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 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.

scholarly journals Machine learning for human learners: opportunities, issues, tensions and threats

2020 ◽  
Author(s):  
Mary E. Webb ◽  
Andrew Fluck ◽  
Johannes Magenheim ◽  
Joyce Malyn-Smith ◽  
Juliet Waters ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ethical Issues ◽  
Practical Experience ◽  
Learning Systems ◽  
Learning System ◽  
Adaptive Behaviour ◽  
Recent Developments ◽  
Key Aspects ◽  
School Curricula

AbstractMachine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

Deep Learning: The Good, the Bad, and the Ugly

2019 ◽  
Vol 5 (1) ◽  
pp. 399-426 ◽  
Author(s):  
Thomas Serre
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Vision ◽  
Artificial Vision ◽  
Vision Systems ◽  
Comprehensive Overview ◽  
Biological Vision ◽  
Recent Developments ◽  
Visual Intelligence ◽  
Modern Machine

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

2020 ◽  
Vol 22 (3) ◽  
pp. 27-29 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
Chris Reberg-Horton ◽  
Anna M. Locke ◽  
Steven Mirsky ◽  
Edgar Lobaton
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Drought Stress ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Stress Detection ◽  
Vision Systems ◽  
Learning Techniques ◽  
Computer Vision Systems

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.

We Want Robots to See and Understand the World

2017 ◽  
Author(s):  
Antonio Torralba ◽  
Adolfo Plasencia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Present Situation ◽  
Digital Cameras ◽  
Vision Systems ◽  
Image Recording ◽  
Vision Processing ◽  
The World ◽  
Driverless Cars

Antonio Torralba, member of MIT CSAIL, opens the dialogue by describing the research he performs in the field of computer vision and related artificial intelligence (AI). He also compares the conceptual differences and the context of the early days of artificial intelligence—where hardly any image recording devices existed—with the present situation, in which an enormous amount of data is available. Next, through the use of examples, he talks about the huge complexity faced by research in computer vision to get computers and machines to understand the meanings of what they “see” in the scenes, and the objects they contain, by means of digital cameras. As he explains afterward, the challenge of this complexity for computer vision processing is particularly noticeable in settings involving robots, or driverless cars, where it makes no sense to develop vision systems that can see if they cannot learn. Later he argues why today’s computer systems have to learn “to see” because if there is no learning process, for example machine learning, they will never be able to make autonomous decisions.

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.

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.

scholarly journals Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Junji Sugiyama
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Wood Identification ◽  
Wood Industry ◽  
Scientific Disciplines ◽  
Learning Techniques ◽  
New Research ◽  
Wood Science

AbstractThe remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

scholarly journals Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Abdulkader Helwan ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Left Ventricle ◽  
Recognition Rate ◽  
Detection Task ◽  
Learning System ◽  
Backpropagation Neural Network ◽  
Vision Systems ◽  
Test Set ◽  
Cardiac Images

The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing the object of interest to be detected. Generally, the different backgrounds in which the objects of interest are contained significantly dwindle the performance of vision systems. In this work, we design a sliding windows machine learning system for the recognition and detection of left ventricles in MR cardiac images. We leverage on the capability of artificial neural networks to cope with some of the inevitable scene constraints encountered in medical objects detection tasks. We train a backpropagation neural network on samples of left and nonleft ventricles. We reformulate the left ventricles detection task as a machine learning problem and employ an intelligent system (backpropagation neural network) to achieve the detection task. We treat the left ventricle detection problem as binary classification tasks by assigning collected left ventricle samples as one class, and random (nonleft ventricles) objects are the other class. The trained backpropagation neural network is validated to possess a good generalization power by simulating it with a test set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained backpropagation neural network is used to determine if the sampled region in a target image contains a left ventricle or not. Lastly, we show the effectiveness of the proposed system by comparing the manual detection of left ventricles drawn by medical experts and the automatic detection by the trained network.

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