Niko's Project Corner

Computer Vision at other sites

Chess video search engine

(13th June 2021)

Youtube has a quite good search func­tion­al­ity based on video ti­tles, de­scrip­tions and maybe even sub­ti­tles but it doesn't go into ac­tual video con­tents and provide ac­cu­rate times­tamps for users' searches. An youtu­ber "Agad­ma­tor" has a very pop­ular chan­nel (1.1 mil­lion sub­scribers, 454 mil­lion video views at the time of writ­ing) which show­cases ma­jor chess games from past and re­cent tour­na­ments and on­line games. Here a search en­gine is in­tro­duced which an­alyzes the videos, rec­og­nizes chess pieces and builds a database of all of the po­si­tions on the board ready to be searched. It keeps track of the ex­act times­tamps of the videos in which the queried po­si­tion oc­curs so it is able to provide di­rect links to rel­evant videos.

Languages: Python Keras Clojure
Tags: Computer Vision Data Structures

Automatic map stitching

(10th September 2014)

Nowa­days there are many HTML5-based map ser­vices, but typ­ically they don't of­fer any ex­port func­tion­al­ity. To cre­ate a full view of the de­sired re­gion, one can ei­ther zoom out (and lose map de­tails) or take many screen­shots of dif­fer­ent lo­ca­tions and man­ually stitch them to­gether. This pro­ject can au­to­mat­ically load all stored screen­shots, de­tect the map, crop rel­evant re­gions, de­ter­mine im­ages rel­ative off­sets and gen­er­ate the high-res out­put with zero con­fig­ura­tion from any map ser­vice.

Languages: Matlab
Tags: Computer Vision Rendering

Image distortion estimation and compensation

(9th August 2014)

This pro­ject's goal was to au­to­mat­ically and ro­bustly es­ti­mate and com­pen­sate dis­tor­tion from any re­ceipt pho­tos. The user is able to just snap the photo and OCR could ac­cu­rately iden­tify bought prod­ucts and their prices. How­ever this task is some­what chal­leng­ing be­cause typ­ically re­ceipts tend to get crum­bled and bent. Thus they won't lie nicely flat on a sur­face for easy anal­ysis. This set of al­go­rithms solves that prob­lem and pro­duces dis­tor­tion-free thresh­olded im­ages for the next OCR step.

Languages: Matlab
Tags: Computer Vision

Real-time car tracking and counting

(7th June 2014)

From my of­fice win­dow I've got an un­blocked size-view to the Ring Road I (Kehä I) in Es­poo, Fin­land. It is one of the bus­iest roads in Fin­land, hav­ing up-to 100.000 cars / day. I wanted to cre­ate a pro­gram which would re­ceive a video feed from a we­bcam and would pro­cess im­ages in real time on com­mon hard­ware.

Languages: Matlab
Tags: Computer Vision

Real-time interest point tracking

(15th July 2013)

As men­tioned in an other ar­ti­cle about om­ni­di­rec­tional cam­eras, my Mas­ter's The­sis' main topic was real-time in­ter­est point ex­trac­tion and track­ing on an om­ni­di­rec­tional im­age in a chal­leng­ing forest en­vi­ron­ment. I found OpenCV's rou­ti­nes mostly rather slow and run­ning in a sin­gle thread, so I ended up im­ple­ment­ing ev­ery­thing my­self to gain more con­trol on the data flow and threads' de­pen­den­cies. The im­ple­mented code would si­mul­ta­ne­ously use 4 threads on CPU and a few hun­dred on the GPU, ex­ecut­ing in­ter­est point ex­trac­tion and match­ing at 27 fps (37 ms/frame) for 1800 × 360 pix­els (≈0.65 Mpix) panoramic im­age.

Languages: C++ FFTW CUDA
Tags: Computer Vision

Omnidirectional cameras

(6th July 2013)

My Mas­ters of Sci­ence The­sis in­volved the us­age of a so-called "om­ni­di­rec­tional cam­era". There are vari­ous ways of achiev­ing 180° or even 360° view, with their dis­tinct pros and cons. The gen­eral ben­efit of these al­ter­na­tive cam­era sys­tems is that ob­jects don't need to be tracked, be­cause gen­er­ally they stay with­ing the ex­tremely broad Field of View (FoV) of the cam­era. This is also very ben­efi­cial in vi­sual odom­etry tasks, be­cause land­marks can be tracked for longer pe­ri­ods of time.

Languages: Matlab C++
Tags: Computer Vision

Coin recognition algorithm

(26th June 2013)

I de­vel­oped a coin recog­ni­tion sys­tem which can rec­og­nize eight dif­fer­ent groups of coins. The used set is all five coins of Sin­ga­pore, but a few cat­egories can­not be dis­tin­guished from each other with­out knowl­edge of the coin's size in re­la­tion to oth­ers.

Languages: Matlab
Tags: Computer Vision Machine Learning

Fingerprint matching algorithm

(25th June 2013)

For my Bach­elor of Sci­ence de­gree I de­vel­oped a novel fin­ger­print match­ing al­go­rithm, which ended up beat­ing many al­ter­na­tive meth­ods which were de­vel­oped by re­search groups around the world. The used dataset the same which was used for FVC 2000 (Fin­ger­print Ver­ifi­ca­tion Com­pe­ti­tion).

Languages: Matlab C++ SDL
Tags: Computer Vision Data Structures