Image processing system based on similarity/dissimilarity measures to classify binary images from contour-based features

Image Processing Systems (IPS) try to solve tasks like image classification or segmentation based on its content. Many authors proposed a variety of techniques to tackle the image classification task. Plenty of methods address the performance of the IPS [1], as long as the influence of many external circumstances, such as illumination, rotation, and noise [2]. However, there is an increasing interest in classifying shapes from binary images (BI). Shape Classification (SC) from BI considers a segmented image as a sample (backgroundsegmentation [3]) and aims to identify objects based in its shape..

Image processing system based on similarity/dissimilarity measures to classify binary images from contour-based features

Image Processing Systems (IPS) try to solve tasks like image classification or segmentation based on its content. Many authors proposed a variety of techniques to tackle the image classification task. Plenty of methods address the performance of the IPS [1], as long as the influence of many external circumstances, such as illumination, rotation, and noise [2]. However, there is an increasing interest in classifying shapes from binary images (BI). Shape Classification (SC) from BI considers a segmented image as a sample (backgroundsegmentation [3]) and aims to identify objects based in its shape..

Review of constraints on vision-based gesture recognition for human–computer interaction

The ability of computers to recognise hand gestures visually is essential for progress in human-computer interaction. Gesture recognition has applications ranging from sign language to medical assistance to virtual reality. However, gesture recognition is extremely challenging not only because of its diverse contexts, multiple interpretations, and spatio-temporal variations but also because of the complex non-rigid properties of the hand. This study surveys major constraints on vision-based gesture recognition occurring in detection and pre-processing, representation and feature extraction, and recognition. Current challenges are explored in detail

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

A Survey on Human Activity Analysis Techniques

Human Activity Recognition(HAR) is Popular research topic in Computer vision and Image Processing area. This Paper Provide an exhaustive survey on the Entire Process of identify or Recognize Human activity. Basically, There are Four steps are involved in HAR process, which are Pre-processing, Feature extraction, Training, and Classification of different activities from video. The need of data preprocessing , and segmentation based on camera movements are presented. This paper provide detailed survey on different features for HAR, feature extraction and selection method , and Classification methods with advantages and disadvantages. Finally, A brief discussion about various classification techniques are presented