Rapid contour detection for image classification

Rasche, Christoph

doi:10.1049/iet-ipr.2017.1066

Cited by 27 publications

(19 citation statements)

References 29 publications

(36 reference statements)

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“…A simple rapid line-detection method that detects ridge and valley pixels by using EC, which is derived from relational operations, to compare the intensity and other properties of a center pixel with those of neighboring pixels has been proposed in [4]. Although the computational cost of this method is low, its performance is limited, especially when images are contaminated with noise, as will be demonstrated in the following sections.…”

Section: Related Workmentioning

confidence: 99%

“…For the given contrast k, described in (2), the intensity at each pixel was then calculated according to (4) as…”

Section: Generation Of Simulated Imagesmentioning

confidence: 99%

“…In some scientific literature, a line is referred to as a ridge or a valley [1][2][3]. As one of the most important and frequently observed features in various types of imagery, including indoor imagery, building facade imagery, satellite imagery, medical imagery, and road imagery, a line is used by perceptual systems to analyze and interpret a scene in an image [4]. Therefore, line detection is a fundamental operation in image processing and computer vision.…”

Section: Introductionmentioning

confidence: 99%

“…However, in this paper, for relatively large line widths, the SD-based line-detection method was shown to produce dis-localized line pixels and low SNR. Line detection based on extremity count (EC) was proposed in [4]. However, this method only partially fulfilled the requirements of high-quality line detection.…”

Section: Introductionmentioning

confidence: 99%

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Line-Detection Based on the Sum of Gradient Angle Differences

Seo

2019

Applied Sciences

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This paper presents a method to detect line pixels based on the sum of gradient angle differences (SGAD). The gradient angle differences are calculated by comparing the four pairs of gradients arising from eight neighboring pixels. In addition, a method to classify line pixels into ridges and valleys is proposed. Furthermore, a simple line model is defined for simulation experiments. Experiments are conducted with simulation images generated using the simple line model for three line-detection methods: second-derivatives (SD)-based method, extremity-count (EC)-based method, and proposed method. The results of the simulation experiments show that the proposed method produces more accurate line-detection results than the other methods in terms of the root mean square error when the line width is relatively large. In addition, the experiments conducted with natural images show that the SD-and EC-based methods suffer from bifurcation, fragmentation, and missing pixels. By contrast, for the original and the noise-contaminated versions of the natural images, the proposed SGAD-based line-detection method is affected by such problems to a considerably smaller extent than the other two methods.

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Section: Related Workmentioning

confidence: 99%

“…For the given contrast k, described in (2), the intensity at each pixel was then calculated according to (4) as…”

Section: Generation Of Simulated Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Line-Detection Based on the Sum of Gradient Angle Differences

Seo

2019

Applied Sciences

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“…The biggest difference between them is that pixels in the RGB image record the color information of the object, while pixels in the depth image record the distance between the object and the camera. Human pose recognition based on RGB images mainly utilizes the apparent features on the image, such as HOG (histogram of oriented gradient) features [7] and contour features [8]. However, these methods are usually affected by the external environment and are particularly vulnerable to the light, resulting in low detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Human Pose Recognition Based on Depth Image Multifeature Fusion

Wang

Zhou

et al. 2018

Complexity

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The recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and selfocclusion. To solve this problem, a feature extraction method combining directional gradient of depth feature (DGoD) and local difference of depth feature (LDoD) is proposed in this paper, which uses a novel strategy that incorporates eight neighborhood points around a pixel for mutual comparison to calculate the difference between the pixels. A new data set is then established to train the random forest classifier, and a random forest two-way voting mechanism is adopted to classify the pixels on different parts of the human body depth image. Finally, the gravity center of each part is calculated and a reasonable point is selected as the joint to extract human skeleton. The experimental results show that the robustness and accuracy are significantly improved, associated with a competitive operating efficiency by evaluating our approach with the proposed data set.

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Pose Estimation Using Machine Learning and Feature Extraction

Palanimeera

Ponmozhi

2023

Mathematics and Computer Science Volume 1

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Rapid contour detection for image classification

Cited by 27 publications

References 29 publications

Line-Detection Based on the Sum of Gradient Angle Differences

Line-Detection Based on the Sum of Gradient Angle Differences

Human Pose Recognition Based on Depth Image Multifeature Fusion

Pose Estimation Using Machine Learning and Feature Extraction

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