Computer Vision |

Edge AI in Action: Practical Approaches to Developing and Deploying Optimized Models

Mon, 17 Jun 2024 00:00:00 +0000

Using Machine Learning to Improve the Whiteboard Experience

Thu, 23 Jun 2022 00:00:00 +0000

Abstract

Virtual meetings and conferences are getting more common and more mainstream in the workplace. This shift in use of technology means that other work practices has to adapt to work with virtual meetings. One such practice is the use of writing on whiteboards. Just like some people prefer to read from a book instead of a monitor, a practice like writing on a whiteboard might never be replaced by writing on a tablet. This creates a set of problems of how can the whiteboard be integrated to work with the virtual world. There’s many ideas and potential solutions for this like using text recognition, but most of these solutions require the whiteboard to be detected in the first place. To solve this issue, a whiteboard detection model is proposed which is composed of a convolutional neural net to classify whiteboards in real-time videos through semantic image segmentation and computer vision to process the outline of the classified whiteboards into a set of points which can be used for further analysis and processing.

Opportunities with Hand Gesture Technology in Mobile Gaming

Mon, 23 Aug 2021 00:00:00 +0000

Abstract

During the past decade, there has been steady developments in the area of computer-vision based hand-gesture recognition (HGR) technologies, and expansion in the environments they are available for. Hand-gesture input, combined with head-mounted displays, has become the principal interaction method in virtual reality games. It also shows promise in other areas, such as sign-language recognition, interactive museum exhibitions, and interactive displays available in public spaces. This paper explores the possible introduction of HGR-based interaction in mobile games, based on the identification of key concepts in literature examining the aforementioned areas. The result is a proposition of four general heuristics guiding the design and development of mobile games based on HGR as the primary interaction method.

Computer Vision til EvoBotten

Mon, 13 Jun 2016 00:00:00 +0000

Abstract

Automated production using robots play a significant role in chemistry, biotechnology and microbiology. Robots that are designed to perform a specific task are in the long run cheaper and much more efficient than humans. In research, however, most tasks are done by humans even though many tasks are cumbersome and repetitive. What complicates the introduction of robots in research are the small variations in the task sequences that are frequently introduced. In this Master thesis we will contribute to a project called the EvoBot which seeks to make robots an integral part of research in order to lower costs and speed up the process of experimenting. We develop a proof-of-concept for a computer vision application for the EvoBot that enables it to find, locate and classify petri dishes and well plates. We present the design of the system implemented using the OpenCV framework along with physical modifications to the EvoBot. In addition to the vision system we develop a framework to test its precision and accuracy and lay ground work for future improvements. We evaluate different approaches based on accuracy and precision results of the detection methods that are experimented with. The evaluation indicates that detection without the use of tagging is feasible for use in the industry, with the introduction of some future improvements.

Introduction to Graphics and Image Analysis (Spring 2016)

Wed, 03 Feb 2016 00:00:00 +0000

Description

The objectives of this course are to provide students with the fundamental knowledge, comprehension, and skills required to design, build, and evolve smaller computer vision (CV) and computer graphics applications (CG).

Computer vision (image analysis) and computer graphics play decisive roles in our society in relation to automated processes in industry and in our daily lives. The dramatic increase of cameras in mobile devices and other consumer products (QRCodes, Kinnect and many others) makes it evident that developing applications based on efficient and accurate techniques are needed to keep up with the large amounts of data produced by cameras.

2D and 3D compute graphics (CG) on the other hand has been an integral part of our daily interaction with computers and (obviously) has a huge application domain (games, displays etc.), but has also lead to the developments of GPU’s. While seemingly different, computer vision/image analysis and computer graphics have quite a lot in common. The basic commonalities and difference between CV and CG will be covered in the course.

The objectives of this course are to provide students with the fundamental knowledge, comprehension, and skills required to design and build smaller computer vision and computer graphics applications on e.g. a PC or a mobile phone.

Through the course the student should be able to use the technique in more advanced topics on game engines, graphics, computer vision and pervasive computing. The course is an introductory course to the basics of computer vision and computer graphics and the intention is that the student will have sufficient knowledge to follow more advanced courses on game engines, graphics, computer vision and object recognition.

The course gives an introduction to computer graphics, computer vision/image analysis, linear algebra and GPU programming. In the course we will present the fundamental models used for CV and CG as well as techniques to implement them. You will in the exercises and mandatory assignments be getting hands-on experience with the techniques described during the lectures. In the exercises we will use images from digital cameras and web cameras to illustrate the theory. Web cameras can be borrowed.

In particular we will describe:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Segmentation and object recognition and a brief introduction to machine learning.
Geometric transformations (2D and 3D).
Cameras, Stereo, structured light (Kinnect).
Texture-mapping, shadows, hidden surface removal and lighting.
Basics of GPU programming.

Python will the main platform for the course yet students may chose to use C#.

Staff

Course Manager and Teacher:
Teacher:
Teaching Assistant:
Teaching Assistant:

Supporting Materials

Introduction to Graphics and Image Analysis (Spring 2015)

Wed, 28 Jan 2015 00:00:00 +0000

Description

In particular we will describe:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Segmentation and object recognition and a brief introduction to machine learning.
Geometric transformations (2D and 3D).
Cameras, Stereo, structured light (Kinnect).
Texture-mapping, shadows, hidden surface removal and lighting.
Basics of GPU programming.

Python will the main platform for the course yet students may chose to use C#.

Staff

Course Manager and Teacher:
Teaching Assistant:

Supporting Materials

Remote Eye Tracking Systems: Technologies and Applications

Mon, 05 Aug 2013 00:00:00 +0000

Computer Vision |

Edge AI in Action: Practical Approaches to Developing and Deploying Optimized Models

Using Machine Learning to Improve the Whiteboard Experience

Abstract

Opportunities with Hand Gesture Technology in Mobile Gaming

Abstract

Computer Vision til EvoBotten

Abstract

Introduction to Graphics and Image Analysis (Spring 2016)

Description

Contents

Staff

Supporting Materials

Introduction to Graphics and Image Analysis (Spring 2015)

Description

Contents

Staff

Supporting Materials

Remote Eye Tracking Systems: Technologies and Applications