International Journal of Pattern Recognition and Artificial Intelligence

A map can be a major advantage when exploring unfamiliar environments. With the proliferation of mobile devices, such as PDAs and even mobile phones, the tourist industry is currently exploring the potential of new presentation strategies that will maximize the promotional appeal of tourism in their region. Mobile devices are capable of guiding a tourist when he/she is exploring a city. These mobile devices offer the potential for providing relevant 3D information to enable tourists to locate themselves within the city, rapidly navigate around the unfamiliar environment and explore it interactively. However, the computational resources of current mobile technology prevents the display of full complex 3D content in real-time, and thus selective rendering techniques must be adopted to ensure the viewer is provided with the perceptually most important information at interactive rates.

This paper presents a series of experiments which help to identify key features of a scene for users to orientate themselves in that environment. Knowledge of these salient key features enable them to be provided to a user at a high quality while the remainder of the scene can be rendered in a much lower quality, saving significant bandwidth and computing power, without the user being perceptually aware of this difference in quality within the image.

This paper describes an efficient biometric writer identification system that can be used in a resource contained embedded environment. Writer identification (personal identification by general handwriting) is a relatively new area of handwriting research when compared to the handwriting recognition or signature verification areas. This work is aimed at exploring only small-scale handwriting samples, especially single handwritten words. A database of such samples has been collected dynamically using a digital writing tablet and a force sensitive pen. The dynamic approach adapted in this area is largely new, and only a few papers exist on the subject. Mainly dynamic features of handwriting connected with the writing process itself are considered, although some static features are also used. It is also shown how simple parameters can be used in embedded writer identification devices, and whether they can perform adequately in this type of application. A new feature selection algorithm based on likeness coefficients is proposed. The classifiers used are Minimum-Distance Classifier, Bayes Classifier, and finally their serial combination. The efficiency of this approach and its high performance together show the reasons for employing it in embedded biometric identification devices.

Accelerometer-based gesture recognition facilitates a complementary interaction modality for controlling mobile devices and home appliances. Using gestures for the task of home appliance control requires use of the same device and gestures by different persons, i.e. user independent gesture recognition. The practical application in small embedded low-resource devices also requires high computational performance. The user independent gesture recognition accuracy was evaluated with a set of eight gestures and seven users, with a total of 1120 gestures in the dataset. Twenty-state continuous HMM yielded an average of 96.9% user independent recognition accuracy, which was cross-validated by leaving one user in turn out of the training set. Continuous and discrete five-state HMM computational performances were compared with a reference test in a PC environment, indicating that discrete HMM is 20% faster. Computational performance of discrete five-state HMM was evaluated in an embedded hardware environment with a 104 MHz ARM-9 processor and Symbian OS. The average recognition time per gesture calculated from 1120 gesture repetitions was 8.3 ms. With this result, the computational performance difference between the compared methods is considered insignificant in terms of practical application. Continuous HMM is hence recommended as a preferred method due to its better suitability for a continuous-valued signal, and better recognition accuracy. The results suggest that, according to both evaluation criteria, HMM is feasible for practical user independent gesture control applications in mobile low-resource embedded environments.

This paper proposes a Content-Based Image Retrieval (CBIR) system applicable in mobile devices. Due to the fact that different queries to a content-based image retrieval (CBIR) system emphasize different subsets of a large collection of features, most CBIR systems using only a few features are therefore only suitable for retrieving certain types of images. In this research we combine a wide range of features, including edge information, texture energy, and the HSV color distributions, forming a feature space of up to 1053 dimensions, in which the system can search for features most desired by the user. Through a training process using the AdaBoost algorithm⁹ our system can efficiently search for important features in a large set of features, as indicated by the user, and effectively retrieve the images according to these features. The characteristics of the system meet the requirements of mobile devices for performing image retrieval. The experimental results show that the performance of the proposed system is sufficiently applicable for mobile devices to retrieve images from a huge database.

Digital watermarking technology would be very useful as part of a related service introduction system (RSIS); which links physical objects in the real world, such as printed photographs, to those in the cyber world. In this paper, we focus on a camera-equipped mobile phone as an RSIS terminal, and propose a fast watermark detection scheme for the captured images. The proposed scheme consists of two processes; to correct geometric distortion of the captured image, and to detect watermark information from the corrected image. For our scheme, we also propose a fast quadrangle detection algorithm and a robust watermarking algorithm. Moreover, we introduce a quantitative evaluation method for determining the reliability of watermark detection, which is essential for RSIS services. Finally, we show that the proposed scheme enables users to detect embedded information far less than one second, even when implemented as a Java application on a mobile phone with limited resources, and our experiments confirm the efficiency of our scheme.

With the growing popularity of personal digital assistant devices and smart phones, consumers have become increasingly enthusiastic to watching videos from these mobile devices. However, when browsing videos in mobiles, users often feel that the display resolution greatly affects their perceptual experience with the limited screen size. In this paper, an attention based spatial video adaptation scheme is proposed to overcome the display constraints by producing and displaying the region of interest. According to the size of the target display, we automatically detect and crop the informative region in each frame to generate a smooth sequence. To avoid costly full encoding operations, we develop a set of transcoding techniques based on the H.264 standard. Experimental results show that this approach not only improves the perceptual quality but also saves the bandwidth and computation, especially for the videos which have not been well edited.

In order to facilitate rapid development of context-aware applications, there is a need for architectural support in the entire context processing flow, and improved programming abstractions that ease the prototyping. In this paper, a toolkit called Semantic Space, is proposed to support rapid prototyping of context-aware applications via a set of programming abstractions on context processing. The functionality encapsulated in the toolkit handles the common, time-consuming and low-level details in context acquisition, aggregation, storage and inference. Architectural design and implementation issues of the Semantic Space toolkit are discussed in detail. Finally, a case study on building a mobile situation aware phone is described to illustrate the validity of our approach and usability of the toolkit.

Image mosaicing involves geometric alignment among video frames and image compositing or blending. For dynamic mosaicing, image mosaics are constructed dynamically along with incoming video frames. Consequently, dynamic mosaicing demands efficient operations for both alignment and blending in order to achieve real-time performance. In this paper, we focus on efficient image blending methods that create good-quality image mosaics from any number of overlapping frames. One of the driving forces for efficient image processing is the huge market of mobile devices such as cell phones, PDAs that have image sensors and processors. In particular, we show that it is possible to have efficient sequential implementations of blending methods that simultaneously involve all accumulated video frames. The choices of image blending include traditional averaging, overlapping and flexible ones that take into consideration temporal order of video frames and user control inputs. In addition, we show that artifacts due to mis-alignment, image intensity difference can be significantly reduced by efficiently applying weighting functions when blending video frames. These weighting functions are based on pixel locations in a frame, view perspective and temporal order of this frame. One interesting application of flexible blending is to visualize moving objects on a mosaiced stationary background. Finally, to correct for significant exposure difference in video frames, we propose a pyramid extension based on intensity matching of aligned images at the coarsest resolution. Our experiments with real image sequences demonstrate the advantages of the proposed methods.