The Understanding of Spatial-Temporal Behaviors

2019 ◽  
pp. 392-405
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Computer Vision ◽  
Image Understanding ◽  
Research Field ◽  
Cutting Edge ◽  
Important Task ◽  
Temporal Behavior ◽  
Time And Space ◽  
Behavior Understanding ◽  
Fast Development ◽  
Over Time

This chapter introduces a cutting-edge research field of computer vision and image understanding – the spatial-temporal behavior understanding. The main concepts, the focus of research, the typical technology, the fast development, etc. of this new field in recent years are overviewed. An important task in computer vision and image understanding is to analyze the scene through image operation on the image of scene in order to guide the action. To do this, one needs to locate the objects in the scene, and to determine how they change its position, attitude, speed, and relationships in the space over time. In short, it is to grasp the action in time and space, to determine the purpose of the operation, and thus to understand the semantics of the information they passed. This is referred ti as the understanding of spatial-temporal behaviors.

The Understanding of Spatial-Temporal Behaviors

2018 ◽  
pp. 1344-1354
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Computer Vision ◽  
Image Understanding ◽  
Research Field ◽  
Cutting Edge ◽  
Important Task ◽  
Temporal Behavior ◽  
Time And Space ◽  
Behavior Understanding ◽  
Fast Development ◽  
Over Time

This chapter introduces a cutting-edge research field of computer vision and image understanding – the spatial-temporal behavior understanding. The main concepts, the focus of research, the typical technology, the fast development, etc. of this new field in recent years are overviewed. An important task in computer vision and image understanding is to analyze the scene through image operation on the image of scene in order to guide the action. To do this, one needs to locate the objects in the scene, and to determine how they change its position, attitude, speed and relationships in the space over time. In short, it is to grasp the action in time and space, to determine the purpose of the operation, and thus to understand the semantics of the information they passed. This is refereed as the understanding of spatial-temporal behaviors.

MACHINE LEARNING FOR OBJECT RECOGNITION AND SCENE ANALYSIS

1994 ◽  
Vol 08 (01) ◽  
pp. 259-304 ◽  
Author(s):  
Y. KODRATOFF ◽  
S. MOSCATELLI
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Knowledge Acquisition ◽  
Image Understanding ◽  
Research Field ◽  
Learning System ◽  
Model Matching ◽  
Vision Systems ◽  
Recent Developments ◽  
Optimizing Model

Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptive target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented on and its strong and weak points will be underlined. In conclusion we will suggest what could be the “ideal” learning system for vision.

scholarly journals Image understanding for color constancy and vice versa

2021 ◽  
Vol 2021 (1) ◽  
pp. 63-67
Author(s):  
Simone Bianco ◽  
Marco Buzzelli
Keyword(s):  
Computer Vision ◽  
Color Constancy ◽  
Image Understanding ◽  
Research Field ◽  
Processing Step ◽  
Ill Posed

In this article we show the change in paradigm occurred in color constancy algorithms: from a pre-processing step in image understanding, to the exploitation of image understanding and computer vision results and techniques. Since color constancy is an ill-posed problem, we give an overview of the assumptions on which classical color constancy algorithms are based in order to solve it. Then, we chronologically review the color constancy algorithms that exploit results and techniques borrowed from the image understanding research field in order to exploit assumptions that could be met in a larger number of images.

Multi-human Parsing with a Graph-based Generative Adversarial Model

2021 ◽  
Vol 17 (1) ◽  
pp. 1-21
Author(s):  
Jianshu Li ◽  
Jian Zhao ◽  
Congyan Lang ◽  
Yidong Li ◽  
Yunchao Wei ◽  
...  
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Image Understanding ◽  
Important Task ◽  
Multimedia Systems ◽  
New Model ◽  
Single Person ◽  
Real World Applications ◽  
Adversarial Model

Human parsing is an important task in human-centric image understanding in computer vision and multimedia systems. However, most existing works on human parsing mainly tackle the single-person scenario, which deviates from real-world applications where multiple persons are present simultaneously with interaction and occlusion. To address such a challenging multi-human parsing problem, we introduce a novel multi-human parsing model named MH-Parser, which uses a graph-based generative adversarial model to address the challenges of close-person interaction and occlusion in multi-human parsing. To validate the effectiveness of the new model, we collect a new dataset named Multi-Human Parsing (MHP), which contains multiple persons with intensive person interaction and entanglement. Experiments on the new MHP dataset and existing datasets demonstrate that the proposed method is effective in addressing the multi-human parsing problem compared with existing solutions in the literature.

scholarly journals A review on food recognition technology for health applications

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Dario Allegra ◽  
Sebastiano Battiato ◽  
Alessandro Ortis ◽  
Salvatore Urso ◽  
Riccardo Polosa
Keyword(s):  
Computer Vision ◽  
Food Intake ◽  
Digital Media ◽  
Human Life ◽  
Image Understanding ◽  
Research Field ◽  
Comprehensive Overview ◽  
Scientific Databases ◽  
Food Recognition ◽  
The Impact

Food understanding from digital media has become a challenge with important applications in many different domains. On the other hand, food is a crucial part of human life since the health is strictly affected by diet. The impact of food in people life led Computer Vision specialists to develop new methods for automatic food intake monitoring and food logging. In this review paper we provide an overview about automatic food intake monitoring, by focusing on technical aspects and Computer Vision works which solve the main involved tasks (i.e., classification, recognitions, segmentation, etc.). Specifically, we conducted a systematic review on main scientific databases, including interdisciplinary databases (i.e., Scopus) as well as academic databases in the field of computer science that focus on topics related to image understanding (i.e., recognition, analysis, retrieval). The search queries were based on the following key words: “food recognition”, “food classification”, “food portion estimation”, “food logging” and “food image dataset”. A total of 434 papers have been retrieved. We excluded 329 works in the first screening and performed a new check for the remaining 105 papers. Then, we manually added 5 recent relevant studies. Our final selection includes 23 papers that present systems for automatic food intake monitoring, as well as 46 papers which addressed Computer Vision tasks related food images analysis which we consider essential for a comprehensive overview about this research topic. A discussion that highlights the limitations of this research field is reported in conclusions.

Language Change and Movement as Seen by Historical Linguistics

2018 ◽  
Author(s):  
Derek Nurse
Keyword(s):  
West Africa ◽  
20Th Century ◽  
Language Change ◽  
Historical Linguistics ◽  
Linguistic Features ◽  
Time And Space ◽  
Change Over Time ◽  
Comparative Linguistics ◽  
Language Boundaries ◽  
Over Time

The focus of this chapter is on how languages move and change over time and space. The perceptions of historical linguists have been shaped by what they were observing. During the flowering of comparative linguistics, from the late 19th into the 20th century, the dominant view was that in earlier times when people moved, their languages moved with them, often over long distances, sometimes fast, and that language change was largely internal. That changed in the second half of the 20th century. We now recognize that in recent centuries and millennia, most movements of communities and individuals have been local and shorter. Constant contact between communities resulted in features flowing across language boundaries, especially in crowded and long-settled locations such as most of Central and West Africa. Although communities did mix and people did cross borders, it became clear that language and linguistic features could also move without communities moving.

scholarly journals Revealing the spatial shifting pattern of COVID-19 pandemic in the United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Di Zhu ◽  
Xinyue Ye ◽  
Steven Manson
Keyword(s):  
United States ◽  
Long Range ◽  
Network Optimization ◽  
Network Modeling ◽  
The United States ◽  
Standard Series ◽  
Time And Space ◽  
Long Range Interactions ◽  
Dynamic Relationships ◽  
Over Time

AbstractWe describe the use of network modeling to capture the shifting spatiotemporal nature of the COVID-19 pandemic. The most common approach to tracking COVID-19 cases over time and space is to examine a series of maps that provide snapshots of the pandemic. A series of snapshots can convey the spatial nature of cases but often rely on subjective interpretation to assess how the pandemic is shifting in severity through time and space. We present a novel application of network optimization to a standard series of snapshots to better reveal how the spatial centres of the pandemic shifted spatially over time in the mainland United States under a mix of interventions. We find a global spatial shifting pattern with stable pandemic centres and both local and long-range interactions. Metrics derived from the daily nature of spatial shifts are introduced to help evaluate the pandemic situation at regional scales. We also highlight the value of reviewing pandemics through local spatial shifts to uncover dynamic relationships among and within regions, such as spillover and concentration among states. This new way of examining the COVID-19 pandemic in terms of network-based spatial shifts offers new story lines in understanding how the pandemic spread in geography.

scholarly journals End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

A New Method for Railway Fastener Detection Using the Symmetrical Image and Its EA-HOG Feature

2019 ◽  
Vol 34 (02) ◽  
pp. 2055006
Author(s):  
Jiajia Liu ◽  
Jianying Yuan ◽  
Yongfang Jia
Keyword(s):  
Computer Vision ◽  
Sparse Representation ◽  
Efficient Method ◽  
Important Task ◽  
New Method ◽  
Automatic Inspection ◽  
Test Image ◽  
Recognition Result ◽  
The Status ◽  
Railway Operation

Railway fastener recognition and detection is an important task for railway operation safety. However, the current automatic inspection methods based on computer vision can effectively detect the intact or completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In our method, we exploit the EA-HOG feature fastener image, generate two symmetrical images of original test image and turn the detection of the original test image into the detection of two symmetrical images, then integrate the two recognition results of symmetrical image to reach exact recognition of original test image. The potential advantages of the proposed method are as follows: First, we propose a simple yet efficient method to extract the fastener edge, as well as the EA-HOG feature of the fastener image. Second, the symmetry images indeed reflect some possible appearance of the fastener image which are not shown in the original images, these changes are helpful for us to judge the status of the symmetry samples based on the improved sparse representation algorithm and then obtain an exact judgment of the original test image by combining the two corresponding judgments of its symmetry images. The experiment results show that the proposed approach achieves a rather high recognition result and meets the demand of railway fastener detection.

Sign in / Sign up

Export Citation Format

Share Document