Urban aerial image understanding using symbolic data

1994 ◽  
Author(s):  
Henri Moissinac ◽  
Henri Maitre ◽  
Isabelle Bloch
Keyword(s):  
Image Understanding ◽  
Aerial Image ◽  
Symbolic Data

Evidence Accumulation for Spatial Reasoning in Aerial Image Understanding.

1983 ◽  
Author(s):  
V. S. S. Hwang ◽  
T. Matsuyama ◽  
L. S. Davis ◽  
A. Rosenfeld
Keyword(s):  
Spatial Reasoning ◽  
Image Understanding ◽  
Aerial Image ◽  
Evidence Accumulation

Cooperative Spatial Reasoning for Image Understanding

1997 ◽  
Vol 11 (01) ◽  
pp. 205-227 ◽  
Author(s):  
Takashi Matsuyama ◽  
Toshikazu Wada
Keyword(s):  
Problem Solving ◽  
Intelligent Agents ◽  
Spatial Reasoning ◽  
Spatial Information ◽  
Spatial Relations ◽  
Image Understanding ◽  
Aerial Image ◽  
Region Segmentation ◽  
Distributed Problem Solving ◽  
Cooperative Distributed Problem Solving

Spatial Reasoning, reasoning about spatial information (i.e. shape and spatial relations), is a crucial function of image understanding and computer vision systems. This paper proposes a novel spatial reasoning scheme for image understanding and demonstrates its utility and effectiveness in two different systems: region segmentation and aerial image understanding systems. The scheme is designed based on a so-called Multi-Agent/Cooperative Distributed Problem Solving Paradigm, where a group of intelligent agents cooperate with each other to fulfill a complicated task. The first part of the paper describes a cooperative distributed region segmentation system, where each region in an image is regarded as an agent. Starting from seed regions given at the initial stage, region agents deform their shapes dynamically so that the image is partitioned into mutually disjoint regions. The deformation of each individual region agent is realized by the snake algorithm14 and neighboring region agents cooperate with each other to find common region boundaries between them. In the latter part of the paper, we first give a brief description of the cooperative spatial reasoning method used in our aerial image understanding system SIGMA. In SIGMA, each recognized object such as a house and a road is regarded as an agent. Each agent generates hypotheses about its neighboring objects to establish spatial relations and to detect missing objects. Then, we compare its reasoning method with that used in the region segmentation system. We conclude the paper by showing further utilities of the Multi-gent/Cooperative Distributed Problem Solving Paradigm for image understanding.

scholarly journals UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images

2021 ◽  
Vol 13 (24) ◽  
pp. 4999
Author(s):  
Boyong He ◽  
Xianjiang Li ◽  
Bo Huang ◽  
Enhui Gu ◽  
Weijie Guo ◽  
...  
Keyword(s):  
Object Recognition ◽  
Object Detection ◽  
Real World ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Image Understanding ◽  
Scene Recognition ◽  
Aerial Image ◽  
Bounding Boxes ◽  
Ship Recognition

As a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognition datasets, aerial image datasets are more challenging to acquire and more expensive to label. Obtaining a large amount of high-quality aerial image data for object recognition and image understanding is an urgent problem. Existing studies show that synthetic data can effectively reduce the amount of training data required. Therefore, in this paper, we propose the first synthetic aerial image dataset for ship recognition, called UnityShip. This dataset contains over 100,000 synthetic images and 194,054 ship instances, including 79 different ship models in ten categories and six different large virtual scenes with different time periods, weather environments, and altitudes. The annotations include environmental information, instance-level horizontal bounding boxes, oriented bounding boxes, and the type and ID of each ship. This provides the basis for object detection, oriented object detection, fine-grained recognition, and scene recognition. To investigate the applications of UnityShip, the synthetic data were validated for model pre-training and data augmentation using three different object detection algorithms and six existing real-world ship detection datasets. Our experimental results show that for small-sized and medium-sized real-world datasets, the synthetic data achieve an improvement in model pre-training and data augmentation, showing the value and potential of synthetic data in aerial image recognition and understanding tasks.

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