R. S. Jadon

Bio: R. S. Jadon is an academic researcher from Madhav Institute of Technology and Science. The author has contributed to research in topics: Facial recognition system & Feature extraction. The author has an hindex of 11, co-authored 38 publications receiving 675 citations. Previous affiliations of R. S. Jadon include Indian Institutes of Technology & Massachusetts Institute of Technology.

A review of vision based hand gestures recognition

Abstract: With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.

Hand gesture recognition using neural networks

Abstract: Visual Interpretation of gestures can be useful in accomplishing natural Human Computer Interactions (HCI). In this paper we proposed a method for recognizing hand gestures. We have designed a system which can identify specific hand gestures and use them to convey information. At any time, a user can exhibit his/her hand doing a specific gesture in front of a web camera linked to a computer. Firstly, we captured the hand gesture of a user and stored it on disk. Then we read those videos captured one by one, converted them to binary images and created 3D Euclidian Space of binary values. We have used supervised feed-forward neural net based training and back propagation algorithm for classifying hand gestures into ten categories: hand pointing up, pointing down, pointing left, pointing right and pointing front and number of fingers user was showing. We could achieve up to 89% correct results on a typical test set.

Effectiveness of Eigenspaces for Facial Expressions Recognition

Abstract: 638 Abstract— Making Computer Systems to recognize and infer facial expressions from the user image is a challenging research topic. A method of facial expression recognition, based on Eigenspaces is presented in this paper. In our approach we identify the user’s facial expressions from the input images, using a method that was modified from eigenface recognition. We have evaluated our method in terms of recognition accuracy using two well known Facial Expressions databases, CohnKanade facial expression database and Japanese Female Facial Expression database. The experimental results show the effectiveness of our scheme.

A fuzzy theoretic approach for video segmentation using syntactic features

Abstract: This paper is concerned with the development of a fuzzy-logic-based framework for segmentation of video sequences. We have proposed a scheme for fuzzification of the frame-to-frame property difference values using the Rayleigh distribution. The difference values have been characterized by fuzzy terms like small, significant, large, etc. These terms have been used to design fuzzy rules for detecting abrupt changes and gradual changes. Fuzzy rules have provided a mechanism for integrating evidences based on different properties. The decompositional inference strategy has been used for fuzzy reasoning over the set of fuzzy rules. Gradual changes have been further classified as fade-in, fade-out and others (including dissolves, wipes, etc). Experimental results have shown that the proposed scheme can detect changes reliably.

Comparison between face recognition algorithm-eigenfaces, fisherfaces and elastic bunch graph matching

Abstract: The technology of face recognition has become mature within these few years System, using the face recognition, has become true in real life In this paper, we will have a comparative study of three most recently methods for face recognition One of the approach is eigenface, fisherfaces and other one is the elastic bunch graph matching After the implementation of the above three methods, we learn the advantages and Disadvantages of each approach and the difficulties for the implementation

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Robot vision

Abstract: A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

Vision based hand gesture recognition for human computer interaction: a survey

Abstract: As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.