Our mobile application development expertise is derived from an inherent sense of what works and what doesn’t on the mobile platform. Working on the latest SDKs, our mobile application developers use their creativity, innovation and mobile apps development expertise to stitch together a seamless solution. With the boundaries of mobile application development and mobile apps development widening beyond imaginable proportions, our team of mobile application developers possesses the potential to develop apps for mobile apps development arenas like retail, finance, entertainment, productivity, utility, gaming, and many more.
mobile application development
We have organized a wonderful team of mobile apps consultants and mobile application developers who leverage the latest mobile operating systems for conceiving their apps. The finished products have earned us a reputation of developing highly engaging and interactive apps, while also taking into consideration the hardware specs of the mobile device. Our highly skilled and creative mobile application developers have been trained to understand your requirements, hear out your ideas, develop prototypes and work with you seamlessly to create a mobile app that matches your requirements to the T, and exceeds your expectations by many fathoms.
If you have an app idea that you think can make you the next big thing, give us a call today and avail of our mobile application development services. Our mobile application developers can help you develop a rich and functional mobile app, that is an actual ROI magnet.
Tiziran Software is a leading mobile application development company. We are proficient in conceptualizing, designing, developing and deploying apps on cutting-edge device platforms like iOS, Android and Windows Phone. Right from the advent of smartphones, we have been at the forefront of the mobile apps development services arena and have enhanced user experience and improved functionalities of mobile devices. With experience in custom mobile apps development for the latest versions of the iPhone, iPad and iPods, as well as Android and Windows Phone, we are primed to deliver according to the dynamic demands of the industry.
The augmented reality framework requirements
The combination of augmented reality, SLAM, robotic, smart phone can be create whole new experience called tiziran. By using it can be discover reality, but augmented with infinite possibilities. If you are an experienced programmer, then you will certainly be able to enjoy more advanced possibilities of augmented reality. The place where you are now has been mapped, and thanks to tiziran you might be able to see it. And that is not all: you might also see all the other cubes, entertainment and training. The visible of tiziran can be a lot, also stacked one on top of the other; it is also possible to visualize spaces that lie below the terrestrial surface, up to the level of a subway. How close to reality dreams must be to become real? What will you use it for? For leisure? Do you want it to express your emotions? It does not matter your level of programming knowledge. Simultaneous localization and mapping (SLAM), also known as concurrent mapping and localization (CML), is an important topic or robotics files. This method produces a real-time map of an environment and finds the current position of a robot on that map. Binarization or thresholding is one problem that must be solved in pattern recognition and it has a very important influence on the sequent steps in imaging applications. Thresholding is used to separate objects from the background, and diminish the amount of data alter the computational speed. Recently, interest in multilevel thresholding has been altered. However, when the levels are altered, the computation time alters so single threshold methods are accelerated than multilevel methods. Moreover, for every new application, new methods are is acquired. The video data is one of the most important and useful information in computer vision applications. Since vibration of the video occurs unavoidably when camera is moving, video stabilization is an important function for computer vision and its removes the unwanted motion from the camera. This image sequence enhancement is necessary to improve the performance of the subsequently complicated image processing algorithms. With consumer video cameras, video stabilization has been developed in several years. For video stabilization system, various techniques have been proposed to estimate motion vectors of view and the robust method use optical flow technique to obtain motion vectors . Multi-dimension robot vision in autonomous humanoid robot is still an open issue as it performs less effective when dealing with different environments. Robot vision becomes more challenging as image quality degrades. Unlike human vision, current robot vision is yet to calibrate automatically when image quality changes abruptly. This may result in poor accuracy due to false negative input data points, and the user needs recapturing new calibration images to compensate . Thresholding is a critical step in pattern recognition and has a significant effect on the subsequent steps in imaging applications. Thresholding is used to separate objects from the background, and decreases the amount of data and increases the computational speed. Recently, there has been an increased interest in multilevel thresholding. However, as the number of levels increases, the computation time increases. In addition, single threshold methods are faster than multilevel methods. Moreover, for each new application, new methods must be developed . Simultaneous localization and mapping (SLAM), also known as concurrent mapping and localization (CML), is an important topic or robotics files. This method produces a real-time map of an environment and finds the current position of a robot on that map. This method is generally used to solve the problem of “Where am I?” for localization, “Where do I go?” for goal determination, and “How do I go there?” for robot motion planning. Recently, the number of studies in this area has increased rapidly and expanded to different areas. In this paper analyzes SLAM or CML, which is currently a hot topic in the field of robotic research . 2D versus 3D Map for Environment Movement Object . Optical character recognition (OCR) is one of the most important fields in pattern recognition world which is able to recognize handwritten characters, irregular characters and machine printed characters. Optical character recognition system consists of five major tasks which are involved pre-processing, segmentation, feature extraction, classification and recognition. Generally, less discriminative features in global feature approach leads to reduce in recognition rate. By proposing a global approach that produces more discriminative features and less dimensionality of data, these problems are overcome. Two feature extraction methods are studied namely Gray Level Co-occurrence Matrix (GLCM) and edge direction matrix (EDMS) and combination of two popular feature extraction methods is proposed. The most important problem of EDMS is the number of produced features with this method which is just 18 features and is not enough for feature extraction purpose and it causes reducing the recognition rate . 2D versus 3D Map for Environment Movement Object . Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Handwritten recognition is one of the important issues, which have various applications in mobile devices. Peak signal noise ratio (PSNR) is one of the methods for measurement the quality of images . Among all the existing segmentation techniques, thresholding technique is one of the most popular one due to its simplicity, robustness and accuracy. Multi-thresholding is an important operation in many analyses which is used in many applications. Selecting correct thresholds to get better result is a critical issue. The maximum entropy thresholding algorithm selects several threshold values by maximizing the cross entropy between the original image and the segmented image. The method can effectively integrate partial range of the image histogram in the License plate recognition with multi-threshold based on entropy . Nowadays, PSNR has been widely used as stopping criteria in multi-level threshold method for segmenting images. Alternatively, we apply the PSNR as criteria to find the most suitable threshold value and it could relatively change according to environment such as when there is a high or low contrast situation . Due to the different types of license plates being used, the requirement of an automatic LPR is rather different for each country. For LPR application it is based on Multi-Layer Perceptron trained by back propagation. The adaptive threshold is introduced to find the optimum threshold values. The technique relies on the peak value from the graph of the number object versus specific range of threshold values. That approach has actually increased the overall performance compared to current optimal threshold techniques. Further improvement on this method is in progress to accommodate real time system specification . Evaluate the best classification techniques for Malaysia license plate recognition (LPR) system. Also discuss four image classification techniques that are used in contemporary LPR sys- tem worldwide. There are artificial immune recognition system, neural network, Bayesian network, support vector machine, and geometrical topological feature analysis on Malaysian character and number images as their inputs. Also explain character error analysis based on those image classification approaches. It shows that support vector machine outperforms compared to other classifiers . 2D versus 3D Map for Environment Movement Object .
 "2D versus 3D Map for Environment Movement Object" 2012.
 "Peak Signal-To-Noise Ratio Based on Threshold Method for Image Segmentation," 2013.  "Augmented optical flow methods for video stabilization," 2015
 "Camera calibration for multi-modal robot vision based on image quality assessment," 2015  "Adaptive Image Thresholding Based On the Peak Signal-To-Noise Ratio," 2014.
 "Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages," 2013.
 "Character and object recognition based on global feature extraction," 2013.
 "Comparison single thresholding method for handwritten images segmentation," 2011,
 "License plate recognition with multi-threshold based on entropy," 2011
 "Adaptive image segmentation based on peak signal-to-noise ratio for a license plate recognition system,"
 "Multi-threshold approach for license plate recognition system," 2010.
 "An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis," 2010.
Latest news and new apps
In this game you should remember the place of images. you only have 10 second to memorise the pictures place. Then you select pair of images and get point. The "number of miss match" shows number of pair images that you should find and the "number of Match" indicate that how many pair of images you find.
Description In this game you should remember the place of images. you only have 10 second to memorise the pictures place. Then you select pair of images and get point. The "number of miss match" shows number of pair images that you should find and the "number of Match" indicate that how many pair of images you find.
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