and image segmentation. However, in many existing ACMs, the driven terms are iteratively updated, resulting in slower segmentation speed because the image segmentation time increases with the iteration number. To address this problem, an ACM based on an improved second-order differential driven term (ISDDT) is presented, which can extract the edge information of images. The improved second-order differential driven term is computed only once before the iterations. Therefore, the computational complexity of our presented ACM is reduced, leading to a faster image segmentation speed. In addition, an improved regularization method with mean filtering is presented to improve the robustness of our ISDDT model. As an application, a target contour tracking method is developed based on our ISDDT model. Experimental results show that our ISDDT model segments images with inhomogeneous intensities well. The image segmentation speed of our proposed model has obvious advantages.

Adaptive contour-tracking to aid wide-field swept-source optical coherence tomography imaging of large objects with uneven surface topology.

High-speed and wide-field optical coherence tomography (OCT) imaging is increasingly essential for clinical applications yet faces challenges due to its inherent sensitivity roll-off and limited depth of focus, particularly when imaging samples with significant variations in surface contour. Here, we propose one innovative solution of adaptive contour tracking and scanning methods to address these challenges. The strategy integrates an electrically tunable lens and adjustable optical delay line control with real-time surface contour information, enabling dynamic optimization of imaging protocols. It rapidly pre-scans the sample surface to acquire a comprehensive contour map. Using this map, it generates a tailored scanning protocol by partitioning the entire system ranging distance into depth-resolved segments determined by the optical Raleigh length of the objective lens, ensuring optimal imaging at each segment. Employing short-range imaging mode along the sample contour minimizes data storage and post-processing requirements, while adaptive adjustment of focal length and reference optical delay line maintains high imaging quality throughout. Experimental demonstrations show the effectiveness of the adaptive contour tracking OCT in maintaining high contrast and signal-to-noise ratio across the entire field of view, even in samples with significantly uneven surface curvatures. Notably, this approach achieves these results with reduced data volume compared to traditional OCT methods. This advancement holds promise for enhancing OCT imaging in clinical settings, particularly in applications requiring rapid, wide-field imaging of tissue structures and blood flow.

A novel method for underactuated UUV tracking unknown contour based on forward-looking sonar

Tracking unknown contour is an essential problem for automated operations of Unmanned Underwater Vehicle (UUV). Applications such as harbor surveillance, island survey, and obstacle avoidance require UUV with the ability to track the contours of unknown marine structures. The absence of a priori information poses a great challenge in solving such problems. Most existing approaches follow the sense-act pattern by designing elaborate control laws to solve the problems under specific configurations. However, they demand much complexity to implement and lack generalizability in unstructured environments. This paper presents a novel unknown contour tracking method based on the sense-plan-act formulation and focuses primarily on the first two processes to make results applicable to conventional controllers. In terms of perception, forward-looking sonar (FLS) is used as the main device of data collection and a probabilistic perception model and a rolling occupancy grid map construction method based on this model are introduced. For planning, it is assumed that the UUV is underactuated and maneuvers in a 2D plane. Considering multiple tracking metrics, the problem is described as a multi-objective optimization problem and an emerging swarm intelligence algorithm is chosen as the solver. Notably, a contour prediction mechanism and a real-time risk assessment and replanning strategy are designed to ensure the continuity and navigational safety of the tracking process, respectively. Results demonstrate the effectiveness of the proposed method with contours extracted from real harbor and island.

Semiautomatic contour tracking method for biological object segmentation in thin-section electron microscope images with modified zero DC component-type Gabor wavelets.

In electron microscopic image processing, artificial intelligence (AI) is a powerful method for segmentation. Because creating training data remains time-consuming and burdensome, a simple and accurate segmentation tool, which is effective and does not rely on manual drawings, is necessary to create training data for AI and to support immediate image analysis. A Gabor wavelet-based contour tracking method has been devised as a step toward realizing such a tool. Although many papers on Gabor filter-based and Gabor filter bank-based texture segmentations have been published, previous studies did not apply the Gabor wavelet-based method to straightforwardly detect membrane-like ridges and step edges for segmentation because earlier works used a nonzero DC component-type Gabor wavelets. The DC component has a serious flaw in such detection. Although the DC component can be removed by a formula that satisfies the wavelet theory or by a log-Gabor function, this is not practical for the proposed scheme. Herein, we devised modified zero DC component-type Gabor wavelets. The proposed method can practically confine a wavelet within a small image area. This type of Gabor wavelet can appropriately track various contours of organelles appearing in thin-section transmission electron microscope images prepared by the freeze-substitution fixation method. The proposed method not only more accurately tracks ridge and step edge contours but also tracks pattern boundary contours consisting of slightly different image patterns. Simulations verified these results.

A robust visible and infrared image matching algorithm for power equipment based on phase congruency and scale-invariant feature

Dense and accurate matching for visible and infrared images of power equipment is crucial to intelligent diagnosis system of power grid, but existing matching methods usually fail in aligning visible and infrared image pairs because of significant intensity, resolution and viewpoint differences. In this paper, we propose a matching algorithm based on phase congruency and scale-invariant feature to address this problem. The proposed method consists of four steps. First, the maximum moment map of phase congruency of input image is computed based on phase congruency theory, which is then used to enhance the raw image. Second, Canny operator and contour tracking method are employed to detect image contours and scale-invariant feature points are extracted by the curvature scale space (CSS) corner detector. Third, the novel histogram of phase congruency orientation (HPCO) descriptors based on phase congruency information are computed for all feature points. Finally, a set of preliminary matches is obtained by the bidirectional matching, and refinement procedures are implemented to achieve dense and accurate matching results. We conduct the experiments on public available dataset. Experimental results show that the proposed method can robustly match feature points in visible and infrared image pairs of power equipment in spite of intensity, resolution and viewpoint differences, and achieve favorable performance compared to state-of-the-art approaches.

Enclosing contour tracking of highway construction equipment based on orientation-aware bounding box using UAV

Construction equipment tracking of highway construction site can obtain the spatiotemporal location in real time and provide data basis for construction risk control. The complete 2D moving of construction equipment in surveillance videos could be spatially represented by the translation, rotation and size change of corresponding images. To describe the temporal relationships of these variables, this study proposes a construction equipment enclosing contour tracking method based on orientation-aware bounding box (OABB), where UAV surveillance videos are employed to alleviate the occlusion problem. The method balances the rotation insensitivity of horizontal bounding box and the complexity of pixel-level segmented contour, which has three modules. The first module integrates OABB into a deep learning detector to provide detected contours. The second module updates OABBs with Kalman prediction to output tracked contours. The third module manages IDs of multiple tracked contours for construction equipment motions. Five in-situ UAV videos including 4325 frames were employed as the evaluation dataset. The tracking performance achieved 2.657 degrees in angle error, 97.523% in MOTA and 83.243% in MOTP.

Study on mandibular contour tracking method that sequentially updates the search direction for swallowing ability estimation

Study on mandibular contour tracking method that sequentially updates the search direction for swallowing ability estimation

A method for the extraction of shorelines from airborne lidar data in muddy areas and areas with shoals

ABSTRACT The shoreline is an extremely important geographical element in marine management and construction, and accurate determination of its position has become a critical requirement for social development. Shoreline extraction is typically achieved through elevation recursion from a digital elevation model (DEM) generated from lidar data. However, analysis focusing on different shoreline types is limited. Furthermore, the elevation recursion algorithm is not unified and cannot be adapted to every type of shoreline. This study analysed the characteristics of shoreline data in shoal and muddy areas and proposed a new method for high-precision shoreline extraction from original lidar data. In this study, the accuracy evaluation indicators have also been designed to compare the shoreline extracted using the proposed method, to that produced by the contour tracking method. The experiments demonstrated that the method proposed in this paper can more accurately locate the shoreline and recreate its position and shape more closely than the contour tracking method. The results exhibited a decrease in the average error of points from 1 m to 0.5 m, and in the overall standard deviation of the shoreline from 0.2116 m to 0.1656 m.

Extraction Methods for Pipeline Weld Defect Features

This paper introduced weld defect features based on a digital X-ray image using texture and geometric feature parameters. An improved complete local ternary pattern (CLTP) algorithm was proposed according to these parameters to extract the texture features from the image, while the geometric feature values were obtained using the contour tracking method. Furthermore, the practicability of this method was demonstrated with an example.

Radiofrequency thermocoagulation of trigeminal nerve assisted by nerve bundle extraction and image fusion based on hamilton-jacobi equation

Background and Objective: Radiofrequency thermocoagulation is an effective method for treating classic trigeminal neuralgia. However, the accurate positioning of thermocoagulation is difficult. The purpose of this study was to design an optimal strategy for performing adjuvant surgery.Methods: A total of 60 patients with trigeminal neuralgia were divided into two groups. One group received conventional computed tomography (CT) guided treatment (CT group). In the other group, neural fiber bundles were firstly extracted based on the Hamilton-Jacobi equation. Then, the MRI, CT, and fiber bundle images were fused to visualize the relationship among semilunar ganglion, trigeminal nerve, and puncture needle (fusion group).Results: Trigeminal fiber bundles were extracted quickly by the contour tracking method, and different types of image fusion were realized for radiofrequency surgery navigation. In the fusion group, 13.3% of patients could not reach semilunar ganglion, and 76.9% of the remaining cases reached the ideal damage area. In the CT group, the preoperative design shows that 26.7% of patients may have puncture difficulty, and 54.5% of remaining cases reached the ideal damage area.Conclusion: The technique of neural bundle extraction and image fusion based on the Hamilton-Jacobi equation can be used to plan the personalized puncture path targeting the semilunar ganglion.

WITHDRAWN: Ear biometric for personal identification using canny edge detection algorithm and contour tracking method

This paper is based on Human ear recognition in a new biometric technology by attracting the interest of biometrics community. Biometrics involves the programmed investigation of strategies in order to understand individual’s dependent on physical or social characteristics. Finding a decent biometric strategy, it has been extensively investigated in an ongoing year. The ear is a steady organ, it does not change with age and feelings, among a few biometric highlights.

Coral reef pore recognition and feature extraction based on borehole image

The coral reef pores are the main reservoir space of oil and gas resources. Through determining the structural form and distribution characteristics of pores, it can be helpful for scientific and reasonable stratigraphic division and reservoir evaluation, and provide basic data for the efficient development of using coral reefs as oil and gas reservoir sites. The core difficulty of interpretation and evaluation of coral reef pore structure and distribution characteristics is the pore recognition and feature extraction. In view of problems of low accuracy and efficiency in the recognition and feature extraction of coral reef pore at present, this paper proposes a method of coral reef pore recognition and feature extraction based on borehole image data. First of all, based on the borehole image and combined with the pore characteristics in borehole image, this paper proposes the coral reef pore recognition method. By using the feature of color gradient difference between the pore and the non pore in the borehole image, it realizes the segmentation of coral reef pore with low noise and weak interference. On the basis of obvious coral reef pores, the contour tracking method and image filling system of coral reef pore are established. Then, on the basis of traditional eight connected region marking algorithm, a pore region feature extraction method is proposed. The equivalent relationship between the temporary mark and the final mark is established, and an evaluation criterion that can comprehensively reflect the pore region feature parameters in the borehole image is formed. Finally, a systematic analysis and a comparison of results are carried out based on the actual coral reef borehole images. The results are consistent with the real values, which verifies the correctness of method.

Visual contour tracking based on inner-contour model particle filter under complex background

In this paper, a novel particle filter–based visual contour tracking method is proposed, which uses inner-contour model to track contour object under complex background. The purpose is to achieve effectiveness and robustness against complex background. To that end, the proposed method first utilized Sobel edge detector to detect the edge information along the normal line of the contour. Then, it sampled the inner part of the normal line to get the local color information, which was then combined with the edge information to construct new normal line likelihood. After that, all the inner color information was used to construct global color likelihood. Finally, the edge information, local color information, and global color information were fused into new observation likelihood. Experimental results showed that the proposed method was robust for contours tracking under complex background, and it was also computationally efficient and can run in real-time completely.

Technical report: An advanced algorithm for the description of mice oocyte cytoplasm and polar body

Abstract Objective To assess oocyte quality to improve assisted reproductive technologies and high quality embryo production using image processing methods. Design A cross-sectional study. Setting Pasteur Institute, Iran. Subject(s) 6 microscopic graphs (including 31 oocytes) of the NMRI mice oocytes that were recorded with a stereomicroscope (Olympus, CX21, Tokyo, Japan). Intervention(s) To apply image processing techniques for oocyte quality assessment. Main outcome measure(s) oocyte areas segmentation using Moore neighborhood contour tracking method and, oocytes clustering in terms of the number of particles in the cytoplasm with the gray-level co-occurrence matrix texture features. Result(s) The success rate of the proposed algorithm is 82/48% and 91/00% in segmentation and clustering stages, respectively. Conclusion(s) The results of the evaluation criteria also reflect the proper functioning of the proposed algorithm. The three obtained clusters provided the following classification of oocyte images. Oocytes with medium or high granularity and eventually some anomalies such as inclusions or vacuoles, oocytes with medium granularity, oocytes with low granularity are oocyte's clusters.

Open-Contour Tracking Using a New State-Space Model and Nonrigid Motion Training

Object tracking in a video sequence is usually achieved by tracking the bounding box over the object or the object’s boundary, each of which has somewhat different applications. In this paper, we present a new open-contour tracking algorithm based on a Bayesian framework in which the contour is a part of the object’s boundary. We first propose a new state-space model for the representation of contours, which can handle the rigid and nonrigid motions of contours independently. This model enables us to focus on the nonrigid motions during the training, and the model works for challenging rigid motion scenarios. In addition, for the robust tracking of contours, we propose a measurement function that considers the contrast on object boundaries, target appearance, and temporal coherence. We applied the proposed method to two kinds of open-contours targets, and the experimental results show that the proposed method achieves superior performance to the conventional contour tracking methods. The proposed method is also compared with recent bounding box tracking methods for the object tracking purposes, and the comparison shows that the proposed method works robustly to fast motions and yields a more accurate estimate of an object’s location than the conventional bounding box tracking methods.

Multiple cues-based active contours for target contour tracking under sophisticated background

In this paper, we propose a novel target contour tracking method under sophisticated background using the multiple cues-based active contour model. To locate the target position, a contour-based mean-shift tracker is designed which combines both color and texture information. To reduce the adverse impact of sophisticated background and also accelerate the curve motion, we propose a two-layer-based target appearance model that combines both discriminative pre-learned-based global layer and voting-based local layer. The proposed appearance model is able to extract rough target region from the complex background, which provides important target region information for our active contour model. We subsequently introduce a dynamical shape model to provide prior target shape information for more stable segmentation. To obtain accurate target boundaries, we design a new multiple cues-based active contour model which integrates with target edge, discriminative region, and shape information. The experimental results on 30 video sequences demonstrate that the proposed method outperforms other competitive contour tracking methods under various tracking environment.

Usefulness of ultrasound contrast media for cardiac output measurement with echocardiography

Abstract : The purpose of this study was to determine if contrast media would enhance visualization of the endocardiumfor assessment of left ventricle (LV) function. In addition, differences between pre- and post-contrast evaluation forthe cardiac output measurements including the modified Simpson’s method and automated contour tracking (ACT)method were examined. Ten clinically healthy adult beagle dogs (three males and seven females) between 2~3 yearsold and weighing 6.6~10.8 kg were used. Echocardiographic examinations were performed to compare pre- and post-contrast LV endocardium visualization using a segmental scoring method. Two different methods for measuring cardiacoutput were also compared. LV visualization was significantly enhanced in post-contrast echocardiography (p < 0.01).Significant differences between pre- and post-contrast measurements for the modified Simpson’s method (p < 0.05)were also observed. No significant difference was found for the ACT method. Contrast echocardiography provides betterLV chamber opacification and significantly improves wall segment visualization. Furthermore, contrast echocardiographyfor measuring cardiac output is helpful for the modified Simpson’s method.Keywords : cardiac output, contrast echocardiography, dog, segmental scoring,

Locally adaptive combining colour and depth for human body contour tracking using level set method

In this study, the authors present a novel human body contour tracking method which adaptively combines colour and depth cues of RGB‐D images in level set framework. The authors model the body object by active contour. When the body object is far away from the background objects, it is relatively easy to separate the object from the background using the depth cue. In this case, the depth cue should dominate the evolution of the active contour. When some part of the body is very close to the background objects, the discriminability of the depth decreases rapidly. In this local region the colour cue should dominate the evolution, whereas the depth cue should still play an important role in other regions. To achieve these objectives, the authors propose to use a superpixel‐based locally adaptive weight map to determine the importance of the depth cue. Moreover, to obtain more accurate contours and to avoid error drifting, based on the two novel properties of the human body surface in the depth images, the authors propose two simple but effective algorithms to refine the tracking results of the level set method. The promising results demonstrate the performance of the proposed method.

A refined particle filter based on determined level set model for robust contour tracking

Traditional particle filter which uses simple geometric shapes for representation cannot track objects with complex shape accurately. In this paper, we propose a refined particle filter method for contour tracking based on a determined binary level set model (DBLSM). In contrast with other previous work, the computational efficiency is greatly improved due to the simple form of the level set function. The DBLSM adds prior knowledge of the target model to the implementation of curve evolution which improves the curve acting principle and ensures a more accurate convergence to the target. Finally, we perform curve evolution in the update step of particle filter to make good use of the observation at current time. Some appearance information are considered together with the energy function to measure weights for particles, which can identify the target more accurately. Experiment results on several challenging video sequences have verified the proposed algorithm is efficient and effective in many complicated scenes.

A contour tracking method of large motion object using optical flow and active contour model

In this study, an object contour tracking method is proposed for an object with large motion and irregular shape in image sequence. To track object contour accurately, an active contour model was used, and the initial snake point of the next frame is set by defining feature points with changing curvature in the object tracked from the previous frame and calculating an optical flow at the location. Here, any misled optical flow due to irregular changes in shape or fast motion was filtered by producing a difference edge map from the previous frame, and as a solution to the energy shortage of objects with complex contour, a method of adding snake points by partial curvature was applied. Findings from experiments with real image sequence showed that the contour of an object with large motion and irregular shapes was extracted in a relatively precise way.