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- 2019
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- pattern recognition
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- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Multi-layer CNN Features Aggregation for Real-time Visual Tracking
摘要： In this paper, we propose a novel convolutional neural network (CNN) based tracking framework, which aggregates multiple CNN features from different layers into a robust representation and realizes real-time tracking. We found that some feature maps have interference for effectively representing objects. Instead of using original features, we build an end-to-end feature aggregation network (FAN) which suppresses the noisy feature maps of CNN layers. The feature significantly benefits to represent objects with both coarse semantic information and fine details. The FAN, as a light-weight network, can run at real-time. The highlighted region of feature maps obtained from the FAN is the tracking result. Our method performs at a real-time speed of 24 fps while maintaining a promising accuracy compared with state-of-the-art methods on existing tracking benchmarks.
关键词： real-time tracking,convolutional neural network,feature aggregation,visual tracking
更新于2025-09-04 15:30:14
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Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: A pilot study
摘要： Background: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and non-cancerous epithelium based on nonlinear microscopic images was developed. Methods: Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types. Results: A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset. Conclusion: Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.
关键词： digital pathology,semantic segmentation,diagnostics,second-harmonic generation,convolutional neural networks,two-photon excited fluorescence,spectral histopathology,image analysis,head and neck cancer,coherent anti-stokes Raman scattering
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Diversifying Deep Multiple Choices for Remote Sensing Scene Classification
摘要： Recently, deep models have shown powerful ability for remote sensing scene representation. However, the training process of these deep methods requires large amount of labelled samples while usual remote sensing image datasets cannot provide enough training samples. Therefore, the learned model is usually suboptimal. To solve the problem, this work focuses on obtaining multiple choices by training multiple models simultaneously, and then the human oracle can choose a proper one from these choices. However, training several models separately usually makes the obtained results similar. This paper tries to diversify the obtained choices by encouraging the obtained choices to repulse from each other. Experiments are conducted on Ucmerced Land Use dataset to validate the effectiveness of the proposed method to provide multiple diversified choices.
关键词： Remote Sensing Image,Cross Entropy,Diversity,Convolutional Neural Network,Classification
更新于2025-09-04 15:30:14
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[IEEE 2018 Innovations in Intelligent Systems and Applications Conference (ASYU) - Adana, Turkey (2018.10.4-2018.10.6)] 2018 Innovations in Intelligent Systems and Applications Conference (ASYU) - Image Classification of Aerial Images Using CNN-SVM
摘要： Image classification is a very easy task for humans. Even a three years old child can classify an image instantly and without any doubt. However, teaching computers classifying images has been a working area for researchers for a long time because of the intrinsic difficulties of the task for computers. With the rise of deep learning, it has been possible to get better classification performance than before. In this work, we evaluated the performance of convolutional neural network combined with support vector machine for classifying aerial images based on presence of a vehicle.
关键词： unmanned aerial vehicle,vehicle detection,convolutional neural networks,aerial image,Support vector machines,image classification
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Non-blind Image Restoration Based on Convolutional Neural Network
摘要： Blind image restoration processors based on convolutional neural network (CNN) are intensively researched because of their high performance. However, they are too sensitive to the perturbation of the degradation model. They easily fail to restore the image whose degradation model is slightly different from the trained degradation model. In this paper, we propose a non-blind CNN-based image restoration processor, aiming to be robust against a perturbation of the degradation model compared to the blind restoration processor. Experimental comparisons demonstrate that the proposed non-blind CNN-based image restoration processor can robustly restore images compared to existing blind CNN-based image restoration processors.
关键词： convolutional neural network,non-blind image restoration
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - CNN-Based Boat Detection Model for Alert System Using Surveillance Video Camera
摘要： In Tokyo, various boats pass through the canal on the bayside. The loud sound created by these boats may cause some stress to the residents in that area. We propose a boat detection model based on convolutional neural networks (CNNs) using VGG19 that is trained using several types of boat pictures. Our proposed model aims to detect the type of boat passing through the canal using images obtained from the surveillance video camera. We ?nally achieve a practical result as F1-score of 0.70 by the proposed model.
关键词： Boat detection,Convolutional Neural Networks,Image Recognition,Boat classification,Surveillance Video Camera
更新于2025-09-04 15:30:14
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[IEEE 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) - Bangalore, India (2018.9.19-2018.9.22)] 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) - Enhanced Deep Image Super-Resolution
摘要： Recent advances in deep learning have facilitated new modalities for transforming the lower resolution image to higher resolution. The generated high resolution image must reconstruct the high frequency details of the image to generate a plausible result. To facilitate feature reuse for the task of super-resolution, we propose residual learning based convolutional neural network architecture. A pixel shuffle operation is performed in the upsampling procedure to mitigate the commonly encountered problem of artifacts in the predicted high resolution image. Our model makes use of a joint loss function consisting of pixel-wise loss and feature loss to learn the mapping from low resolution to its high resolution version. Additionally, our model has the ability to progressively increment to perform multi-scale super-resolution. An extensive experiment is performed to validate our model on the diverse ImageNet dataset. We show the effectiveness of our model through visual comparative assessment as well as quantitative comparative analysis with the state-of-the-art.
关键词： Residual block,Convolutional Neural Network,Image super-resolution
更新于2025-09-04 15:30:14
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[IEEE 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Coimbatore, India (2018.3.1-2018.3.3)] 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Processing Retinal Images to Discover Diseases
摘要： The retina of a human eye consists of billion of photosensitive cells (rods and cones) and alternative nerve cells that acquire and arrange visual information. The retina of a human eye is a thin tissue layer on the inside back wall of your eye. Three of the are Diabetic retinal diseases most Retinopathy, Glaucoma, and Cataract. The world is presently experiencing an epidemic of Diabetic Retinopathy (DR). Current predictions draw an estimation of doubling of the number affected from the current 170 million to an estimated 367 million by 2030. We propose a system wherein we extract blood vessels of the retina to detect eye diseases. Manually extracting the blood vessels of the human retina is a time-consuming task, and thus an automation of this process results in easy implementation of the work. This paper aims to design and consequently implement deep convolutional neural networks to identify the presence of an exudate, and thereby classify it into Diabetic Retinopathy, Glaucoma, and/or Cataract.
关键词： Computer vision,Glaucoma,Diabetic Retinopathy,Cataract,Convolutional Neural Networks,Retinal disease detection,CNN
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Real-time Stereo Reconstruction Failure Detection and Correction using Deep Learning
摘要： This paper introduces a stereo reconstruction method that besides producing accurate results in real-time, is capable to detect and conceal possible failures caused by one of the cameras. A classification of stereo camera sensor faults is initially introduced, the most common types of defects being highlighted. We next present a stereo camera failure detection method in which various additional checks are being introduced, with respect to the aforementioned error classification. Furthermore, we propose a novel error correction method based on CNNs (convolutional neural networks) that is capable of generating reliable disparity maps by using prior information provided by semantic segmentation in conjunction with the last available disparity. We highlight the efficiency of our approach by evaluating its performance in various driving scenarios and show that it produces accurate disparities on images from Kitti stereo and raw datasets while running in real-time on a regular GPU.
关键词： error correction,convolutional neural networks,stereo reconstruction,failure detection,semantic segmentation
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multi-Source Remote Sensing Data Classification via Fully Convolutional Networks and Post-Classification Processing
摘要： This paper presents a new data fusion methodology named Fusion-FCN for the classification of multi-source remote sensing data using fully convolutional networks (FCNs). Three different types of data including LiDAR data, hyperspectral images and very high resolution images are utilized in the proposed framework. Considering the confusions between similar categories (e.g., road and highway), we further implement post-classification processing with the topological relationship among different objects based on the result yielded by the proposed Fusion-FCN. The proposed method achieved an overall accuracy of 80.78% and a kappa coefficient of 0.80, which ranked first in the 2018 IEEE GRSS Data Fusion Contest.
关键词： Data fusion,deep learning,image segmentation,fully convolutional network
更新于2025-09-04 15:30:14