Nowadays, 360° video/image has been increasingly popular and drawn great attention. The spherical viewing range of 360° video/image accounts for huge data, which pose the challenges to 360° video/image processing in solving the bottleneck of storage, transmission, etc. Accordingly, the recent years have witnessed the explosive emergence of works on 360° video/image processing. In this article, we review the state-of-the-art works on 360° video/image processing from the aspects of perception, assessment and compression. First, this article reviews both datasets and visual attention modelling approaches for 360° video/image. Second, we survey the related works on both subjective and objective visual quality assessment (VQA) of 360° video/image. Third, we overview the compression approaches for 360° video/image, which either utilize the spherical characteristics or visual attention models. Finally, we summarize this overview article and outlook the future research trends on 360° video/image processing.

State-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression

Nowadays, 360° video/image has been increasingly popular and drawn great attention. The spherical viewing range of 360° video/image accounts for huge data, which pose the challenges to 360° video/image processing in solving the bottleneck of storage, transmission, etc. Accordingly, the recent years have witnessed the explosive emergence of works on 360° video/image processing. In this paper, we review the state-of-the-art works on 360° video/image processing from the aspects of perception, assessment and compression. First, this paper reviews both datasets and visual attention modelling approaches for 360° video/image. Second, we survey the related works on both subjective and objective visual quality assessment (VQA) of 360° video/image. Third, we overview the compression approaches for 360° video/image, which either utilize the spherical characteristics or visual attention models. Finally, we summarize this overview paper and outlook the future research trends on 360° video/image processing.

State-of-the-art in 360{\deg} Video/Image Processing: Perception, Assessment and Compression

Color is an efficient feature for object detection as it has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Skin color detection is an essential required step in various applications related to computer vision. The rapidly-growing research in human skin detection is based on the premise that information about individuals, intent, mode, and image contents can be extracted from colored images, and computers can then respond in an appropriate manner. Detecting human skin in complex images has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, aging, imaging conditions, and complex background. However, many methods have been developed to deal with skin detection problem in color images. The purpose of this study is to provide an up-to-date survey on skin color modeling and detection methods. We also discuss relevant issues such as color spaces, cost and risks, databases, testing, and benchmarking. After investigating these methods and identifying their strengths and limitations, we conclude with several implications for future direction.

A survey on skin detection in colored images

A good skin detector that is capable of capturing skin tones under different conditions is important for human–machine interaction applications. In a general situation, skin detectors, such as skin probability maps or Gaussian mixture models, achieve acceptable skin segmentation results. However, the false positive rate increases significantly when the skin tones are in shadow or when skin-like background objects are under similar illumination. In this paper, we propose a novel skin feature learning algorithm based on stacked autoencoders, which are deep neural networks. To overcome the problems encountered in skin segmentation that are caused by different ethnicities and varying illumination conditions, the stacked autoencoders are utilized to learn more discriminative representations of the skin area in both the RGB color space and the HSV color space. Unlike traditional machine learning methods, instead of predicting each pixel individually, our algorithm utilizes blocks to learn the representations and detect the skin areas. The algorithm exploits the learning ability of deep neural networks to learn high-level representations of skin tones. Experiments on test images show that the proposed algorithm achieves acceptable results on several publicly available data sets. To reduce the difficulty of detecting skin pixels in these data sets, the ground truths of these data sets are commonly focused on foreground skin area detection. Our skin detector is also able to detect background areas, as shown in our experiments.

A Skin Segmentation Algorithm Based on Stacked Autoencoders

Skin color segmentation has proven to be useful in various application, such as face detection, hand gesture analysis, image content filtering, etc. There are some challenges on skin-color detection, such as wide variety of illumination condition and skin-like color objects appear in the background of image. Therefore, a method is required to cope with illumination condition while detecting skin color. In our proposed method, we combine the chrominance channels of three color spaces, namely HSV, YCbCr and Normalized RGB, to produce better rate result for skin color segmentation. The result shows that the proposed method is able to detect skin pixel and get up to 91.05% correct result while being tested in ECU and HGR Dataset. This result is significantly higher than the other single color spaces.

Skin color segmentation using multi-color space threshold

Human face detection has become a major field of interest in current research because there is no deterministic algorithm to find face(s) in a given image. Further the algorithms that exist are very much specific to the kind of images they would take as input and detect faces. The problem is to detect faces in the given, colored group photograph. In this paper, an improved segmentation algorithm for face detection in color images with multiple faces and skin tone regions is proposed. Algorithm ingeniously uses a novel skin color model, RGB-HS-CbCr for the detection of human faces. Skin regions are extracted using a set of bounding rules based on the skin color distribution obtained from a training set. The segmented face regions are further classified using a parallel combination of simple morphological operations. Experimental results on a large photo data set have demonstrated that the proposed model is able to achieve good detection success rates for near-frontal faces of varying orientations, skin color and background environment.

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Face detection using skin tone segmentation

Estimation of Distribution algorithms (EDAs) are probabilistic-model based optimization techniques that exploit promising solution candidates by developing particles around them in accordance to a pre-specified distribution. This paper attempts to approach constrained optimization problems by an interdependent parallel functioning of a modified Gaussian distribution based EDA with differential mutation on the lines of rand/1 perturbation scheme. A modified penalty function free from scaling parameters has been proposed to deal with the constraints associated. The results have been collected from functional landscapes defined by the CEC 2010 benchmark and have been compared with existing state-of-the-art methods for constrained optimization.

Modified estimation of Distribution algorithm with differential mutation for constrained optimization

Implementation of salient region based secured digital image watermarking

Large scale real parameter optimization using genome grouping

In today’s world, the interest on multi-objective optimization through evolutionary algorithms (EAs) is growing day by day. However, most of the relative researches are confined within small scale with relatively fewer number of decision variables limited within 30, though real-world multi-objective optimization problems deal with, most of the times, with more than hundred decision variables. Also, optimization with fully separable decision variables along with non-separable decision variables leads to more optimal solutions than dealing with only any one of the both. In this paper, we have proposed an algorithm, which deals with medium- to large-scale multi-objective decision variables, compares the optimal solutions of separable and non-separable decision variables, and accepts the one having most optimized decision. Here we have adopted the test functions (large-scale multiobjective and many-objective optimization test problems for separable decision variables and Zitzler–Deb–Thiele test suit for non-separable decision variables) that are scalable with more than 100 decision variables and can range the results of both separable and non-separable decision variables.

Ankur Mondal

Papers

Face detection using skin tone segmentation

Modified estimation of Distribution algorithm with differential mutation for constrained optimization

Implementation of salient region based secured digital image watermarking

Large scale real parameter optimization using genome grouping

A Cooperative Co-evolutionary Approach for Multi-objective Optimization