Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems

Efficient shadow map filtering

Schatten liefern dem menschlichen Auge wichtige Informationen, um die räumlichen Beziehungen in der Umgebung in der wir leben wahrzunehmen. Sie sind somit ein unverzichtbarer Bestandteil der realistischen Bildsynthese. Leider ist die Sichtbarkeitsberechnung ein rechenintensiver Prozess. Bildbasierte Methoden, wie zum Beispiel Shadow Maps, verhalten sich positiv gegenüber einer wachsenden Szenenkomplexität, produzieren aber Artefakte sowohl in der räumlichen, als auch in der temporalen Domäne, da sie nicht wie herkömmliche Bilder gefiltert werden können. Diese Dissertation präsentiert neue Echtzeit-Schattenverfahren die das effiziente Filtern von Shadow Maps ermöglichen, um die Bildqualität und das Kohärenzverhalten zu verbessern. Hierzu formulieren wir den Schattentest als eine Summe von Produkten, bei der die beiden Parameter der Schattenfunktion separiert werden. Shadow Maps werden dann in sogenannte Basis-Bilder transformiert, die im Gegensatz zu Shadow Maps linear gefiltert werden können. Die gefilterten Basis-Bilder sind äquivalent zu einem vorgefilterten Schattentest und werden verwendet, um geglättete Schattenkanten und realistische weiche Schatten zu berechnen.Shadows provide the human visual system with important cues to sense spatial relationships in the environment we live in. As such they are an indispensable part of realistic computerenerated imagery. Unfortunately, visibility determination is computationally expensive. Image-based simplifications to the problem such as Shadow Maps perform well with increased scene complexity but produce artifacts both in the spatial and temporal domain because they lack efficient filtering support. This dissertation presents novel real-time shadow algorithms to enable efficient filtering of Shadow Maps in order to increase the image quality and overall coherence characteristics. This is achieved by expressing the shadow test as a sum of products where the parameters of the shadow test are separated from each other. Ordinary Shadow Maps are then subject to a transformation into new so called basis-images which can, as opposed to Shadow Maps, be linearly filtered. The convolved basis images are equivalent to a pre-filtered shadow test and used to reconstruct anti-aliased as well as physically plausible all-frequency shadows