Object Detection using Circular Hough Transform

Rizon, M.; Yazid, Haniza; Saad, Puteh; Yeon, Md Shakaff Ali; Rahman, Saad Abdul; Sugisaka, Masanori; Yaacob, Sazali; Rozailan, M.; Karthigayan, M.

doi:10.3844/ajassp.2005.1606.1609

Cited by 122 publications

(52 citation statements)

References 8 publications

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“…The standard Hough transform is less sensitive to noise, shape disconnectedness, and occlusions, but has the disadvantage of massive requirements of computation and memory [21]. Some improved Hough transform-based methods have overcome the standard Hough transform's deficiencies by combining edge orientation with it [62][63][64].…”

Section: Circular Hough Transform (Cht)mentioning

confidence: 99%

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

et al. 2014

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Qanats in northern Xinjiang of China provide valuable information for agriculturists and anthropologists who seek fundamental understanding of the distribution of qanat water supply systems with regard to water resource utilization, the development of oasis agriculture, and eventually climate change. Only the tops of qanat shafts (TQSs), indicating the course of the qanats, can be observed from space, and their circular archaeological traces can also be seen in very high resolution imagery in Google Earth. The small size of the TQSs, vast search regions, and degraded features make manually extracting them from remote sensing images difficult and costly. This paper proposes an automated TQS extraction method that adopts mathematical morphological processing methods before an edge detecting module is used in the circular Hough transform approach. The accuracy OPEN ACCESSRemote Sens. 2014, 6 11957 assessment criteria for the proposed method include: (i) extraction percentage (E) = 95.9%, branch factor (B) = 0 and quality percentage (Q) = 95.9% in Site 1; and (ii) extraction percentage (E) = 83.4%, branch factor (B) = 0.058 and quality percentage (Q) = 79.5% in Site 2. Compared with the standard circular Hough transform, the quality percentages (Q) of our proposed method were improved to 95.9% and 79.5% from 86.3% and 65.8% in test sites 1 and 2, respectively. The results demonstrate that wide-area discovery and mapping can be performed much more effectively based on our proposed method.

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Section: Circular Hough Transform (Cht)mentioning

confidence: 99%

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

et al. 2014

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“…The Hough Transform was used to detect the eye sockets. It is used in this kind of tasks, but most often in visible light [16,28,30,34]. The biggest disadvantages of this method, that is, problems with the selection of ellipse sizes and with their analysis in the case of rotation, were here eliminated.…”

Section: Detection Of the Eye Socket Areasmentioning

confidence: 99%

Automatic method for detection of characteristic areas in thermal face images

Marzec

Koprowski

Wróbel

et al. 2013

Multimed Tools Appl

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The use of thermal images of a selected area of the head in screening systems, which perform fast and accurate analysis of the temperature distribution of individual areas, requires the use of profiled image analysis methods. There exist methods for automated face analysis which are used at airports or train stations and are designed to detect people with fever. However, they do not enable automatic separation of specific areas of the face. This paper presents an algorithm for image analysis which enables localization of characteristic areas of the face in thermograms. The algorithm is resistant to subjects' variability and also to changes in the position and orientation of the head. In addition, an attempt was made to eliminate the impact of background and interference caused by hair and hairline. The algorithm automatically adjusts its operation parameters to suit the prevailing room conditions. Compared to previous studies (Marzec et al., J Med Inform Tech 16:151-159, 2010), the set of thermal images was expanded by 34 images. As a result, the research material was a total of 125 patients' thermograms performed in the Department of Pediatrics and Child and Adolescent Neurology in Katowice, Poland. The images were taken interchangeably with several thermal cameras: AGEMA 590 PAL (sensitivity of 0.1°C), ThermaCam S65 (sensitivity of 0.08°C), A310 (sensitivity of 0.05°C), T335 (sensitivity of 0.05°C) with a 320×240 pixel optical resolution of detectors, maintaining the principles related to taking thermal images for medical thermography. In comparison to (Marzec et al., J Med Inform Tech 16:151-159, 2010), the approach presented there has been extended and modified. Based on the comparison with other methods presented in the literature, it was demonstrated that this method is more complex as it enables to determine the approximate areas of selected parts of Multimed Tools Appl (2015)

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“…2.a). Rizon [16] proposed a method to detect the object (e.g., a human body) in the sequence of image stream. Fig.…”

Section: Ir Image Analysis and Signal Extractionmentioning

confidence: 99%

New motherwavelet for pattern detection in IR image

Mirzaei

Prianto

Chardonnet

et al. 2013

2013 Visual Communications and Image Processing (VCIP)

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. The maximum correlation between the pattern and the signal of interest will be used as a criterion to define the mother wavelet.The proposed mother wavelet were tested and verified under the scale variation and the presence of noise. The experimental tests and performance analysis show promising results for both scale variation and noisy signal. 90% accuracy for the proposed wavelet under intensive noisy condition (50% of the signal amplitude) is guaranteed and high precision is expected under real condition.

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Object Detection using Circular Hough Transform

Cited by 122 publications

References 8 publications

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

Automatic method for detection of characteristic areas in thermal face images

New motherwavelet for pattern detection in IR image

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