Abstract: The disclosure relates to a method in which one or more local descriptors relating to an interest point of an image are received. A global descriptor is determined for the image on the basis of the one or more local descriptors; and the global descriptor is compressed. The disclosure also relates to an apparatus comprising a processor and a memory including computer program code, and storage medium having stored thereon a computer executable program code for use by an apparatus.

Abstract: Various methods are described for improving the compression of location information during a visual search. One example method may comprise identifying one or more features of a source query image for a visual search. The method may further comprise generating a histogram map comprising one or more histogram bins, wherein the histogram map represents the location of the one or more features of the source query image. Additionally, the method may comprise generating a context for at least one of the one or more histogram bins, wherein the context comprises a first context value based on information related to one or more neighboring bins located a first distance from the respective histogram bin and a second context value based on information related to one or more neighboring bins located a second distance from the respective histogram bin. Similar and related methods, apparatuses, and computer program products are also provided.

Abstract: A motion estimation method for determining a resultant motion vector. The motion estimation method includes: performing a first searching operation starting from an initial motion vector to determine an intermediate motion vector, wherein the first searching operation is performed according to a first searching principle; and performing a second searching operation starting from the intermediate motion vector, to determine the resultant motion vector, wherein the second searching operation is performed according to a second searching principle. The first searching operation is a rough search, while the second searching operation is a fine search.

Abstract: A digital image is converted to a multiple level image, and multiple scale sets are formed from connected components of the multiple level image such that different ones of the scale sets define different size spatial bins. For each of the multiple scale sets there is generated a count of connected components extracted from the respective scale set for each spatial bin; and adjacent spatial bins which represent connected components are linked. Then the connected components from the different scale sets are merged and text line detection is performed on the merged connected components. In one embodiment each of the scale sets is a histogram, and prior to linking all bins with less than a predetermined count are filtered out; and each histogram is extended such that counts of adjacent horizontal and vertical bins are added (single region bins are filtered out) and the linking is on the extended histograms.

Abstract: Methods and apparatuses are provided for facilitating detection of text within an image. A method may include calculating an alpha value associated with an image region containing a hypothesized text fragment. The alpha value may be defined as a function of a curved character length distribution, a character width distribution, and an inter-character spacing distribution for the hypothesized text fragment. The method may additionally include calculating a gamma value based at least in part on an interval length distribution determined for the hypothesized text fragment. The method may also include classifying whether the image region is a text-containing region based at least in part on the calculated alpha and gamma values. Corresponding apparatuses are also provided.

Abstract: Methods and apparatuses are provided for facilitating detection of text within an image. A method may include calculating an alpha value associated with an image region containing a hypothesized text fragment. The alpha value may be defined as a function of a curved character length distribution, a character width distribution, and an inter-character spacing distribution for the hypothesized text fragment. The method may additionally include calculating a gamma value based at least in part on an interval length distribution determined for the hypothesized text fragment. The method may also include classifying whether the image region is a text-containing region based at least in part on the calculated alpha and gamma values. Corresponding apparatuses are also provided.

Abstract: A digital image is converted to a multiple level image, and multiple scale sets are formed from connected components of the multiple level image such that different ones of the scale sets define different size spatial bins. For each of the multiple scale sets there is generated a count of connected components extracted from the respective scale set for each spatial bin; and adjacent spatial bins which represent connected components are linked. Then the connected components from the different scale sets are merged and text line detection is performed on the merged connected components. In one embodiment each of the scale sets is a histogram, and prior to linking all bins with less than a predetermined count are filtered out; and each histogram is extended such that counts of adjacent horizontal and vertical bins are added (single region bins are filtered out) and the linking is on the extended histograms.

Abstract: Methods, apparatuses, and computer program products are herein provided for providing a REVV system that is configured to provide an MVS that is operable on a mobile terminal. One example method may include causing a plurality of vector word residuals to be aggregated for at least one visual word using local feature descriptors extracted from an image. The method may further include causing the dimensionality of the aggregated at least one vector word residual for each visual word to be reduced by using a classification aware linear discriminant analysis. The method may further include computing, using a processor, a weighted correlation for at least one compact image signature that is binarized from the aggregated at least one vector word residual when compared to a list of candidates. The method may further include determining a ranked list of candidates based on the computed weighted correlation.

Abstract: A motion estimation method for determining a resultant motion vector. The motion estimation method includes: performing a first searching operation starting from an initial motion vector to determine an intermediate motion vector, wherein the first searching operation is performed according to a first searching principle; and performing a second searching operation starting from the intermediate motion vector, to determine the resultant motion vector, wherein the second searching operation is performed according to a second searching principle. The first searching operation is a rough search, while the second searching operation is a fine search.