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Shadows appear in many scenes. Human can easily distinguish shadows from objects, but it is one of the challenges for shadow detection intelligent automated systems. Accurate shadow detection can be difficult due to the illumination variations of the background and similarity between appearance of the objects and the background. Color and edge information are two popular features that have been used to distinguish cast shadows from objects. However, this become a problem when the difference of color information between object, shadow and background is poor, the edge of the shadow area is not clear and the shadow detection method is supposed to use only color or edge information method. In this article a shadow detection method using both color and edge information is presented. In order to improve the accuracy of shadow detection using color information, a new formula is used in the denominator of original c 1 c 2 c 3 . In addition using the hue difference of foreground and background is proposed. Furthermore, edge information is applied separately and the results are combined using a Boolean operator.

Many fruit recognition systems today are designed to classify different type of fruits but there is no content-based fruit recognition system focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same color from green to yellowish brown with slightly different shape of thorns and it is hard to differentiate them with the current methods. Sometimes it is even hard for general consumers to differentiate durian species by themselves. This work aims to contribute to an automatic content-based durian species recognition that will be able to assist users in differentiating various species of durian. Few global contour-based and region-based shape descriptors such as area, perimeter, and circularity are computed as feature vectors and K-Nearest Neighbors algorithm is used to classify the durian based on the extracted features. 10-fold cross-validation is used to evaluate the proposed system. Experimental results have shown that the proposed feature extraction method for the durian species recognition system has successfully obtained a positive recognition rate of 100%.

Many fruit recognition approaches today are designed to classify different type of fruits but there is little effort being done for content-based fruit recognition specifically focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same colour from green to yellowish brown with just slightly different shape and pattern of thorns. Therefore, it is hard to differentiate them with the current methods. It would be valuable to have an automated content-based recognition framework that can automatically represent and recognise a durian species given a durian image as the input. Therefore, this work aims to contribute to a new representation method based on multiple features for effective durian recognition. Two features based on shape and texture is considered in this work. Simple shape signatures which include area, perimeter, and circularity are used to determine the shape of the fruit durian and its base while the texture of the fruit is constructed based on Local Binary Pattern. We extracted these features from 240 durian images and trained this proposed method using few classifiers. Based on 10-fold cross validation, it is found that Logistic Regression, Gaussian Naïve Bayesian, and Linear Discriminant Analysis classifiers performed equally well with 100% achievement of accuracy, precision, recall, and F1-score. We further tested the proposed algorithm on larger dataset which consisted of 42337 fruit images (64 various categories). Experimental results based on larger and more general dataset have shown that the proposed multiple features trained on Linear Discriminant Analysis classifier able to achieve 72.38% accuracy, 73% precision, 72% recall, and 72% F1-score.

Abstract-Computer facial animation is not a new endeavour as it had been introduced since 1970s. However, animating human face still presents interesting challenges because of its familiarity as the face is the part used to recognize individuals. Facial modelling and facial animation are important in developing realistic computer facial animation. Both modelling and animation is dependent to drive the animation. This paper reviews several geometric-based modelling (shape interpolation, parameterization and muscle-based animation) and data-driven animation (image-based techniques speech-driven techniques and performance-driven animation) techniques used in computer graphics and vision for facial animation. The main concepts and problems for each technique are highlighted in the paper.Index Terms-Computer graphics, data-driven animation, facial animation, geometric-based modelling. I. INTRODUCTIONComputer facial animation is experiencing a continuous and rapid growth since the pioneering work of Frederick I. Parke [1] in 1972. This is partly due to the increasing demand of virtual characters or avatars in the field of gaming, filming, human computer interaction and human machine communication.The human face is one of the channels in expressing the affective states. It has complex but flexible three-dimensional (3D) surfaces. To present the human face onto a computer system is a challenging task due to several goals to be achieved. According to Deng and Noh [2], the main goal in facial modelling and animation research is to develop a high adaptability system that creates realistic animation in real time mode where decreasing manual handling process. Here, high adaptability refers to the system that is easily adapted to any individuals" face.Various approaches have been proposed by the research community to improve the performance of facial animation in different aspects. Some claims that a good facial modelling and animation involving how to lip synchronization and how to use the eyes, brows and lids for expression [3]. Others believed that a facial model should include other attributes such as surface colours and textures [4].The focus of this paper is to present a review of the past and recent advancements in facial animation research. Besides, an overview of the facial animation framework is discussed to give a picture of where the modelling and animation techniques take place. The goal of this review is to improve the current computer facial animation system with the knowledge of the current advancement in this technology. II. A GENERAL FACIAL ANIMATION FRAMEWORKAnimating facial action is a tedious and complicated task. The construction of facial animation proceeds in several stages and is shown in Fig. 1. Each facial animation starts from the head model creation. The facial geometry is created from the input data either captured from images or motions. The input data are pre-processed and filtered, only the corresponding data is used to create the basis of morphable meshes. Next, geometric-based modelling is a...