Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić, Vladimir; Király, Zoltán; Ódry, Péter; Trpovski, Željen; Lončar-Turukalo, Tatjana

doi:10.12700/aph.17.1.2020.1.4

Cited by 15 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Plane and obstacle detection and extraction are common problems in robot vision systems [19][20][21][22][23][24][25][26][27][28][29][30]. This action can be determining in certain applications where it is necessary to separate some objects from the background.…”

Section: Related Workmentioning

confidence: 99%

“…This ranging operation eliminates all the unnecessary depth image components that have no effect on the algorithm and the application itself. The ranging operation is followed by a binarization operation using a simple thresholding operation [18][19][20][21]. After the thresholding operation is applied, a sequence of morphological operations is performed to extract the wall area that should be painted.…”

Section: Wall Extraction Algorithmmentioning

confidence: 99%

“…First, it should be noted that the distance of the camera from the wall and the position of the camera's sensor play crucial roles in the formulation of the size, orientation, and shape of the structuring elements (SEs) [18][19][20][21][22]. Considering these facts, the specified SEs were formed.…”

Section: Wall Extraction Algorithmmentioning

confidence: 99%

“…7 (b). After the depth image is captured, the first operation is to find the Z value, i.e., the distance thresholding in the range of 930 mm<Z<1030 mm to separate the wall area with possible obstacles from the window [20]. As mentioned, the maximum spread in Z distance is 3 cm, or 30 mm since the depth camera records the Z values in mm.…”

Section: A Wall Extraction With Obstaclementioning

confidence: 99%

See 3 more Smart Citations

Application of the ZED Depth Sensor for Painting Robot Vision System Development

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper presents the use of the ZED depth sensor in a robot-based painting application. The use of a stereo depth sensor is a very important factor in robotic applications, since it is both the initial and the essential step in a sequence of robotic operations, where the goal is to detect and extract the useful surface and objects or the obstacle on a wall that is not intended for painting. The ZED depth sensor was used for surface recording and navigation of our painting robot. Later, wall extraction was performed using simple image processing and morphological operations in a surface extraction algorithm. The client's instruction was to use well-known, simple, and proven image processing operations in the algorithm to ensure both reliable and smooth operation of the robot's vision system in an industrial environment. The experiments showed that the developed algorithm detects and extracts the wall successfully under various depth measurement conditions.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Wall Extraction Algorithmmentioning

confidence: 99%

Section: Wall Extraction Algorithmmentioning

confidence: 99%

Section: A Wall Extraction With Obstaclementioning

confidence: 99%

See 2 more Smart Citations

Application of the ZED Depth Sensor for Painting Robot Vision System Development

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Gabor filter has also been used for license plate detection. Tadic et al [38] has presented the comparison of Gabor filter bank and fuzzified Gabor filter for license plate detection.…”

Section: Gabor Filter Bankmentioning

confidence: 99%

Efficient image segmentation method based on an adaptive selection of Gabor filters

2020

View full text Add to dashboard Cite

Image segmentation is one of the most important aspects of image processing that has many applications in image analysis, computer vision, and machine vision. So far, researchers have proposed many image segmentation algorithms that provide a relatively appropriate response for a particular application and over a specific type of input images. At the same time, they have not yet suggested a simple, fast, and efficient segmentation method that can apply to a wide range of natural and texture images. In this study, the authors present a new adaptive method for selecting Gabor filters using the image frequency content. Adaptive spectral features of the image are then extracted using only these selected filters from a Gabor filter bank. A pre-setting parameter Gabor filter is used to extract the gradient of each feature in x and y directions. Combining these features gradient, the texture gradient image is calculated, and then by selected markers for different texture regions and applying the marker-based watershed transform on texture gradient image, the authors present a new image segmentation method that has a less computational cost and yields better results compared to using all other filter banks. 12 IET Image Process.

show abstract

X-ray image analysis for osteoporosis diagnosis: From shallow to deep analysis

et al. 2023

View full text Add to dashboard Cite

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Cited by 15 publications

References 27 publications

Application of the ZED Depth Sensor for Painting Robot Vision System Development

Application of the ZED Depth Sensor for Painting Robot Vision System Development

Efficient image segmentation method based on an adaptive selection of Gabor filters

X-ray image analysis for osteoporosis diagnosis: From shallow to deep analysis

Contact Info

Product

Resources

About