Robust recognition of hand gestures in real-world applications is still an unaccomplished goal due to many remaining challenges, such as cluttered backgrounds and unconstrained environmental factors. In most existing methods in this field, hand segmentation is a critical step to reduce redundant information in the scene background, in preparation for the hand gesture recognition stage. Therefore, we propose a new two-stage convolutional neural network (CNN) architecture, called HGR-Net, where the first stage performs accurate pixel-level semantic segmentation to determine the hand regions, and the second stage identifies the hand gesture. The segmentation stage architecture is based on the combination of fully convolutional residual network and atrous spatial pyramid pooling. Although the segmentation sub-network is trained without depth information, it is significantly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two-stream CNN which fuses the information from the RGB and segmented images by combining their deep representations in a new fully connected layer before classification. Extensive experiments on public hand gesture datasets show that our deep architecture achieves almost as good as state-of-the-art performance in segmentation and recognition of static hand gestures, at a fraction of training time, run time, and model size.
Machine learning techniques for skin segmentation, such as @cite_27 @cite_0 @cite_50 utilize a classifier trained on skin (hand) and non-skin (background) classes. In @cite_30 @cite_24 , Kawulok and co-workers extracted textural features from skin probability maps and then propogated them in a spatial analysis scheme. Hettiarachchi and Peters @cite_15 built their skin classifier training data set with skin and skin-like classes instead of skin and non-skin classes in order to obtain a more balanced class distribution in the training dataset. They segmented the skin candidate regions using a Voronoi-based image segmentation technique and then classified them using a multi-manifold-based skin classifier.
@cite_30 @cite_0 @cite_24 @cite_27 @cite_50 @cite_15
1990248972 2168572916 2737691244 2054279472
Problem statement: Skin color detection is used as a preliminary step in numerous computer vision applications like face detection, nudity recognition, hand gesture detection and person identification. In this study we present a pixel based skin color classification approach, for detecting skin pixels and non skin pixels in color images, using a novel neural network symmetric classifier. The neural classifiers used in the literature either uses a symmetric model with single neuron in the output layer or uses two separate neural networks (asymmetric model) for each of the skin and non-skin classes. The novelty of our approach is that it has two output layer neurons; one each for skin and non-skin class, instead of using two separate classifiers. Thus by using a single neural network classifier we have improved the separability between these two classes, eliminating additional time complexity that is needed in asymmetric classifier. Approach: Skin samples from web images of people from different ethnic groups were collected and used for training. Ground truth skin segmented images were obtained by using semiautomatic skin segmentation tool developed by the authors. The ground truth database of skin segmented images, thus obtained was used to evaluate the performance of our NN based classifier. Results: With proper selection of optimum classification threshold that varies from image to image the classifier gave the detection rate of more than 90 with 7 false positives on an average, Conclusion Recommendations: It is observed that the neural network is capable of detecting skin in complex lighting and background environments. The classifier has the ability to classify the skin pixels belonging to people from different ethnic groups even when they are present simultaneously in an image. The proper choice of optimum classification threshold that varies from image to image is an issue here. Automatic computation of this optimum threshold for each image is desired in practical skin detection applications. This issue can be taken up as a future study, which will enable us to perform fully automatic skin segmentation with reduced false positives. This paper introduces a new method for skin regions segmentation which consists in spatial analysis of skin probability maps obtained using pixel-wise detectors. There are a number of methods which use various techniques of skin color modeling to classify every individual pixel or transform input color images into skin probability maps, but their performance is limited due to high variance and low specificity of the skin color. Detection precision can be enhanced based on spatial analysis of skin pixels, however this direction has been little explored so far. Our contribution lies in using the distance transform for propagating the “skinness” across the image in a combined domain of luminance, hue and skin probability. In the paper we explain theoretical advantages of the proposed method over alternative skin detectors that also perform spatial analysis. Finally, we present results of an extensive experimental study which clearly indicate high competitiveness of the proposed method and its relevance to gesture recognition. In this paper, we study the problem of training large-scale face identification model with imbalanced training data. This problem naturally exists in many real scenarios including large-scale celebrity recognition, movie actor annotation, etc. Our solution contains two components. First, we build a face feature extraction model, and improve its performance, especially for the persons with very limited training samples, by introducing a regularizer to the cross entropy loss for the multi-nomial logistic regression (MLR) learning. This regularizer encourages the directions of the face features from the same class to be close to the direction of their corresponding classification weight vector in the logistic regression. Second, we build a multi-class classifier using MLR on top of the learned face feature extraction model. Since the standard MLR has poor generalization capability for the one-shot classes even if these classes have been oversampled, we propose a novel supervision signal called underrepresented-classes promotion loss, which aligns the norms of the weight vectors of the one-shot classes (a.k.a. underrepresented-classes) to those of the normal classes. In addition to the original cross entropy loss, this new loss term effectively promotes the underrepresented classes in the learned model and leads to a remarkable improvement in face recognition performance. We test our solution on the MS-Celeb-1M low-shot learning benchmark task. Our solution recognizes 94.89 of the test images at the precision of 99 for the one-shot classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017. This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695---702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.).