Abstract Current era is to make the interaction between humans and their artificial partners (Computers) and make communication easier and more reliable. One of the actual tasks is the use of vocal interaction. Speech recognition may be improved by visual information of human face. In literature, the lip shape and its movement are referred to as lip reading. Lip reading computing... Current era is to make the interaction between humans and their artificial partners (Computers) and make communication easier and more reliable. One of the actual tasks is the use of vocal interaction. Speech recognition may be improved by visual information of human face. In literature, the lip shape and its movement are referred to as lip reading. Lip reading computing plays a vital role in automatic speech recognition and is an important step towards accurate and robust speech recognition. In this paper we proposed method to detect the lips, shape and movement and use towards automatic lip tracking in detail by using Active Appearance Model (AAM). Purposed method shows the how accurate lip detection and tracking which is useful for speech recognition. Appearance-based methods consider the raw image for lip extraction. AAM uses for detection of speaker’s lips features and feature points from faces automatically by appearance parameters of a speaker’s lips. In this paper, we will describe the Lips shape, movement and appearance descriptions for lip detection and detailed information about the automatic lip tracking system. Individual characteristics and efficiency of both approaches towards the Lip reading is also discuss. The efficiency of purposed method will be evaluated individually by characteristics and their performances are then compared. The recognition process of common visual features (i.e. selection of lip, shape and extraction of movement) for an improved lip reading. Read More Read Less

Abstract The process of land cover change prediction is generally subjected to several types of imperfections which affect the reliability of decision about these changes. Several works in literature are carried out in an attempt to mitigate the issues caused by data related imperfections. Although new prediction models are created and improvement is done for the existing ones, both the imperfection... The process of land cover change prediction is generally subjected to several types of imperfections which affect the reliability of decision about these changes. Several works in literature are carried out in an attempt to mitigate the issues caused by data related imperfections. Although new prediction models are created and improvement is done for the existing ones, both the imperfection related to the input of models and its propagation through models are disregarded. To bridge this research gap, we propose a methodology that propagates imperfection throughout a model of land cover change prediction. The proposed approach incorporates three steps: 1) computing membership functions for input variables of the model of land cover change prediction, 2) applying a sensitivity analysis technique to determine which input variables are the most influential in the overall imperfection model, and 3) propagating distributions of the most influential input variables throughout the model of land cover change prediction. Experiments are made on images representing the Saint-Denis region, capital of Reunion Island. Results show the effectiveness of the proposed methodology in improving both computation time and prediction of the land cover change. Read More Read Less