  This paper presents a framework designed for the multi-object detection
purposes and adjusted for the application of product search on the market
shelves. The framework uses a single feedback loop and a pattern resizing
mechanism to demonstrate the top effectiveness of the state-of-the-art local
features. A high detection rate with a low false detection chance can be
achieved with use of only one pattern per object and no manual parameters
adjustments. The method incorporates well known local features and a basic
matching process to create a reliable voting space. Further steps comprise of
metric transformations, graphical vote space representation, two-phase vote
aggregation process and a cascade of verifying filters.
