- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Multi-target joint detection, tracking and classification based on random finite set for aerospace applications
摘要： Multi-target detection, tracking and classification are important problems in aerospace applications, such as reconnaissance, airborne and spaceborne sensing. These problems are correlated but are difficult to be solved simultaneously, especially for systems with multiple sensors. This paper summarized the existing work for multi-target joint detection, tracking and classification based on labeled random finite set. Furthermore, a new algorithm is proposed for multi-sensor multi-target joint detection, tracking and classification problem. A conditional multi-sensor multi-target state estimator is derived, and the optimal solution is then obtained based on the minimum Bayes risk criterion. The numerical simulations are performed, and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set. It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection, tracking and classification.
关键词： Multiple targets,Tracking and classification,Labeled RFS,Generalized bayesian risk,Sensor registration,Joint detection
更新于2025-09-23 15:23:52
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Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images
摘要： Blindness usually comes from two main causes, glaucoma and diabetes. Robust mass screening is performed for diagnosing, such as screening that requires a cost-effective method for glaucoma and diabetic retinopathy and integrates well with digital medical imaging, image processing, and administrative processes. For addressing all these issues, we propose a novel low-cost automated glaucoma and diabetic retinopathy diagnosis system, based on features extraction from digital eye fundus images. This paper proposes a diagnosis system for automated identification of healthy, glaucoma, and diabetic retinopathy. Using a combination of local binary pattern features, Gabor filter features, statistical features, and color features which are then fed to an artificial neural network and support vector machine classifiers. In this work, the classifier identifies healthy, glaucoma, and diabetic retinopathy images with an accuracy of 91.1%,92.9%, 92.9%, and 92.3% and sensitivity of 91.06%, 92.6%, 92.66%, and 91.73% and specificity of 89.83%, 91.26%, 91.96%, and 89.16% for ANN, and an accuracy of 90.0%,92.94%, 95.43%, and 97.92% and sensitivity of 89.34%, 93.26%, 95.72%, and 97.93% and specificity of 95.13%, 96.68%, 97.88%, and 99.05% for SVM, based on 5, 10, 15, and 31 number of selected features. The proposed system can detect glaucoma, diabetic retinopathy and normal cases with high accuracy and sensitivity using selected features, the performance of the system is high due to using of a huge fundus database.
关键词： and Classification,Diagnosis,Diabetic Retinopathy,Glaucoma,Eye Fundus
更新于2025-09-23 15:22:29
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Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation
摘要： Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation.
关键词： dynamic path-planning algorithms,obstacle detection and classification,perception in challenging conditions
更新于2025-09-19 17:15:36
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[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Performance Analysis of Classifiers and Future Directions for Image Analysis Based Leaf Disease Detection
摘要： Plants play a very important role in the environment to maintain ecosystem, so this is our responsibility to protect it by detected disease which appears in it. In the plant disease, most symptoms appear on leaf, so by performing some image analysis we can detect these diseases very fast and accurately. This paper includes survey of different techniques which are used in leaf disease detection. To detect plant disease color conversion, Canny and Sobel edge detectors are used initially and then some segmentation techniques, i.e., Otsu and k-means, are used; after then, feature extraction takes place and is classified with classification techniques.
关键词： K-means segmentation,Edge detection,GLCM and classification technique
更新于2025-09-09 09:28:46
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[Lecture Notes in Electrical Engineering] Recent Trends in Communication, Computing, and Electronics Volume 524 (Select Proceedings of IC3E 2018) || Classification of Normal and Abnormal Retinal Images by Using Feature-Based Machine Learning Approach
摘要： The human eye is one of the most beautiful and important sense organs of human body as it allows visual perception by reacting to light and pressure. Human eyes are capable of differentiating approximately 10 million colors. It contains more than 2 million tissues and cells. Along with these entire specialties, human eyes are the most delicate and sensitive organ. If not taken proper care, it may be infected with various diseases like glaucoma, myopia, hyper-myopia, diabetic retinopathy, age-related macular disease. Therefore, early-stage detection of these diseases could help in curing them completely and prevent from complete blindness. In this paper, we propose an approach to classify the normal (healthy) and abnormal (disease-infected) retinal images by using retinal image feature-based machine learning classification approach. The performance of proposed approach by using SVM classifier is 77.3%, which is found better with respect to the other classifiers like k-NN, linear discriminant, quadratic discriminant and decision tree classifiers.
关键词： Machine learning and classification,Texture features,Retina images
更新于2025-09-09 09:28:46
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Wavelet Method for Automatic Detection of Eye-Movement Behaviors
摘要： With the rapid development of eye tracking technology, eye movements have become more and more important in human-computer interaction. Generally, eye movements are classified into fixation, saccade and smooth pursuit. Since the eye movements are natural and fast, contain important cues for human cognitive state and visual attention, the eye movement behaviors are difficult to detect and classify. In this study, the novel eye-movement data filtering and eye-movement classification algorithm are proposed. The nonlinear wavelet threshold denoising method was used to the eye-movement data and detect saccades in the presence of smooth pursuit movements, according to different eye-movement behaviors related to the different characteristics of wavelet detail coefficients. Experiments were conducted to compare the eye-movement signal analyzing algorithm based on wavelet with other algorithms. The results showed that the eye-movement data filtering algorithm based on wavelet performed better than the other eye-movement filters. Moreover, the classification algorithm based on wavelet can classify different eye-movement behaviors more accurately. Then we used eye tracking technology to record and analyze the user’s eye movement during the test, so as to get the user's psychological and cognitive state.
关键词： Wavelet analysis,Eye tracking,Eye-Movement Detection and Classification
更新于2025-09-04 15:30:14