Algorithm for face and eye detection using colour segmentation and invariant features

Petrisor, Daniel; Foşalău, Cristian; Avila, Manuel; Mariut, Felix

doi:10.1109/tsp.2011.6043666

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…In the very early years, many computer vision techniques were proposed for human detection and segmentation, such as background subtraction [3,12,13], colour detection [14][15][16], texture detection [5,17,18]. The background subtraction is to separate the static background and moving foreground.…”

Section: Related Workmentioning

confidence: 99%

Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

Yang

Yu²,

Kumar

et al. 2021

ELCVIA

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This paper introduces a method to use deep convolutional neural networks (CNNs) to automatically replace advertisement (AD) photo on social (or self-media) videos and provides the suitable evaluation method to compare different CNNs. An AD photo can replace a picture inside a video. However, if a human being occludes the replaced picture in the original video, the newly pasted AD photo will block the human occluded part. The deep learning algorithm is implemented to segment the human being from the video. The segmented human pixels are then pasted back to the occluded area, so that the AD photo replacement becomes natural and perfect appearance in the video. This process requires the predicted occlusion edge to be closed to the ground truth occlusion edge, so that the AD photo can be occluded naturally. Therefore, this research introduces a curve fitting method to measure the predicted occlusion edge’s error. By using this method, three CNN methods are applied and compared for the AD replacement. They are mask of regions convolutional neural network (Mask RCNN), a recurrent network for video object segmentation (ROVS) and DeeplabV3. The experimental results show the comparative segmentation accuracy of the different models and DeeplabV3 shows the best performance.

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Section: Related Workmentioning

confidence: 99%

Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

Yang

Yu²,

Kumar

et al. 2021

ELCVIA

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“…However, based on [56], HIS should be employed with histogram technique to produce acceptable results. Authors in [112] stated that HIS is the best color space and used it in an explicitly defined method. In a recent study [106], using of both explicitly defined models and single Gaussian distribution resulted that YPbPr outperforms other models.…”

Section: Color Space Comparisonmentioning

confidence: 99%

“…In [112,135,136,137], boundary models based on skin locus in other perceptual color spaces are proposed.…”

Section: A Explicitly Defined Boundary Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

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