Morphological Transformation in Color Space-Based Edge Detection of Skin Lesion Images

Sengupta, Sudhriti; Mittal, Neetu; Modi, Megha

doi:10.1007/978-981-16-4149-7_23

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…The main steps include computing the histogram, selecting thresholds, applying the thresholds to segment the image, and optionally performing post-processing for refinement (morphological operations). In order to classify skin pixels and provide robust parameters under different lighting situations, the color space transformation is intended to reduce the overlap between skin and non-skin pixels [44].…”

Section: Hand Detectionmentioning

confidence: 99%

“…The input RGB color image is first downsized to save processing time and then converted to a CbCr chrominance image (chrominance vector). In order to mitigate the effects of lighting variation, the system uses only the chrominance matrices to fully describe the color data, ignoring the Y matrix [44]. Building a decision process that can differentiate between pixels that contain skin and those that do not is the last objective of skin color detection.…”

Section: Hand Detectionmentioning

confidence: 99%

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Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

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Section: Hand Detectionmentioning

confidence: 99%

Section: Hand Detectionmentioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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“…In [16], Sengupta et al introduced the color space-based edge detection method (CSEDM), including a linear structural element. In the first phase, the image is transformed into red space.…”

Section: Introductionmentioning

confidence: 99%

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

Chaabane¹,

Bushnag²

2023

Computers, Materials &Amp; Continua

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A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism. The secondorder neighborhood shows the connection between the current pixel and the surroundings of this pixel. This connection is for each RGB component color of the input image. Once the image edges are detected for the three primary colors: red, green, and blue, these colors are merged using the combination rule. Then, the final decision is applied to obtain the segmentation. This process allows different data sources to be combined, which is essential to improve the image information quality and have an optimal image segmentation. Finally, the segmentation results of the proposed model are validated. Moreover, the classification accuracy of the tested data is assessed, and a comparison with other current models is conducted. The comparison results show that the proposed model outperforms the existing models in image segmentation.

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Morphological Transformation in Color Space-Based Edge Detection of Skin Lesion Images

Cited by 2 publications

References 18 publications

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

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