scholarly journals A Note on Advantages of the Fuzzy Gabor Filter in Object and Text Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 678
Author(s):  
Vladimir Tadic ◽  
Tatjana Loncar-Turukalo ◽  
Akos Odry ◽  
Zeljen Trpovski ◽  
Attila Toth ◽  
...  
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Gabor Filter ◽  
Low Cost ◽  
Fuzzy Optimization ◽  
Detection Performance ◽  
Text Detection ◽  
License Plate ◽  
2D Gabor Filter ◽  
Fine Tune

This note presents a fuzzy optimization of Gabor filter-based object and text detection. The derivation of a 2D Gabor filter and the guidelines for the fuzzification of the filter parameters are described. The fuzzy Gabor filter proved to be a robust text an object detection method in low-quality input images as extensively evaluated in the problem of license plate localization. The extended set of examples confirmed that the fuzzy optimized Gabor filter with adequately fuzzified parameters detected the desired license plate texture components and highly improved the object detection when compared to the classic Gabor filter. The robustness of the proposed approach was further demonstrated on other images of various origin containing text and different textures, captured using low-cost or modest quality acquisition procedures. The possibility to fine tune the fuzzification procedure to better suit certain applications offers the potential to further boost detection performance.

Noise Detection Method of Iris by Using the 2D-Gabor Filter

2010 ◽  
Vol 39 (2) ◽  
pp. 369-374 ◽  
Author(s):  
苑玮琦 YUAN Wei-qi ◽  
冯琪 FENG Qi ◽  
白晓光 BAI Xiao-guang
Keyword(s):  
Detection Method ◽  
Gabor Filter ◽  
Noise Detection ◽  
2D Gabor Filter

scholarly journals APPLICABILITY EVALUATION OF OBJECT DETECTION METHOD TO SATELLITE AND AERIAL IMAGERIES

2016 ◽  
Vol XLI-B7 ◽  
pp. 229-234 ◽  
Author(s):  
K. Kamiya ◽  
T. Fuse ◽  
M. Takahashi
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Ground Truth ◽  
Traffic Monitoring ◽  
Detection Performance ◽  
Aerial Images ◽  
Training Dataset ◽  
Computational Time ◽  
Infrared Band ◽  
Applicability Evaluation

Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ <i>object detection</i> before <i>object recognition</i> can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING) method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

scholarly journals A LiDAR Sensor-Based Spray Boom Height Detection Method and the Corresponding Experimental Validation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2107
Author(s):  
Hanjie Dou ◽  
Songlin Wang ◽  
Changyuan Zhai ◽  
Liping Chen ◽  
Xiu Wang ◽  
...  
Keyword(s):  
Control System ◽  
Experimental Validation ◽  
Detection Method ◽  
Low Cost ◽  
Single Point ◽  
Detection Performance ◽  
Movement Speed ◽  
Detection Error ◽  
Detection Experiment ◽  
Wheat Stubble

Sprayer boom height (Hb) variations affect the deposition and distribution of droplets. An Hb control system is used to adjust Hb to maintain an optimum distance between the boom and the crop canopy, and an Hb detection sensor is a key component of the Hb control system. This study presents a new, low-cost light detection and ranging (LiDAR) sensor for Hb detection developed based on the principle of single-point ranging. To examine the detection performance of the LiDAR sensor, a step height detection experiment, a field ground detection experiment, and a wheat stubble (WS) height detection experiment as well as a comparison with an ultrasonic sensor were performed. The results showed that the LiDAR sensor could be used to detect Hb. When used to detect the WS height (HWS), the LiDAR sensor primarily detected the WS roots and the inside of the WS canopy. HWS and movement speed of the LiDAR sensor (VLiDAR) has a greater impact on the detection performance of the LiDAR sensor for the WS canopy than that for the WS roots. The detection error of the LiDAR sensor for the WS roots is less than 5.00%, and the detection error of the LiDAR sensor for the WS canopy is greater than 8.00%. The detection value from the LiDAR sensor to the WS root multiplied by 1.05 can be used as a reference basis for adjusting Hb, and after the WS canopy height is added to the basis, the value can be used as an index for adjusting Hb in WS field spraying. The results of this study will promote research on the boom height detection method and autonomous Hb control system.

scholarly journals APPLICABILITY EVALUATION OF OBJECT DETECTION METHOD TO SATELLITE AND AERIAL IMAGERIES

2016 ◽  
Vol XLI-B7 ◽  
pp. 229-234
Author(s):  
K. Kamiya ◽  
T. Fuse ◽  
M. Takahashi
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Ground Truth ◽  
Traffic Monitoring ◽  
Detection Performance ◽  
Aerial Images ◽  
Training Dataset ◽  
Computational Time ◽  
Infrared Band ◽  
Applicability Evaluation

Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ <i>object detection</i> before <i>object recognition</i> can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING) method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Alifia Puspaningrum ◽  
Nahya Nur ◽  
Ozzy Secio Riza ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Hierarchical Clustering ◽  
Gabor Filter ◽  
Hierarchical Cluster ◽  
Experimental Result ◽  
Main Process ◽  
Percentile Method ◽  
Cluster Analysis Method ◽  
2D Gabor Filter

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

scholarly journals Fuzzified Circular Gabor Filter for Circular and Near-Circular Object Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96706-96713 ◽  
Author(s):  
Vladimir Tadic ◽  
Akos Odry ◽  
Attila Toth ◽  
Zoltan Vizvari ◽  
Peter Odry
Keyword(s):  
Object Detection ◽  
Gabor Filter ◽  
Circular Object ◽  
Circular Gabor Filter
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