In previous work, a probabilistic image matching model for binary images was developed that predicts the number of mappings required to detect dissimilarity between any pair of binary images based on the amount of similarity between them. The model showed that dissimilarity can be detected quickly by randomly comparing corresponding points between two binary images. In this paper, we improve on this quickness for images that have dissimilarity concentrated near their centers. We apply smart mapping schemes to different image sets and analyze the results to show the effectiveness of this mapping scheme for images that have dissimilarity concentrated near their center. We compare three different smart mapping schemes with three different mapping densities to find the best mapping / best density performance.
A Probabilistic Matching Model for Binary Images (PMMBI) is presented that predicts the probability of matching
binary images with any level of similarity. The model relates the number of mappings, the amount of similarity between
the images and the detection confidence. We show the advantage of using a probabilistic approach to matching in
similarity space as opposed to a linear search in size space. With PMMBI a complete model is available to predict the
quick detection of dissimilar binary images. Furthermore, the similarity between the images can be measured to a good
degree if the images are highly similar. PMMBI shows that only a few pixels need to be compared to detect dissimilarity
between images, as low as two pixels in some cases. PMMBI is image size invariant; images of any size can be matched
at the same quick speed. Near-duplicate images can also be detected without much difficulty. We present tests on real
images that show the prediction accuracy of the model.
We present a quick method to detect dissimilar binary images. The method is based on a “probabilistic matching model” for image matching. The matching model is used to predict the probability of occurrence of distinct-dissimilar image pairs (completely different images) when matching one image to another. Based on this model, distinct-dissimilar images can be detected by matching only a few points between two images with high confidence, namely 11 points for a 99.9% successful detection rate. For image pairs that are dissimilar but not distinct-dissimilar, more points need to be mapped. The number of points required to attain a certain successful detection rate or confidence depends on the amount of similarity between the compared images. As this similarity increases, more points are required. For example, images that differ by 1% can be detected by mapping fewer than 70 points on average. More importantly, the model is image size invariant; so, images of any sizes will produce high confidence levels with a limited number of matched points. As a result, this method does not suffer from the image size handicap that impedes current methods. We report on extensive tests conducted on real images of different sizes.
Matching images is a fundamental problem in image processing. The most common technique used to compare binary images is to calculate the correlation between two images or simply to subtract them. Both of these methods –as well as other matching methods– require some type of similarity operation to be applied to the whole image, and hence they are image size dependent. This implies that as image size increases, more processing time is required. However, with image sizes already exceeding 20 mega-pixels and standard image sizes doubling approximately every five years, the need to find a size invariant image matching method is becoming crucial. In this paper, we present a quick way to compare and match binary images based on the <i>Probabilistic Matching Model </i>(PMM). We present two simple image size invariant methods based on <i>PMM</i>: one for fast detection of dissimilar binary images and another for matching binary images. For detecting dissimilar binary images we introduce the <i>Dissimilar Detection via Mapping method </i>(DDM). We compare <i>DDM</i> to other popular matching methods used in the image processing arena and show that <i>DDM</i> is magnitudes faster than any other method. For binary image matching, we use <i>DDM</i> as a preprocessor for other popular methods to speed up their matching speed. In particular, we use DDM with cross correlation to speed it up. Test results are presented for real images varying in size from 16 kilo-pixel images to 10 mega-pixel images to show the method’s size invariance.
Object identification by matching is a central problem in computer vision. A major issue that any object matching method must address is the ability to correctly match an object to its model when only a partial view of the object is visible due to occlusion or shadows (or any other reason). In this paper we introduce surface boundary signatures as an extension to our surface signature formulation. Boundary signatures are surface feature vectors that reflect the probability of occurrence of a surface boundary feature.