Wyniki wyszukiwania - BazTech

1

FPGA Implementation of Multi-scale Pedestrian Detection in Thermal Images

Kańka T., Kryjak T., Gorgon M.

Image Processing & Communications

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2016

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Vol. 21, no. 3

55--68

EN

In this paper an embedded vision system for human silhouette detection in thermal images is presented. As the computing platform a reprogrammable device (FPGA – Field Programmable Gate Array) is used. The detection algorithm is based on a sliding window approach, which content is compared with a probabilistic template. Moreover, detection is four scales in supported. On the used test database, the proposed method obtained 97% accuracy, with average one false detection per frame. Due to the used parallelization and pipelining real-time processing for 720 × 480 @ 50 fps and 1280 × 720 @ 50 fps video streams was achieved. The system has been practically verified in a test setup with a thermal camera.

2

Segmentation of Dishes for Customer Service Automation in a Self-service Canteen

Kryjak T., Krol D., Gorgon M.

Image Processing & Communications

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2015

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Vol. 20, no. 2

5--16

EN

The article describes research on dishes segmentation for the purposes of customer service process automation in a self-service canteen. The project assumptions and a prototype test stand are presented. The developed empty workspace detection and tray position determination algorithms are discussed. Finally, the chosen dishes segmentation algorithm is described and justified.

3

Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module

Kryjak T., Komorkiewicz M., Gorgon M.

International Journal of Electronics and Telecommunications

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2014

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Vol. 60, No. 1

61-72

EN

The article presents a hardware implementation of the foreground object detection algorithm PBAS (Pixel-Based Adaptive Segmenter) with a scene analysis module. A mechanism for static object detection is proposed, which is based on consecutive frame differencing. The method allows to distinguish stopped foreground objects (e.g. a car at the intersection, abandoned luggage) from false detections (so-called ghosts) using edge similarity. The improved algorithm was compared with the original version on popular test sequences from the changedetection.net dataset. The obtained results indicate that the proposed approach allows to improve the performance of the method for sequences with the stopped objects. The algorithm has been implemented and successfully verified on a hardware platform with Virtex 7 FPGA device. The PBAS segmentation, consecutive frame differencing, Sobel edge detection and advanced one-pass connected component analysis modules were designed. The system is capable of processing 50 frames with a resolution of 720×576 pixels per second.

4

Neural networks for medical image processing

Pieciak T., Jaworek J., Gorgon M.

Bio-Algorithms and Med-Systems

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2011

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Vol. 7, no. 4

101--110

EN

The proposed article presents the most common types of artificial neural networks used to be performed in the field of medical imaging. The first section describes the use of artificial neural networks in the preprocessing stage, restoration of noisy and distorted images and in conjunction with morphological operations. The second part presents the artificial neural networks in image segmentation problem, particularly in adaptive binarization threshold level selection and as a complement to the active contour method.