The video applications on mobile communication devices have usually been designed for content creation, access,
and playback. For instance, many recent mobile devices replicate the functionalities of portable video cameras
and video recorders, and digital TV receivers. These are all demanding uses, but nothing new from the consumer
point of view. However, many of the current devices have two cameras built in, one for capturing high resolution
images, and the other for lower, typically VGA (640x480 pixels) resolution video telephony. We employ video to
enable new applications and describe four actual solutions implemented on mobile communication devices. The
first one is a real-time motion based user interface that can be used for browsing large images or documents such
as maps on small screens. The motion information is extracted from the image sequence captured by the camera.
The second solution is a real-time panorama builder, while the third one assembles document panoramas, both
from individual video frames. The fourth solution is a real-time face and eye detector. It provides another type
of foundation for motion based user interfaces as knowledge of presence and motion of a human faces in the view
of the camera can be a powerful application enabler.
A non-supervised clustering based method for classifying paper according to its quality is presented. The method is simple to train, requiring minimal human involvement. The approach is based on Self-Organizing Maps and texture features that discriminate the texture of effectively.
Multidimensional texture feature vectors are first extracted from paper images. The dimensionality of the data is then reduced by a Self-Organizing Map (SOM). In dimensionality reduction, the feature data are projected to a two-dimensional space and clustered according to their similarity. The clusters represent different paper qualities and can be labeled according to the quality information of the training samples. After that, it is easy to find the quality class of the inspected paper by checking where a sample is placed in the low-dimensional space.
Tests based on images taken in a laboratory environment from four different paper quality classes provided very promising results. Local Binary Pattern (LBP) texture features combined with a SOM-based approach classified the test data almost perfectly: the error percentage was only 0.2% with the multiresolution version of LBP and 1.6% with the regular LBP. The improvement to the previously used texture features in paper inspection is huge: the classification error is reduced over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view to the inspected data.
A key problem in using automatic visual surface inspection in industry is training and tuning the systems to perform in a desired manner. This may take from minutes up to a year after installation, and can be a major cost. Based on our experiences the training issues need to be taken into account from the very beginning of system design. In this presentation we consider approaches for visual surface inspection and system training. We advocate using a non-supervised learning based visual training method.
Dimensionality reduction methods for visualization map the original high-dimensional data typically into two dimensions. Mapping preserves the important information of the data, and in order to be useful, fulfils the needs of a human observer.
We have proposed a self-organizing map (SOM)- based approach for visual surface inspection. The method provides the advantages of unsupervised learning and an intuitive user interface that allows one to very easily set and tune the class boundaries based on observations made on visualization, for example, to adapt to changing conditions or material. There are, however, some problems with a SOM. It does not address the true distances between data, and it has a tendency to ignore rare samples in the training set at the expense of more accurate representation of common samples. In this paper, some alternative methods for a SOM are evaluated. These methods, PCA, MDS, LLE, ISOMAP, and GTM, are used to reduce dimensionality in order to visualize the data. Their principal differences are discussed and performances quantitatively evaluated in a few special classification cases, such as in wood inspection using centile features.
For the test material experimented with, SOM and GTM outperform the others when classification performance is considered. For data mining kinds of applications, ISOMAP and LLE appear to be more promising methods.
We have developed a self-organizing map (SOM) -based approach for training and classification in visual surface inspection applications. The approach combines the advantages of non-supervised and supervised training and offers an intuitive visual user interface. The training is less sensitive to human errors, since labeling of large amounts of individual training samples is not necessary. In the classification, the user interface allows on-line control of class boundaries. Earlier experiments show that our approach gives good results in wood inspection. In this paper, we evaluate its real time capability. When quite simple features are used, the bottleneck in real time inspection is the nearest SOM code vector search during the classification phase. In experiments, we compare acceleration techniques that are suitable for high dimensional nearest neighbor search typical for the method. We show that even simple acceleration techniques can improve the speed considerably, and the SOM approach can be used in real time with a standard PC.