{"id":null,"code":"TKO_7096","name":{"valueFi":"Computer Vision and Sensor Fusion","valueEn":"Computer Vision and Sensor Fusion","valueSv":"Computer Vision and Sensor Fusion"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534322550,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"After completing this course, students will understand the key theories, techniques, and algorithms—particularly in deep learning—applied to two popular fields: computer vision and sensor fusion. In addition to the theoretical knowledge, students will gain practical experience using modern tools (e.g., PyTorch) and Python libraries to work on real-world computer vision tasks and datasets.","valueEn":"After completing this course, students will understand the key theories, techniques, and algorithms—particularly in deep learning—applied to two popular fields: computer vision and sensor fusion. In addition to the theoretical knowledge, students will gain practical experience using modern tools (e.g., PyTorch) and Python libraries to work on real-world computer vision tasks and datasets.","valueSv":"After completing this course, students will understand the key theories, techniques, and algorithms—particularly in deep learning—applied to two popular fields: computer vision and sensor fusion. In addition to the theoretical knowledge, students will gain practical experience using modern tools (e.g., PyTorch) and Python libraries to work on real-world computer vision tasks and datasets."}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"Application of computer vision and sensor fusion technology has drawn a lot of industrial and academic interest in recent years. They are widely used in many real applications such as autonomous vehicles, remote sensing, video surveillance and military. This course consists of two parts: \r\n\r\nThe *first* part introduces students to computer vision, starting from basics and then turning to more modern deep leaning models.  It covers applications of deep learning in different computer vision problems such as image classification, object localization, object detection, image segmentation, object tracking and pose estimation. \r\n\r\n*Second* part gives an overview of classic sensor fusion techniques, camera calibration, stereo vision and modern sensors such as camera, radar and Lidar. It also covers deep learning algorithms for image fusion, multi-source fusion and depth image prediction. Moreover, application of multi-senor fusion in autonomous driving, target recognition and remote sensing will be discussed.","valueEn":"Application of computer vision and sensor fusion technology has drawn a lot of industrial and academic interest in recent years. They are widely used in many real applications such as autonomous vehicles, remote sensing, video surveillance and military. This course consists of two parts: \r\n\r\nThe *first* part introduces students to computer vision, starting from basics and then turning to more modern deep leaning models.  It covers applications of deep learning in different computer vision problems such as image classification, object localization, object detection, image segmentation, object tracking and pose estimation. \r\n\r\n*Second* part gives an overview of classic sensor fusion techniques, camera calibration, stereo vision and modern sensors such as camera, radar and Lidar. It also covers deep learning algorithms for image fusion, multi-source fusion and depth image prediction. Moreover, application of multi-senor fusion in autonomous driving, target recognition and remote sensing will be discussed.","valueSv":"Application of computer vision and sensor fusion technology has drawn a lot of industrial and academic interest in recent years. They are widely used in many real applications such as autonomous vehicles, remote sensing, video surveillance and military. This course consists of two parts: \r\n\r\nThe *first* part introduces students to computer vision, starting from basics and then turning to more modern deep leaning models.  It covers applications of deep learning in different computer vision problems such as image classification, object localization, object detection, image segmentation, object tracking and pose estimation. \r\n\r\n*Second* part gives an overview of classic sensor fusion techniques, camera calibration, stereo vision and modern sensors such as camera, radar and Lidar. It also covers deep learning algorithms for image fusion, multi-source fusion and depth image prediction. Moreover, application of multi-senor fusion in autonomous driving, target recognition and remote sensing will be discussed."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Lectures, exercises, project (individual and team work), written exam","valueEn":"Lectures, exercises, project (individual and team work), written exam","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Lectures, independent programming assignments, group work","valueEn":"Lectures, independent programming assignments, group work","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Course Materials, book, web learning materials","valueEn":"Course Materials, book, web learning materials","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"Replaces the previous course TKO_2123 Special course in computer science","valueEn":"Replaces the previous course TKO_2123 Special course in computer science","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"-  Basic calculus, linear algebra, statistics,\r\n-  Basic knowledge of deep learning and machine learning,\r\n-  Experience with Python","valueEn":"-  Basic calculus, linear algebra, statistics,\r\n-  Basic knowledge of deep learning and machine learning,\r\n-  Experience with Python","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietojenkäsittelytieteet","valueEn":"Computer Science","valueSv":"Computer Science"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Fahimeh Farahnakian","valueEn":"Fahimeh Farahnakian","valueSv":"Fahimeh Farahnakian"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}