Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space

AbstractThis paper presented a comparative study of human skin color detection HSV and YCbCr color space. Skin color detection is the process of separation between skin and non-skin pixels. It is difficult to develop uniform method for the segmentation or detection of human skin detection because the color tone of human skin is drastically varied for people from one region to another. Literature survey shows that there is a variety of color space is applied for the skin color detection. RGB color space is not preferred for color based detection and color analysis because of mixing of color (chrominance) and intensity (luminance) information and its non uniform characteristics. Luminance and Hue based approaches discriminate color and intensity information even under uneven illumination conditions. Experimental result shows the efficiency of YCbCr color space for the segmentation and detection of skin color in color images

Statistical facial feature extraction and lip segmentation

Facial features such as lip corners, eye corners and nose tip are critical points in a human face. Robust extraction of such facial feature locations is an important problem which is used in a wide range of applications including audio-visual speech recognition, human-computer interaction, emotion recognition, fatigue detection and gesture recognition. In this thesis, we develop a probabilistic method for facial feature extraction. This technique is able to automatically learn location and texture information of facial features from a training set. Facial feature locations are extracted from face regions using joint distributions of locations and textures represented with mixtures of Gaussians. This formulation results in a maximum likelihood (ML) optimization problem which can be solved using either a gradient ascent or Newton type algorithm. Extracted lip corner locations are then used to initialize a lip segmentation algorithm to extract the lip contours. We develop a level-set based method that utilizes adaptive color distributions and shape priors for lip segmentation. More precisely, an implicit curve representation which learns the color information of lip and non-lip points from a training set is employed. The model can adapt itself to the image of interest using a coarse elliptical region. Extracted lip contour provides detailed information about the lip shape. Both methods are tested using different databases for facial feature extraction and lip segmentation. It is shown that the proposed methods achieve better results compared to conventional methods. Our facial feature extraction method outperforms the active appearance models in terms of pixel errors, while our lip segmentation method outperforms region based level-set curve evolutions in terms of precision and recall results

Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning

The success of deep learning in computer vision is rooted in the ability of deep networks to scale up model complexity as demanded by challenging visual tasks. As complexity is increased, so is the need for large amounts of labeled data to train the model. This is associated with a costly human annotation effort. To address this concern, with the long-term goal of leveraging the abundance of cheap unlabeled data, we explore methods of unsupervised "pre-training." In particular, we propose to use self-supervised automatic image colorization. We show that traditional methods for unsupervised learning, such as layer-wise clustering or autoencoders, remain inferior to supervised pre-training. In search for an alternative, we develop a fully automatic image colorization method. Our method sets a new state-of-the-art in revitalizing old black-and-white photography, without requiring human effort or expertise. Additionally, it gives us a method for self-supervised representation learning. In order for the model to appropriately re-color a grayscale object, it must first be able to identify it. This ability, learned entirely self-supervised, can be used to improve other visual tasks, such as classification and semantic segmentation. As a future direction for self-supervision, we investigate if multiple proxy tasks can be combined to improve generalization. This turns out to be a challenging open problem. We hope that our contributions to this endeavor will provide a foundation for future efforts in making self-supervision compete with supervised pre-training.Comment: Ph.D. thesi

Novel Approach for Detection and Removal of Moving Cast Shadows Based on RGB, HSV and YUV Color Spaces

Cast shadow affects computer vision tasks such as image segmentation, object detection and tracking since objects and shadows share the same visual motion characteristics. This unavoidable problem decreases video surveillance system performance. The basic idea of this paper is to exploit the evidence that shadows darken the surface which they are cast upon. For this reason, we propose a simple and accurate method for detection of moving cast shadows based on chromatic properties in RGB, HSV and YUV color spaces. The method requires no a priori assumptions regarding the scene or lighting source. Starting from a normalization step, we apply canny filter to detect the boundary between self-shadow and cast shadow. This treatment is devoted only for the first sequence. Then, we separate between background and moving objects using an improved version of Gaussian mixture model. In order to remove these unwanted shadows completely, we use three change estimators calculated according to the intensity ratio in HSV color space, chromaticity properties in RGB color space, and brightness ratio in YUV color space. Only pixels that satisfy threshold of the three estimators are labeled as shadow and will be removed. Experiments carried out on various video databases prove that the proposed system is robust and efficient and can precisely remove shadows for a wide class of environment and without any assumptions. Experimental results also show that our approach outperforms existing methods and can run in real-time systems

A new visual object tracking algorithm using Bayesian Kalman filter

This paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity. © IEEEpublished_or_final_versio