Probabilistic collision estimation for tracked vehicles based on corner point self-activation approach

Sathiya, P.; Anandhakumar, P.

doi:10.1016/j.compeleceng.2018.07.029

Cited by 4 publications

(3 citation statements)

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“…The Harris feature point detection is sensitive to rotation, scale, and illumination [27]. SIFT feature extraction method has the advantages of good robustness, rotation transform, scale transform and illumination invariant.…”

Section: Surf Descriptionmentioning

confidence: 99%

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

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Extracting the fuzzy feature of the mobile video image can effectively improve the low illumination image quality. Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy. Therefore, this paper proposes a method for extracting fuzzy features of mobile terminal video images based on SURF-based virtual reality technology. First, perform video image grayscale extraction on the input mobile terminal video image, and detect the closed area in the mobile terminal video image as the radiation invariant area of the terminal video image. Secondly, Hessian matrix is used to detect the feature points of the image, and the non-maximum suppression method and interpolation operation are used to find and locate the extreme value points. Then, the main direction of feature points was determined, and SURF description operator was used for matching to obtain initial matching point pairs. Finally, the obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction. The experimental results show that the proposed algorithm is effective in feature extraction and matching, stability and speed. The misrecognition rate of the algorithm in this paper is 0.101, and the time used is only 0.41 s, which fully meets the real-time requirements.

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Section: Surf Descriptionmentioning

confidence: 99%

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

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“…To extract a corner point [29][30][31], this study made use of FAST which is the fast corner extraction algorithm proposed by Edward Rosten at University of Cambridge, the UK [32][33][34][35]. As guessed in its name, FAST is the method of extracting feature points with fast speed.…”

Section: Detection Of Real Character Regionsmentioning

confidence: 99%

Extraction of Character Regions through Machine Learning and Filtering

2019

IJRTE

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 Abstract: Characters in images are able to provide main information of the image. Therefore, it is important to analyze various kinds of image data and accurately extract the characters in images. This study proposes a new method of excluding background regions and accurately detecting character regions from input images with the uses of MCT features and Adaboost algorithm. The proposed method first extracts candidate character regions from input images with the uses of MCT features and Adaboost algorithm. It then excludes non-character regions and detects real character regions from the extracted candidate regions with the use of geometrical features. In the experiment of this study, the proposed method more robustly detected character regions from various input color images than a conventional method. For performance comparison, this study compared the method based on existing texture analysis and the proposed method. In this study, to qualitatively evaluate the performance of the proposed method of extracting license plate regions, the accuracy measure was defined. The measure is used to show the ratio of the accurately extracted character regions to all character regions of an image. The conventional method using the frequency factor-based texture information had many errors of character region detection, since it failed to execute binarization of background and character regions properly. On contrary, the proposed method made use of MCT features andAdaboost algorithm, effectively filtered candidate regions with the use of geometrical features, so that it detected character regions more accurately. The proposed character detection method is expected to be usefully applied to the fields of pattern recognition and image processing, such as store sign recognition and license plate recognition.

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“…Vehicle safety applications specifically require accurate positioning systems to improve vehicular navigation. This is the case of lane-level positioning and collision avoidance systems [7,8,9,10]. Nevertheless, the accuracy of GNSS receivers is often not enough.…”

Section: Introductionmentioning

confidence: 99%