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Research on Railway Track Edge Detection Based on BM3D and Zernike Moments

Wang Nan, Huo Tao, Zhang Tianming

Archives of Transport

2023

Vol. 68, iss. 4

7--20

With the rapid development of intelligent rail transportation, the realization of intelligent detection of railroad foreign body intrusion has become an important topic of current research. Accurate detection of rail edge location, and then delineate the danger area is the premise and basis for railroad track foreign object intrusion detection. The application of a single edge detection algorithm in the process of rail identification is likely to cause the problem of missing important edges and weak gradient change edges of railroad tracks. It will affect the subsequent detection of track foreign objects. A combined global and local edge detection method is proposed to detect the edges of railroad tracks. In the global pixel-level edge detection, an improved blok-matching and 3D filtering (BM3D) algorithm combined with bilateral filtering is used for denoising to eliminate the interference information in the complex environment. Then the gradient direction is added to the Canny operator, the computational template is increased to achieve non-extreme value suppression, and the Otsu thresholding segmentation algorithm is used for thresholding improvement. It can effectively suppress noise while preserving image details, and improve the accuracy and efficiency of detection at the pixel level. For local subpixel-level edge detection, the improved Zernike moment algorithm is used to extract the edges of the obtained pixel-level images and obtain the corresponding subpixel-level images. It can enhance the extraction of tiny feature edges, effectively reduce the computational effort and obtain the subpixel edges of the orbit images. The experimental results show that compared with other improved algorithms, the method proposed in this paper can effectively extract the track edges of the detected images with higher accuracy, better preserve the track edge features, reduce the appearance of pseudo-edges, and shorten the edge detection time with certain noise immunity, which provides a reliable basis for subsequent track detection and analysis.

Fine-tuned spot detection on ELISPOT images

Bieniecki W., Gonzalez V.M.M., Grabowski S.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

2009

T. 13, z. 3/1

781-791

Enzyme linked immunospot assay (ELISPOT) is a powerful technique used for detection and quantification of antigen specific immunological responses at the single cell level, which could have a prognostic value to diagnose the long-term graft outcome (i.e., kidney) and to evaluate the level of immunosuppression therapy needed. Basically, ELISPOT image contains round spots of different color, intensity and size. The image analysis should include accurate measurements of spot properties, like area and color. We propose a new approach to spot detection and measurement on ELISPOT microscope images, combining the Canny operator and a circle-fitting routine that we previously used (in somewhat different context) for that class of images. Good edge detection algorithms, like the Canny one, yield locally accurate spot contour arcs but their drawback is that the found arcs are not connected, i.e., do not constitute full contours. We solved the problem, fitting arcs belonging to the same spot contour. For each arc, a circle to each it belongs was approximated and then circle clus-tering was performed not to produce too many (false) contours.

ELISPOT (enzyme linked immunospot assay) jest metodą pozwalającą na ocenę natężenia odpowiedzi immunologicznej wobec określonych antygenów na poziomie pojedynczych komórek. Badanie tej odpowiedzi pozwala na monitorowanie ryzyka przewlekłego odrzutu przeszczepu (np. nerki) i zastosowania odpowiedniej terapii immunosupresyjnej. Obraz w badaniu ELISPOT zawiera okrągłe plamki różniące się zabarwieniem, wielkością i intensywnością. Celem analizy komputerowej obrazu jest precyzyjny pomiar parametrów plamek, takich jak pole powierzchni i kolor. W pracy zaproponowano nowy algorytm detekcji i określenia konturów plamek łączący operator Canny'ego z procedurą aproksymacji średniokwadratowej okręgiem konturu. Podobna metoda została przez nas sprawdzona w innym kontekście (wykrywanie całkowitego obszaru zainteresowania). Algorytmy detekcji krawędzi (m.in. Canny) dobrze lokalnie oddają fragmenty rozmytych konturów, ale zwykle zwracają łuki, a nie zamknięte krzywe. Rozwiązaliśmy ten problem, dopasowując do siebie łuki należące do tego samego konturu. Dla każdego łuku znajdowane było koło, a następnie w procesie grupowania usuwaliśmy fałszywe (nadmiarowe) kontury.