In our work, we introduce the block significance measure reflecting
the property of the blocks to be shared by pixels from different image
regions. The blocks B with higher significance value C(B) belong to
the fields of region contrast and thus tend to attract more attention
in terms of the Human Visual System (HVS). The computation of C(B) is
based on the execution of a region merging procedure and determination
of the first partitioning containing a region completely covering
a block B. The introduced measure is related to the flat/texture/edge
block classification but reflects global image properties and differs
from the latter. We study how one may incorporate the measure in the
DCT-based based image/video compression. Experimental results are
Efficient search and retrieval of similar shapes in large databases stipulates two hardly compatible demands to the shape representations. On one hand, shape similarity conveys similarity of spatial relations of the shape parts. Thus, the representation should embed a kind of graph description of the shape, and allow estimation of the (inexact) correspondence between these descriptions. On the other hand, the representation should enable fast retrieval in large databases. Current shape indexing solutions do not comply well to these stipulations simultaneously. The G-graphs have been introduced as shape descriptors conveying structural and quantitative shape information. In the current work we define a representation of the G-graphs by strings consisting of the symbols from a four-letter alphabet such that two G-graphs are isomorphic as G-graphs if and only if their string representations are identical. This allows us to represent shapes by vectors consisting of strings and to introduce a shape representation satisfying both above demands. Experimental results are presented.
We study the problem of temporal partitioning a video sequence by blobs - the segments consisting of similar frames, and the choice of frames within the blobs well representing their content. In general, blobs give a finer subdivision of video then the shots. This problem is relevant for various applications, e.g., indexing and retrieval in video databases, or compression of video at very low bitrates. We present an efficient algorithm for the considered problem based on the use of a suitable frame distance measure reflecting similarity among the frames. Experimental results are presented.
Often objects which are not convex in the mathematical sense are treated as `perceptually convex'. We present an algorithm for recognition of the perceptual convexity of a 2D contour. We start by reducing the notion of `a contour is perceptually convex' to the notion of `a contour is Y-convex'. The latter reflects an absence of large concavities in the OY direction of an XOY frame. Then we represented a contour by a G-graph and modify the slowest descent-- the small leaf trimming procedure recently introduced for the estimation of shape similarity. We prove that executing the slowest descent dow to a G-graph consisting of 3 vertices allows us to detect large concavities in the OY direction. This allows us to recognize the perceptual convexity of an input contour.