Complex Background Interference Research Articles

Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion

In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.

General model for linear information extraction based on the shear transformation

Most images have lots of linear information, which plays an important role in the tasks of image processing and pattern recognition. However, due to the interference of complex background in the images, the multiple directional characteristics of the linear information, and the problems of directional limitations in traditional methods, all these lead to the incomplete and discontinuities existed in the extracted linear information. To solve these problems, this paper puts forward a general model for improving the performance of the linear information extraction methods (LIEM) by utilizing the shear transformation. In this model, the shear transformation can transform an object (a filter or an image) in multiple directions, which directly or indirectly increases the directional characteristics of the traditional LIEM, and improves their performances to extract directional linear information and weak linear information. This paper elaborates on the basic principles of the model and its two implementations for the specific tasks. Furthermore, a variety of experiments are made to verify the versatility and effectiveness of the proposed model.

Hyperspectral image target detection via integrated background suppression with adaptive weight selection

Abstract Hyperspectral image (HSI) target detection is an important technology, but is faced with the critical challenge of complex background interference. The background clutter is difficult to effectively suppress, which often results in a relatively poor detection performance being obtained. To address this problem, a new target detection method via integrated background suppression with adaptive weight selection (TDIBS_AWS) is proposed in this paper. TDIBS_AWS has the following capabilities: (1) it explores the great difference between the target and background with two different background suppression strategies: principal component analysis (PCA) and spectral unmixing (SU); (2) it applies adaptive weight selection based on the particle swarm optimization (PSO) algorithm to optimize the weighting coefficient of the integrated background suppression model; and (3) it takes full advantage of support vector data description (SVDD) to improve the target detection performance for the rest of the information, after the background and noise have been removed. Experiments were undertaken using synthetic data and a real HSI, and it was found that TDIBS_AWS generally shows a better detection performance than the other state-of-the-art target detection methods.

Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes

Vanishing point detection is an essential component of vision-based autonomous navigation for unmanned ground vehicles and mobile robots. In this paper, we propose an accurate and robust vanishing point detection method for unstructured road scenes, where the road scenes lack clear road markings and include complex background interference. Since only the road region provides informative clues for vanishing point detection, we first introduce the manifold ranking method to estimate the road region based on background suppression. Then, we develop a series of principles for voter selection, and propose a dynamic adjustment strategy for the candidate selection that reduces the search scope of the vanishing point to perform candidate selection. Finally, we propose an effective voting strategy, in which the candidate that achieves the greatest number of votes in the voting space is considered to be the vanishing point. The experimental results on a large number of unstructured road images show that our proposed method is more accurate and robust than five existing methods.

Region of interest extraction via common salient feature analysis and feedback reinforcement strategy for remote sensing images

Saliency detection is an effective approach to extract regions of interest (ROIs) for remote sensing images. However, existing saliency detection models mainly focus on ROI extraction from a single image and usually are not able to produce satisfactory results because of complex background interference in remote sensing images. The employment of mutual information in a set of remote sensing images can provide an effective solution to this issue. In this paper, we propose a novel saliency detection model for multiple remote sensing images to simultaneously extract ROIs and identify images without ROIs. First, common salient feature analysis based on synthesized feature clustering and global contrast is implemented to exploit global correspondence in the synthesis feature domain, thereby highlighting preliminary ROIs against background interference and assigning lower saliency values to images without ROIs. Then, we design an exclusion criterion based on saliency value judgment to remove images without ROIs, and the remaining saliency maps are refined by an enhancement strategy. Finally, the enhanced maps serve as a feedback to yield a homogenous synthesized feature space in which integral ROIs with subtle borders are extracted by the reused cluster-based saliency calculation. Experiments reveal that our model outperforms seven state-of-the-art models by achieving the best ROC curve (AUC = 0.945) and maximal F-measure at 0.729.

A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection

Synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods of SAR ship detection are difficult to detect small scale ships and avoid the interference of inshore complex background. Deep learning detection methods have shown great performance on various object detection tasks recently but using deep learning methods for SAR ship detection does not show an excellent performance it should have. One of the important reasons is that there is no effective model to handle the detection of multiscale ships in multiresolution SAR images. Another important reason is it is difficult to handle multiscene SAR ship detection including offshore and inshore, especially it cannot effectively distinguish between inshore complex background and ships. In this paper, we propose a densely connected multiscale neural network based on faster-RCNN framework to solve multiscale and multiscene SAR ship detection. Instead of using a single feature map to generate proposals, we densely connect one feature map to every other feature maps from top to down and generate proposals from each fused feature map. In addition, we propose a training strategy to reduce the weight of easy examples in the loss function, so that the training process more focus on the hard examples to reduce false alarm. Experiments on expanded public SAR ship detection dataset, verify the proposed method can achieve an excellent performance on multiscale SAR ship detection in multiscene.

Identification of Abnormal Furnace Flame Based Image Processing

Research using image processing techniques to identify new burning point of industrial tube furnace.First, get the flame image through the design system, and then using the symmetric differencing to obtain each furnace burning point position, and finally the use of Clustering Algorithm to identify the new burning point. Through the experimental simulation show that this algorithm can avoid the complex background interference in the furnace, accurate and effective identify the new burning point.

Hand Segmentation for Hand-based Biometrics in Complex Environments

Hand-basedbiometric techniques, such as the ones based on palmprint, hand vein and handshape, is becoming more important because of their convenience and highperformance. Hand segmentation is one of the most important steps in thesetechniques. It is a challenge task to accurately segment hand in complex environment because of the complex background,varying illuminance and other unexpected interference factors. This paperproposes a novel approach to segment hand in complex environment using colorand boundary information. In the proposed approach, the hand skin color model(HSCM) is firstly constructed by using artificial neural network (ANN). Thenthe HSCM is used to generate a probability map (PM) and the hand is roughlysegmented from the complex background by thresholding PM. After that, the handboundary is extracted from the original image by edge detecting and votingtechniques. Finally, the hand boundary is employed to cut the roughly segmentedhand to get the final segmented hand. The experimental results show that theproposed approach can effectively segment hand in complex environment.

Target Recognition in Complex Background with Optical Joint Transform Correlator

The optical joint transform correlator (JTC) has been used in target real-time recognition, but the interference of complex background has always been the difficult problem for recognition system, which limits its further development and application. In this paper the principle and experimental system of JTC are introduced. The interference of complex background is simulated and the optimization method is proposed to process the joint power spectrum to eliminate zero-order correlation peak. The analysis result shows that the method of power subtraction can enhance the output peaks and improve the recognition efficiency, particularly for target identification in complex background.