Color Skin Segmentation Based on Non-linear Distance Metrics

Sobieranski, Antonio Carlos; Chiarella, Vito F.; Barreto-Alexandre, Eduardo; Linhares, Rodrigo T. F.; Comunello, Eros; Wangenheim, Aldo von

doi:10.1007/978-3-319-12568-8_18

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…The Mahalanobis distance, in both its linear and higherorder polynomial variations, has been shown to produce better results than linear color-metric approaches such as RGB or CIELab, when employed as a customized color-metric in various segmentation algorithms [22,23,24,25,26,27,28].…”

Section: Mahalanobis Distancementioning

confidence: 99%

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Júnior

Monteiro

Ribeiro

et al. 2020

RITA

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In this paper, we present a comparison between convolutional neural networks and classicalcomputer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models

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Section: Mahalanobis Distancementioning

confidence: 99%

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Júnior

Monteiro

Ribeiro

et al. 2020

RITA

Self Cite

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“…We also use the contiguous uniform deviation algorithm which is based on values of curve difference in the same range when the test curve is compared to the reference curve [20]. This distance evaluation method is better compared to Mahalanobis distance method [30] because it does not need translation of curve position since it calculates the deviation of differences. Mahalanobis does not work if the curve position is translated to be overlapped each other to enable R 2 calculation.…”

Section: Combination Of Face and Posture Features For Tracking Of Movingmentioning

confidence: 99%

Combination of Face and Posture Features for Tracking of Moving Human Visual Characteristics

Andriana¹,

Prihatmanto²,

Hidayat³

et al. 2017

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Understanding images by recognizing its objects is still a challenging task. Tracking of moving human and recognition have been developed by researchers but not yet shows enough information needed for recognition. Initially, a tracking process of an object starts with detection and recognition of the object in a static pose and position, and then continues in movement in different poses. Available moving human recognition methods still has error in classification and need a huge amount of examples which may still be incomplete. Human face and body posture characteristics such as size of the eyes, nose, mouth, or fat or thin bodies, are important visual features in different poses for personal identification to increase accuracy of human recognition system, and it is still rare in researches. This paper attempts to describe visual features that best known for human, but hard to be recognized by machines. Curve fitting approaches to face and body posture features are also introduced to capture exact patterns of the features. Body postures are also preprocessed with a Kinect depth camera, and also compared to popular and recent methods of visual object recognition. Finally, we demonstrate our method can be useful for visual object classification. Probabilities of personal identification can be increased by using different poses and characteristics of smaller detail features through body postures and face areas. More detail features will richen comparison data samples for higher recognition accuracy.

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“…Santos y Pedrini propusieron otro método para la segmentación de piel en espacio de color RGB, utilizando histogramas de color y apoyándose en mapas de salientes [10]. Sobieranski et al [11] propuso la segmentación usando el mismo espacio de color, y clasificadores de distancia no lineal (Mahalanobis).…”

Section: Antecedentesunclassified

“…Modelos probabilísticos. Se tiene una base de datos de colores, y se generará un modelo utilizando probabilidades que permitan segmentar el objeto de su fondo[10,11], y 3. Algoritmos de clasificación.…”

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Estudio comparativo de algoritmos de segmentación de piel usando atributos de color

Gonzalez-Reyna¹,

López-Jiménez²,

Zavala-Mateo³

et al. 2016

RCS

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Resumen. El presente artículo analiza una serie de métodos para el procesamiento de imágenes de personas, con el fin de segmentar su piel. La base de datos Jochen Triesch Static Hand Posture Database [9] proporciona una amplia gama de tonalidades de piel, obtenida de personas de razas y edades distintas. Las tareas de detección de piel en imágenes se enfrentan a diversos retos, debido a factores diversos como: variaciones en la iluminación, factores ambientales y fondo de la escena. En este trabajo se muestran los resultados obtenidos al desarrollar algoritmos de segmentación basados en distintas técnicas heurísticas y probabilísticas, a partir de la utilización de una base de datos en el espacio de color RGB.

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Color Skin Segmentation Based on Non-linear Distance Metrics

Cited by 3 publications

References 7 publications

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Combination of Face and Posture Features for Tracking of Moving Human Visual Characteristics

Estudio comparativo de algoritmos de segmentación de piel usando atributos de color

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