Summer School on Image Processing 2021

Annual gathering of researchers and professionals dealing with image analysis and machine vision

University of Rijeka – Faculty of Engineering, Croatia

8–17 July, 2021

About SSIP 2021

• Learn about image processing• from basics to advanced topics

• both theory and applications

• Connect with other researchers

• Obtain 3 ECTS

• Have fun

About SSIP 2021

• Registered 48 students from 11 countries

• 17 teachers from 9 countries

• Meet the organizers

About SSIP 2021

• Lectures

• Project work

• Exam and project presentations

• Poster session

• Social events

• Other useful info

Lectures

• Friday – Thursday,

8:30 – 12:00

• Location P1&P2

• 1 h slots (50+10 min)

• Live: in person or online

• Q&A

Coffee breaks and lunch

• Location:• Cantina (ground floor)

• Coffee and snack machines (ground floor)

• Elsewhere

• 15 min coffee breaks

• 1 h lunch break

Coffee breaks and lunch – Cantina

• Work hours: 8:00 – 15:00

• Coffee to go• Nice terrace in the back• ≈ 6 HRK

• Lunch• Menu on the reception desk• Self-service• ≈ 40 HRK

• Cash / credit card

Project work

• Friday – Thursday,

13:00 – 17:00

• Location: informatics cabinets I1, I2, I3, I5, I6, I7, I8 – teams assigned, max 2 per room

• Rest as needed

• Teachers supervising your work

Project work

• Research – Code – Report• Webpage

• Presentation

• Important to finish in time!

• Project assignments, roles and other details later

Poster session

• Next Thursday,

11:00 – 12:00

• Location P1&P2, hall

• Bring your poster in the morning

• Designated space

• We provide the masking tape

Exam

• Next Friday, 8:30 – 9:30

• Location: P3, P4 (third floor)

• MCQ, paper (bring a pen)

• Score (%) recorded in your Certificate of Achievement (along with ECTS points awarded)

Project presentations

• Next Friday, 9:45 – 14:00

• Location: P3 (third floor)

• 20 min per project presentation (10+2+5 min)

• Awards for 1st, 2nd and 3rd

place

WiFi

• eduroam

• For those without an eduroam account:• SSID: ----

• password: ----

Coronavirus precautions

• At entry – measuring body temeperature, sanitising your hands

• During all time (inside) – wear mouth and nose covering mask

• Lectures• Only the seats with desk stickers allowed (preferably on the sides)• Daily seating position

• Project work• Stick with your team• Windows and door open

• If you feel ill, notify us immediately

Boat trip

• Sunday

9:00 – 18:00

• Lunch on board

• Dress accordingly

Boat trip

• Departure / arrival at Gat Karoline Riječke

• https://goo.gl/maps/dort4Ja7LSyx7bas6

• Any bus line heading downhill (towards city centre; e.g. 7, 7a, 6, ...)

• Don’t be late!

Farewell dinner

• Next Friday, 19:00 – 23:00

• https://g.page/PivnicaCont?share

• https://www.pivnicacont.com.hr/

Group photo

• Planned for Tuesday, 12:00

Vacancies @RITEH

• Postdoctoral position in big data medical image analysis (HRZZproject) – from 2022• Computer science, medical informatics• https://euraxess.ec.europa.eu/jobs/605400

• PhD positions in optimising peak load, energy efficiency and occupant comfort (IRI2 project) – one month from now!• Computer science, thermodynamics, physics

• PhD position in machine learning and digital signal processing • Non-stationary signal analysis, time-frequency analysis, image processing,

machine learning – from September!

• ...

Projects proposalSSIP 2021 Summer Scool on image processing,

Rijeka, Croatia

Goals of project work

• to establish new contacts with other members of community,

• to collaborate on project tasks,

• to exchange knowledge and experience.

Team roles

•Researcher

•Data analyst

•Programmer

•Report writer

•Presenter

•Team leader

Choose roles according to your interest and experience.

Share tasks so that the team is well balanced and achieves the best results.

Project assignment procedure

Topics are selected according to the preferences of the candidates

Teams are already formed

candidates from different countries and different universities/ companies

each candidate will work in 1 team.

team members will be announced immediately upon presenting the project task

Teams can already start with internal meetings, division of tasks and organization of work on the project

Selected projects

P2: Person detection in drone images

• Dataset: https://ieee-dataport.org/documents/search-and-rescue-image-dataset-person-detection-sard

• Input: Drone images (frame) of search andrescue opperations

• Output: Detection of the person in theimage (bounding box with confidence score)

• Requirements:

• Detection involves localization of the person (at bounding box level)

• Multiple persons may be present in the image and should all be detected.

• Evaluate detection performance in terms of average precision (AP)

P2: Person detection in drone images

Team members:

• A. K.

• N. C. R.

• D. J.

• J. T.

Location:

• I8 ground floor

P5: Counting Objects -Counting cars

• Datasets: http://cnrpark.it/

• Task: count all cars in the image, give every car a unique id (number)

• Input: a digital image or video streamsof cars in a parking lot

• Output: Detection of cars in the image(bounding box with confidence score)

Requirements:

Detection involves localization of the objects (at bounding box level) and classification

Evaluate detection performance in terms of average precision (AP)

P5: Counting Objects -Counting cars

Team members:

• A. Č.

• S. A.

• L. R.

• M. B.

Location:

• I8 ground floor

P6: Licence plate detectionand recognition

• Datasets: https://www.kaggle.com/andrewmvd/car-plate-detection

• Task: licence plate detection and recognition of plate numbers and signs

• Input: a digital image or video streams of car images

• Output: Detection of licence plate in the image(bounding box) and recognition of numbers on plate

Requirements:

Plate detection involves localization of the plate (at bounding box level) and recognition of plate numbers and signs (OCR)

P6: Licence plate detectionand recognition

Team members:

• M. N.

• T. K.

• D. Lj.

• N. L.

Location:

• I3 / I5 first floor

P7: Traffic signs detection and recognition

• Datasets: https://benchmark.ini.rub.de/gtsrb_news.html

• Task: traffic sign detection and recognition

• Input: a digital image or video streams of street images with traffic sign

• Output: Traffic signs detection in the image(bounding box) and recognition

P7: Traffic signs detection and recognition

Team members:

• E. J.

• O. – D.

• I. G.

• L. P.

Location:

• I3 / I5 first floor

P8: Age recognition

• Datasets: https://www.face-rec.org/databases/

• Task: recognizing the age of person based on the facial image (the age can be recognized within several classes: teenager, young, middle age, old)

• Input: a digital facial image

• Output: Age of person on image

P8: Age recognition

• Team members:

• M. D

• J. S.

• B. B.

• D. B.

• Location:

• I7 second floor

P9: Artistic Style transfer

• Task: Generate new images by artistic style transfer to existing image of real life.

• Input: real life image

• Output: generated images by artistic style transfer

P9: Artistic Style transfer

Team members:

• D. Š.

• J. Đ.

• J. F.

• I. V.

Location:

• I7 second floor

P10: Face expression recognition

• Datasets: https://www.kaggle.com/jonathanoheix/face-expression-recognition-dataset

• Task: recognizing the person facial expression within classes: angry, disgust, fear, happy, neutral, sad, surprise

• Input: a digital facial image

• Output: Facial expression mapped to one of expression classes: angry, disgust, fear, happy, neutral, sad, surprise

P10: Face expression recognition

Team members:

• L. P.

• S. B.

• M. O. O.

• I. Š.

Location:

• I6 second floor

P12: Butterfly recognitionand retrieval

• Task: Build a butterfly recognition framework which returns a list of potential matches ranked according to the similarity to a query image

• The problem involves several subtasks:- coarse localization of the butterfly,- segmentation (hint: symmetry, color, shape might help)- appearance representation and matching (hint: analyzing the dataset with respect to discriminative features might be necessary)- evaluation e.g. how recognition performance depends on the size of the training dataset, which species are similar to each other (confusion)

• Dataset: http://www.comp.leeds.ac.uk/scs6jwks/dataset/leedsbutterfly/

• Note: You can use additional butterfly data (beyond the provided link)

P12: Butterfly recognitionand retrieval

Team members:

• M. N.

• S. M.

• L. I.

• N. R.

Location:

• I6 second floor

P13: Highwaytrafficmonitoring

• Analyse video surveillance of the highways.

• The vehicles should be separated and tracked while they are visible.

• The team should consider the shadows, the different time of day as well as the different weather conditions.

• The team should also implement tracking abnormal vehicle movements, giving the possibility to detect accidents.

• Dataset: Input can be found on Youtube searching “highway traffic surveillance videos”.

P13: Highway traffic monitoring

Team members:

• I. J.

• A. S.

• K. H.

• M. K.

Location:

• I2 first floor

P14: Identify Pneumotorax Disease

• Develop a model to classify (and if present, segment) pneumothorax from a set of chest radiographic images

• Description and Data• https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/

P14: Identify Pneumotorax Disease

Team members:

• V. S.

• T. T.

• K. D.-H.

• A. F.

Location:

• I1 first floor

P18: Binary Tomography

• Calculate projections of binary images in few directions • (MATLAB, ImageJ, Python: Radon transform)

• Try to reconstruct the original image from the projections • can be solved by optimization

• Improve reconstruction quality by using prior knowledge• binary values,

• homogeneity,

• structural information (Discrete Tomography)

P18: Binary Tomography

Location:

• I2 first floor

Team members:

• M. D.

• R. W.

• I. P.

• I. M.

P20: Bone age estimation

• Develop a method for accurately determining skeletal age in a curated data set of pediatric hand radiographs.

• Input: Annotated X-ray datasetOutput: AgeDataset: https://www.kaggle.com/kmader/rsna-bone-age

P20: Bone age estimationTeam members:

• A. F.

• T. H.

• A. V.

• Lj. D.

Location:

• I1 first floor

Project implementation

• The deadline: Friday, 16.07.20201

• to do the research on a given topic and write a report

• Location: P3 (third floor)

• Presentation activities: max. 15+2 min• Presentation of project and experimental results – 10+2 min

• Questions and answering – 5 min

Project evaluation:

• Scientific originality, Innovation

• Use of resources

• Technical performance (programming quality)

• Demonstration and explanation of methods being used

• Evaluation of results using standard metrics

• Quality of documentation

• Presentation quality (presentation, web page)

• Ability to function as a team

Awards for 1st, 2nd and 3rd place

You’re Only as Good as Your Team!

Summer School on Image Processing 2021

Annual gathering of researchers and professionals dealing with image analysis and machine vision

University of Rijeka – Faculty of Engineering, Croatia

8–17 July, 2021