Search Results (33)

To solve the problem of the limited capability of multimodal image feature descriptors constructed by gradient information and the phase consistency principle, a method of cumulative structure feature descriptor construction with rotation invariance is proposed in this paper. Firstly, we extract the direction of multi-scale and multi-direction feature point edges using the Log-Gabor odd-symmetric filter and calculate the amplitude of pixel edges based on the phase consistency principle. Then, the main direction of the key points is determined based on the edge direction feature map, and the coordinates are established according to the main direction to ensure that the feature point descriptor has rotation invariance. Finally, the Log-Gabor odd-symmetric filter calculates the cumulative structural response in the maximum direction and constructs a highly identifiable descriptor with rotation invariance. We select several representative heterogeneous images as test data and compare the matching performance of the proposed algorithm with several excellent descriptors. The results indicate that the descriptor constructed in this paper is more robust than other descriptors for heterosource images with rotation changes. Full article

Star sensor is disturbed by strong straylight, which increases the gray level of the captured star map, and this leads to invalid detection of star points and affects the high-precision location of the centroid. To address this issue, we propose a star centroid localization method based on gradient-oriented multi-directional local contrast enhancement. First, the background gray level distribution patterns of star sensors under various actual straylight interference conditions are analyzed. Based on this analysis, a background imaging model for complex operational scenarios is established. Finally, simulations are conducted under complex conditions with straylight images to test the star point detection rate, false detection rate, centroid localization accuracy, and statistical significance testing. The results show that the proposed algorithm outperforms the TOP-HAT, MAX-BACKG (Max-Background Filtering), LCM (Local Contrast Measure), MPCM (Multiscale Patch-Based Contrast Measure), and CMLCM (Curvature-Based Multidirectional Local Contrast Method for Star Detection of Star Sensor) algorithms in terms of star point detection rate. Additionally, the RMSE centroid localization error is achieved with 0.1 pixels, demonstrating its ability to effectively locate star centroids under complex conditions and meet certain engineering application requirements. Full article

Infrared (IR) small target detection is a crucial component of infrared imaging systems and is vital for applications in surveillance, security, and early warning systems. However, most existing algorithms for detecting small targets in infrared imagery encounter difficulties in achieving both high accuracy and speed, particularly in complex scenes. Additionally, infrared image sequences frequently exhibit gradual background changes as well as sudden alterations, which further complicates the task of detecting small targets. To address these issues, a dual-region search method (DRSM) is proposed and combined with multi-directional filtering, min-sum fusion, and clustering techniques, forming an infrared small moving target detection method in complex scenes. First, a multi-directional filter bank is proposed and it causes the original infrared image sequence to retain only point-like features after the filtering. Then, several consecutive filtered feature maps are superimposed into one, where the moving target will leave a trajectory due to its motion characteristics. Finally, based on the trajectory, a dual-region search strategy is employed to pinpoint the exact location of the target. The experimental outcomes show that, compared to alternative algorithms, the proposed approach outperforms others in terms of detection accuracy and speed, particularly in diverse real-world complex scenarios. Full article

Synthetic Aperture Radar (SAR) imaging is essential for monitoring geomorphic changes, urban transformations, and natural disasters. However, the inherent complexities of SAR, particularly pronounced speckle noise, often lead to numerous false detections. To address these challenges, we propose the Multidirectional Attention Fusion Network (MDAF-Net), an advanced framework that significantly enhances image quality and detection accuracy. Firstly, we introduce the Multidirectional Filter (MF), which employs side-window filtering techniques and eight directional filters. This approach supports multidirectional image processing, effectively suppressing speckle noise and precisely preserving edge details. By utilizing deep neural network components, such as average pooling, the MF dynamically adapts to different noise patterns and textures, thereby enhancing image clarity and contrast. Building on this innovation, MDAF-Net integrates multidirectional feature learning with a multiscale self-attention mechanism. This design utilizes local edge information for robust noise suppression and combines global and local contextual data, enhancing the model’s contextual understanding and adaptability across various scenarios. Rigorous testing on six SAR datasets demonstrated that MDAF-Net achieves superior detection accuracy compared with other methods. On average, the Kappa coefficient improved by approximately 1.14%, substantially reducing errors and enhancing change detection precision. Full article

Time-varying underwater acoustic (UWA) channels are the key challenge of underwater acoustic communication (UAC). Although UAC exhibits time-variance characteristics significantly in time domains, its delay-Doppler (DD) domain representation tends to be time-invariant. Orthogonal time–frequency space (OTFS) modulation has recently been proposed and has acquired widespread interest due to its excellent performance over time-varying channels. In the UWA OTFS system, the novel DD domain channel estimation algorithm that employs a multidirectional adaptive moving average scheme is proposed. Specifically, the proposed scheme is cascaded by a channel estimator and moving average filter. The channel estimator can be employed to estimate the time-invariant channel of the DD domain multidirectionally, improving proportionate normalized least mean squares (IPNLMS). Meanwhile, the moving average filter is used to reduce the output noise of the IPNLMS. The performance of the proposed method is verified by simulation experiments and real-world lake experiments. The results demonstrate that the proposed channel estimation method can outperform those of benchmark algorithms. Full article

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm. Full article

This work presents a study on the modeling, analysis, and control of asymmetric source structures, which focuses on a multi-directional active mounting system that aims to consider the location and orientation of an actual automotive powertrain mount. An active mount was created by connecting a PZT (piezo-stack) actuator with a rubber grommet. Additional force necessary for every mount was determined by using forces caused by harmonic stimulation and the control input has the capability to reduce vibrations by engaging in detrimental opposition against the input. In addition, the vibration in the horizontal direction can be reduced with the adjustment of variables that can be modified via the dynamic interconnection of the source frame. This study especially evaluated the effectiveness of vibration reduction without a horizontal active component and determined the feasibility of control. Through sequences of simulated outcomes, it was demonstrated that the implementation of this asymmetric, bi-directional (both horizontally and vertically) active mount may effectively reduce stimulation oscillations. Additionally, a numerical validation was performed to reduce the vibrations generated by the modulation. It was accomplished by observing the system’s response utilizing a digital filter with a normalized least mean square method. The simulations of adaptive digital filters demonstrated that the efficacy of control diminishes when faced with intricate noise and signals, while the attenuation trend stays unaltered. Full article

In this study, a deep transfer learning model was developed using ResNet-101 architecture to diagnose double roller bearing defects. Vibration data were collected for three different load scenarios, including conditions without load, and for five different rotational speeds, ranging from 500 to 2500 RPM. Significantly, the speed condition of 2500 RPM has not previously been investigated, therefore offering a potential avenue for future investigations. This study offers a thorough examination of bearing conditions using multidirectional vibration data collected from accelerometers positioned in both vertical and horizontal orientations. In addition to transfer learning using ResNet-101, four additional models (VGG-16, VGG19, ResNet-18, and ResNet-50) were trained. Transfer learning using ResNet-101 consistently achieved the highest accuracy in all scenarios, with accuracy rates ranging from 90.78% to 99%. Scattergram Filter Bank 1 was used as the image input for training as a preprocessing method to enhance feature extraction. Research has effectively applied transfer learning to improve fault diagnosis accuracy, especially in limited data scenarios. This shows the capability of the method to differentiate between normal and faulty bearing conditions using signal-to-image transformation, emphasizing the potential of transfer learning to augment diagnostic performance in scenarios with limited training data. Full article

Active mounting systems have become more prevalent in recent years to effectively mitigate structure-induced vibration across the automobile chassis. This trend is particularly evident in engine mounts. Considerable research has been dedicated to this approach owing to its potential to enhance the quietness and travel comfort of automobiles. However, prior research has concentrated on a limited spectrum of specific vibrations and noise control or has been restricted to vertical vibration control. This article describes the modeling, analysis, and control of a source structure employing a multidirectional active mounting system designed to closely simulate the position and direction of an actual automobile engine mount. A piezoelectric stack actuator is connected in series to an elastic (rubber) mount to form an active mount. The calculation of the secondary force required for each active mount is achieved through the application of harmonic excitation forces. The control signal can also reduce vibrations caused by destructive interference with the input signal. Furthermore, horizontal oscillations can be mitigated by manipulating the parameters via dynamic interconnections of the source structure. We specifically examined the level of vibration reduction performance in the absence of a vertical active element operation and determined whether the control is feasible. Simulation outcomes demonstrate that this active mount, which operates in both the vertical and horizontal directions, effectively mitigates excitation vibrations. Furthermore, a simulation was conducted to mitigate the vibrations caused by complex signals (AM and FM signals) and noise. This was achieved by monitoring the system response using an adaptive filter NLMS algorithm. Adaptive filter simulations demonstrate that the control efficacy degrades in response to complex signals and noise, although the overall relaxation trend remains unchanged. Full article

In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform. Full article

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties. Full article

An image edge detection algorithm using multi-directional and multi-scale Gabor filters is proposed in this paper. The main merit of this method is that high edge detection accuracy can be obtained while maintaining noise robustness. The approach proposed in this paper consists of three procedures: firstly, the transformation to the CIE L*a*b* color space, which has a wide shading area and uniform distribution; secondly, under different scales, the edge feature information of the image is extracted from several different directions by Gabor filters, and a new edge strength map is obtained by feature fusion; thirdly, the new fused edge strength map is enhanced with local features, and a noise-resistant image edge detector is obtained under a novel hysteresis threshold calculation. The experiments illustrate that, compared to the methods involved, the designed edge detector outperforms by about 2% to 4%, and also shows competitive performance regarding the ability to handle noise. Full article

Ground filtering is one of the essential steps for processing airborne light detection and ranging data in forestry applications. However, the performance of existing methods is still limited in forested areas due to the complex terrain and dense vegetation. To overcome this limitation, we proposed an improved surface-based filter based on multi-directional narrow window and cloth simulation. The innovations mainly involve two aspects as follows: (1) sufficient and uniformly distributed ground seeds are identified by merging the lowest points and line segments from the point clouds within a multi-directional narrow window; (2) complete and accurate ground points are extracted using a cyclic scheme that includes incorrect ground point elimination using the internal force adjustment of cloth simulation, terrain reconstruction with moving least-squares plane fitting, and ground point extraction based on progressively refined terrain. The proposed method was tested in five forested sites with various terrain characteristics and vegetation distributions. Experimental results showed that the proposed method could accurately separate ground points from non-ground points in different forested environments, with the average kappa coefficient of 88.51% and total error of 4.22%. Moreover, the comparative experiments proved that the proposed method performed better than the classical methods involving the slope-based, mathematical morphology-based and surface-based methods. Full article

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified. Full article

Optical remote sensing images obtained in haze weather not only have poor quality, but also have the characteristics of gray color, blurred details and low contrast, which seriously affect their visual effect and applications. Therefore, improving the image clarity, reducing the impact of haze and obtaining more valuable information have become the important aims of remote sensing image preprocessing. Based on the characteristics of haze images, combined with the earlier dark channel method and guided filtering theory, this paper proposed a new image haze removal method based on histogram gradient feature guidance (HGFG). In this method, the multidirectional gradient features are obtained, the atmospheric transmittance map is modified using the principle of guided filtering, and the adaptive regularization parameters are designed to achieve the image haze removal. Different types of image data were used to verify the experiment. The experimental result images have high definition and contrast, and maintain significant details and color fidelity. This shows that the new method has a strong ability to remove haze, abundant detail information, wide adaptability and high application value. Full article