IEEE Transactions on Pattern Analysis and Machine Intelligence

Nearly all super-resolution algorithms are based on the fundamental constraints that the super-resolution image should generate low resolution input images when appropriately warped and down-sampled to model the image formation process. (These reconstruction constraints are normally combined with some form of smoothness prior to regularize their solution.) We derive a sequence of analytical results which show that the reconstruction constraints provide less and less useful information as the magnification factor increases. We also validate these results empirically and show that, for large enough magnification factors, any smoothness prior leads to overly smooth results with very little high-frequency content. Next, we propose a super-resolution algorithm that uses a different kind of constraint in addition to the reconstruction constraints. The algorithm attempts to recognize local features in the low-resolution images and then enhances their resolution in an appropriate manner. We call such a super-resolution algorithm a hallucination or reconstruction algorithm. We tried our hallucination algorithm on two different data sets, frontal images of faces and printed Roman text. We obtained significantly better results than existing reconstruction-based algorithms, both qualitatively and in terms of RMS pixel error.

The density-based clustering algorithm is a fundamental data clustering technique with many real-world applications. However, when the database is frequently changed, how to effectively update clustering results rather than reclustering from scratch remains a challenging task. In this work, we introduce IncAnyDBC, a unique parallel incremental data clustering approach to deal with this problem. First, IncAnyDBC can process changes in bulks rather than batches like state-of-the-art methods for reducing update overheads. Second, it keeps an underlying cluster structure called the object node graph during the clustering process and uses it as a basis for incrementally updating clusters wrt. inserted or deleted objects in the database by propagating changes around affected nodes only. In additional, IncAnyDBC actively and iteratively examines the graph and chooses only a small set of most meaningful objects to produce exact clustering results of DBSCAN or to approximate results under arbitrary time constraints. This makes it more efficient than other existing methods. Third, by processing objects in blocks, IncAnyDBC can be efficiently parallelized on multicore CPUs, thus creating a work-efficient method. It runs much faster than existing techniques using one thread while still scaling well with multiple threads. Experiments are conducted on various large real datasets for demonstrating the performance of IncAnyDBC.

In the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges, and junctions may provide a 3D model of the scene but it will not provide information about the actual "scale" of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, object recognition, under unconstrained conditions, remains difficult and unreliable for current computational approaches. We propose a source of information for absolute depth estimation based on the whole scene structure that does not rely on specific objects. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene and, therefore, its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.