Compressed sensing (CS) technology has a wide range of application prospects and research value in many fields, which will have a positive impact on the development of digital signal processing and communication techn...
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In view of the characteristics of high score remotesensing data containing rich semantic information, combined with different processing requirements for high-score remotesensingimages, this paper designs and imple...
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image fusion is a significant problem in many fields including digital photography, computational imaging and remotesensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusio...
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Change Detection is a classic and fundamental task in remotesensing(RS), and has accomplish remarkable achievement on bitemporal image based methods. However, bitemporal images are very expensive and needs profession...
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Intelligent registration of heterogeneous remotesensingimages is a hot issue in the field of remotesensing and has important research and application values. Due to the significant differences in data sources, imag...
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The proceedings contain 136 papers. The topics discussed include: comparison of image generation methods based on diffusion models;research on image preprocessing and target detection of vehicle-machine cooperative sy...
ISBN:
(纸本)9798350326444
The proceedings contain 136 papers. The topics discussed include: comparison of image generation methods based on diffusion models;research on image preprocessing and target detection of vehicle-machine cooperative system;a research on traditional tangka image classification based on visual features;research on imagerecognition based on neural network model learning algorithm;research on imagerecognition based on reinforcement learning;low-quality image binarization method based on threshold array system;comprehensive study of imageprocessing techniques for low-altitude target recognition;visual cryptography scheme by freeform optics based on optimal mass transport;SPECT bone scan image classification by fusing multi-attention mechanism with deep residual networks;fine-grained imagerecognition method based on input perception joint probability prediction;deep learning in image classification: an overview;auricular images with annotations for segmenting key-organ mapping regions in auricular diagnosis;insulator image dataset generation based on generative adversarial network;structure-preserving domain adaptation network for generating pencil sketches;underwater image recovery method considering target polarization characteristics;and a dual attention network for multimodal remotesensingimage matching.
This paper proposes a method for fast construction of the entropy field calculated in a sliding local window. The method is based on the representation of the local histogram by a truncated series using cosine basis f...
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Despite significant advancements in remotesensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundan...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Despite significant advancements in remotesensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remotesensingimages, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remotesensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results.
The remotesensingimage analysis, classification, and patternrecognition processes all depend on image segmentation. In this research, a search-based convolutional neural network (SBCNN) is used to identification me...
The remotesensingimage analysis, classification, and patternrecognition processes all depend on image segmentation. In this research, a search-based convolutional neural network (SBCNN) is used to identification method for remotesensingimages. Prior to applying the image data to the SKFCM with PeSOA segmentation step, the image data must first undergo pre-processing. When pre-processing satellite images for road networks, noise is removed using an improved median filtering technique. The image is then segmented using the SKFCM with PeSOA Segmentation technique to have inverse shape determination with lowest energy usage. Using an intensity constraint, it is possible to identify the segments of a building and vegetation, a road, and a barren area of land. Following segmentation, MLBP with DWT feature extraction is performed on the road satellite images, and the SBCNN is then used to categorize the images. After associated with obtainable methods, the findings of the suggested technique display excellent precision of 98.6%.
We introduce an interactive image segmentation and visualization framework for identifying, inspecting, and editing tiny objects (just a few pixels wide) in large multi-megapixel high-dynamic-range (HDR) images. Detec...
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ISBN:
(数字)9781665469463
ISBN:
(纸本)9781665469463
We introduce an interactive image segmentation and visualization framework for identifying, inspecting, and editing tiny objects (just a few pixels wide) in large multi-megapixel high-dynamic-range (HDR) images. Detecting cosmic rays (CRs) in astronomical observations is a cum-bersome workflow that requires multiple tools, so we developed an interactive toolkit that unifies model inference, HDR image visualization, segmentation mask inspection and editing into a single graphical user interface. The feature set, initially designed for astronomical data, makes this work a useful research-supporting tool for human-in-the-loop tiny-object segmentation in scientific areas like biomedicine, materials science, remotesensing, etc., as well as computer vision. Our interface features mouse-controlled, synchronized, dual-window visualization of the image and the segmentation mask, a critical feature for locating tiny objects in multi-megapixel images. The browser-based tool can be readily hosted on the web to provide multi-user access and GPU acceleration for any device. The toolkit can also be used as a high-precision annotation tool, or adapted as the frontend for an interactive machine learning framework. Our open-source dataset, CR detection model, and visualization toolkit are available at https://***/cy-xu/cosmic-conn.
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