Object Detection Using FAST Corner Detector Based on Smartphone Platforms

Jeong, Kanghun; Moon, Hyeonjoon

doi:10.1109/cnsi.2011.60

Cited by 21 publications

(6 citation statements)

References 6 publications

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“…Corner detection is an important computer vision problem, with downstream applications in Simultaneous Localization and Mapping (SLAM), Structure from Motion, object recognition and tracking [30,40,14,37]. Event cameras [16,35,8] are sensors that capture visual information with high dynamic range and high temporal resolution, while maintaining power consumption and data rate acceptable for real time applications on embedded platforms [9].…”

Section: Introductionmentioning

confidence: 99%

Detecting Stable Keypoints from Events through Image Gradient Prediction

Chiberre

Pérot

Sironi

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Introductionmentioning

confidence: 99%

Detecting Stable Keypoints from Events through Image Gradient Prediction

Chiberre

Pérot

Sironi

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…Computer Vision relies a lot in feature extraction [2]. Feature extraction is the initial stage of object detection [3]. This technology works by taking features in the image.…”

Section: Introductionmentioning

confidence: 99%

Object Detection System to Help Navigating Visual Impairments

Rahmad¹,

Arai²,

Rawansyah³

et al. 2019

IJACSA

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The number of people with severe visual impairments and blind people in the world is 216.6 million and 38.5 million, respectively in 2018 and that number will increase every year. While the development of Computer Vision technology became popular after that method is used in automatic driving system using an object detection system to detect surrounding object, this technology can be a solution to help blind people too. This can be done by implementing Harris Corner Detection method. Harris Corner Detection method is used to detect the corner of the object in the image taken. The number of corner and location of corner in detection result can be used for predicting position and distance. To predict the distance, a triangle rule will be used in finding the distance. Furthermore, it can predict the location and distance of the object in the picture taken. From the results of the implementation above it was found that the accuracy of object detection using Harris Corner Detector's angle detection method is 88%. Therefore, this application can help detecting objects based on the number of corner and location detected using a Smartphone.

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“…There is also a large group of algorithms oriented at logo retrieval, which is a similar problem [15,16]. The newest ones include application of modern shape descriptors, for example, SIFT/SURF/FAST [17][18][19] or ART [20]. It should be, however, noted that such approaches aimed at logo detection can not be directly employed to stamp detection because of different properties of both objects' classes.…”

Section: Introductionmentioning

confidence: 99%

“…otherwise. (18) Minimal bounding figure coefficient is calculated as a ratio of object's actual area to the area of an ideal figure that is circumscribed on it. It is called minimal bounding figure and the method of calculation is similar to [35].…”

mentioning

confidence: 99%

Stamps Detection and Classification Using Simple Features Ensemble

Forczmański

Markiewicz

2015

Mathematical Problems in Engineering

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The paper addresses a problem of detection and classification of rubber stamp instances in scanned documents. A variety of methods from the field of image processing, pattern recognition, and some heuristic are utilized. Presented method works on typical stamps of different colors and shapes. For color images, color space transformation is applied in order to find potential color stamps. Monochrome stamps are detected through shape specific algorithms. Following feature extraction stage, identified candidates are subjected to classification task using a set of shape descriptors. Selected elementary properties form an ensemble of features which is rotation, scale, and translation invariant; hence this approach is document size and orientation independent. We perform two-tier classification in order to discriminate between stamps and no-stamps and then classify stamps in terms of their shape. The experiments carried out on a considerable set of real documents gathered from the Internet showed high potential of the proposed method.

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Object Detection Using FAST Corner Detector Based on Smartphone Platforms

Cited by 21 publications

References 6 publications

Detecting Stable Keypoints from Events through Image Gradient Prediction

Detecting Stable Keypoints from Events through Image Gradient Prediction

Object Detection System to Help Navigating Visual Impairments

Stamps Detection and Classification Using Simple Features Ensemble

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