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A novel iris segmentation technique based on active contour is proposed in this paper. Our approach uses innovative algorithms, including two important ones, pupil segmentation and iris circle calculation. With our algorithms, we are able to find the center position and radius of pupil correctly and segment the iris precisely. The accuracy of our proposed method for ICE dataset is around 92% and also reached high accuracy level of 79% for UBIRIS. Our results demonstrate that the proposed iris segmentation method can perform well with high accuracy and better efficacy for Iris segmentation in images. Through a relatively high-performance algorithm to further cut up the round out the picture of the pupil conversion cutting growth square picture in order to make the judgment for biometric applications.

In this research, we propose a state-of-the-art 3D finger gesture tracking and recognition method. We use the depth sensors for both hands in real time music playing. In line with the development of 3D depth cameras, we implemented a set of 3D gesture-based instruments, such as Virtual Cello and Virtual Piano, which need precise finger tracking in 3D space. For hands tracking, model-based tracking for left hand and appearance-based tracking for right hand techniques are proposed. To detect finger gestures, our approaches consist number of systematic steps as reducing noise in depth map and geometrical processing for Virtual Cello. For Virtual Piano, we introduce the Neural Network (NN) method to detect special hand gestures. It has Multilayer Perceptron (MLP) structure with back propagation training. Literature has few examples using touch screen as medium, with fixed-coordinates, and 2D–gestures to control MIDI input. The end users should no longer carry anything on their hands. We use Senz3D and Leap Motion due to a few technical benefits. Senz3D and Leap Motion use a closer distance to hands, thus detailed finger gestures can be precisely identified. In the past years, we announced a set of virtual musical instruments and the MINE Virtual Band. Our research work is tested on lab environment and professional theatrical stage. More information and demonstrations of the proposed method can be accessed at: http://video.minelab.tw/DETS/VMIB/ .

3D model construction techniques using RGB-D information have been gaining a great attention of the researchers around the world in recent decades. The RGB-D sensor, Microsoft Kinect is widely used in many research fields, such as in computer vision, computer graphics, and human computer interaction, due to its capacity of providing color and depth information. This paper presents our research finding on calibrating information from several Kinects in order to construct a 3D model of a human object and to render texture captured from RGB camera. We used multiple Kinect sensors, which are interconnected in a network. High bit rate streams captured at each Kinect are first sent to a centralized PC for the processing. This even can be extended to a remote PC in the Internet. Main contributions of this work include calibration of the multiple Kinects, properly aligning point clouds generated from multiple Kinects, and generation of the 3D shape of the human objects. Experimental results demonstrate that the proposed method provides a better 3D model of the human object being captured.

With the advancements of the human computer interaction field, nowadays it is possible for the users to use their body motions, such as swiping, pushing and moving, to interact with the content of computers or smart phones without traditional input devices like mouse and keyboard. With the introduction of gesture-based interface Kinect from Microsoft it creates promising opportunities for educators to offer students easier and intuitive ways to interact with the learning systems. The integration of Kinect based applications into classroom make students' learning experience very active and joyful. In this context, this paper proposes a system for assessment in a smart classroom environment. An interactive framework is designed using Microsoft Kinect sensor for virtual learning environment with new gesture-based questions supporting QTI-based assessment, and further a rich set of gesture commands are also introduced for practical usage in the classroom. Proposed system was experimented with teachers and students then collected feedback of the users using a usability questionnaire. The results show that the participants are satisfied with the system and it demonstrates that the proposed system is simple to use, provides better functionality and motivates student learning by assessment.

An object surface extraction from a set of point clouds extracted using a Kinect V2 sensor is presented here. First several point clouds are extracted from different angles around the object. Second point clouds are registered with each other using the Iterative Closest Point algorithm. If the angle between two camera positions is large, point clouds are manually rough aligned. Then iterative closest point algorithm provides a finer alignment. Next the object boundary is found enclosing all the points in the aligned and merged point cloud. After that a triangular mesh is created with the boundary points. Finally colors acquired from the original point cloud are applied on the triangular surface generated. For a better visual effect colors of the points are interpolated on the triangular mesh generated.

In this paper we propose a novel method to improve seam carving based on the method meta-heuristic algorithms combining simulated annealing (SA) and genetic algorithm (GA). SA is a single solution method which searches locally while GA belongs to population based algorithms that globally search to find the best answer. By this strategy, both speed and quality of the seam carving method can be increased simultaneously. First, SA is performed to find near optimum seams, which form initial population of GA. Then genetic algorithm develops this initial population to find optimum seam. Experimental results show that search for optimum seams by our proposed method successfully improves the retargeting results of seam carving.

Panorama photos are very useful when capturing large scenic backgrounds like famous constructions or eye catching landscapes. Users normally prefer capturing such backgrounds with them or friends as the foreground, but the moving people in such popular locations always obstruct the iconic structure in the background. In this paper a solution for automatic panorama generation is presented which is capable of removing moving objects in the background. First user captures a short video starting from the centre of the focus area and then following a clockwise circular path. The proposed application generates the panorama based on input video following the steps: segmentation of human object, removal of moving human objects, and generation of panorama. Experimental output panorama photos show that the proposed system is very usable and results are satisfactory.

Human Computer Interaction is becoming a major component in computer science related fields allowing humans to communicate with machines in very simple ways exploring new dimensions of research. Kinect, the 3D sensing device introduced by Microsoft mainly aiming computer games domain now is used in different scopes, one is being generation or controlling of sound signals producing aesthetic music. Here, in this paper, authors' experimental efforts on three virtual music instruments: Drum, Guitar and Spider King, based on Kinect sensor are presented. All three instruments virtually set the relevant sensing input areas, as an example, strings of the guitar or cymbals of the drum, then, the player controls the instrument through those virtual inputs through the Kinect. Sound control data is then generated and fed to the audio library based on the musically oriented human computer interaction gestures, composing a realtime musical expressive performance. A live performance using the presented virtual instruments was carried out at the end.