A Complete Probabilistic Model for the Quick Detection of Dissimilar Binary Images by Random Intensity Mapping (original) (raw)

An Image Size Invariant Method for Quick Detection of Dissimilar Binary Images

In this paper we present an image size invariant method for quick detection of dissimilar binary images. The method is based on a Probabilistic Matching Model (PMM) for binary image matching. Using the model, the probability of matching dissimilar image pairs can be predicted, as a function of the number of points mapped between two images and the amount of similarity between them. The model tells us that by matching few points between two images, we can determine dissimilar images with high confidence. For example, if images are distinct-dissimilar, i.e., completely different, only 8 points need to be mapped to arrive at a 90% successful detection rate, 11 points need to be mapped for a 99% confidence detection rate and only 15 points need to be mapped for a 99.9% confidence detection rate. If the images are not distinct-dissimilar and the images have some similarity between them, then more points need to be matched; depending on the amount of similarity between the images. The mod...

Fast Size-Invariant Binary Image Matching Through Dissimilarity Detection via Pixel Mapping

International Journal of Engineering Research and Technology, 2019

In this paper we present a fast size-invariant method for binary image matching. The method, called Dissimilar Detection via Mapping (DDM), is based on probabilistic matching models that can quickly detect dissimilarity between binary images regardless of image size. Dissimilarity detection is performed quickly by comparing only a few points (pixels) between the images. As a result, image matching can be performed fairly quickly. A complete detailed analysis of DDM and the mathematical proof of its superiority over other methods as images become big is given. We compare DDM to three of the most popular matching methods employed in the image processing arena: image correlation, sum of the absolute difference and mutual information. We show that DDM is magnitudes faster than these methods-if the images are not small. Furthermore, we show how DDM can be used as a pre-processor for other matching methods to speed up their matching speed. In particular, we use DDM with image correlation to enhance the latter's performance. Test results are presented for real images varying in size from 16 kilo-pixels to 10 mega-pixels to show the quickness of the method and its size-invariance.

Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping

World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2018

In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented. Keywords—Big images, binary images, similarity, matching.

Quick Probabilistic Binary Image Matching: Changing the Rules of the Game

Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 2016

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.

Quick Matching of Binary Images

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 Probabilistic Matching Model (PMM). We present two simple image size invariant methods based on PMM: one for fast detection of dissimilar binary images and another for matching binary images. For detecting dissimilar binary images we introduce the Dissimilar Detection via Mapping method (DDM). We compare DDM to other popular matching methods used in the image processing arena and show that DDM is magnitudes faster than any other method. For binary image matching, we use DDM 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.

Comparative Analysis of Dissimilarity Detection between Binary Images

International Conference on Signal, Image Processing and Applications, 2019

Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time consuming process when matching images to a database consisting of hundreds of images-if exact matching is required. It can be even more time consuming if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space by introducing a pre-matching stage, where dissimilar images are quickly removed. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather different. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a modified gamma binary similarity distance that does distinguish between an image and its inverse as being dissimilar.

Fast Binary Image Matching with Image Mappings

Given two binary images, how can we determine if the images are different? The most common technique used in the image processing arena is to calculate the correlation between the two images or simply to subtract the two images. Both of these methods require some type of operation to be applied to the whole image. This implies that as the image size increases, more time will be required. In this paper, we show that only a limited number of points need to be checked to arrive at a required confidence level that the images are the same or different. In fact, for completely different binary images only 8 points need to be checked to arrive at a 99% confidence level, 11 points need to be checked to arrive at a 99.9% confidence level and only 15 points need to be checked to arrive at a 99.99% confidence level that the images are the same or different. As a result, this method is magnitudes faster than traditional methods.

A Probabilistic Model for Random Binary Image Mapping

Many probabilistic models have been developed for numerous problems in robot and computer vision such as for image segmentation, road extraction, and object tracking. In this paper we present a probabilistic model for the random pixel mapping of binary images. The model predicts the probability of detecting dissimilarity between dissimilar binary images as a function of the number of random mappings and the amount of similarity. The model shows that detecting dissimilarity can be accomplished quickly by random pixel mapping, without the need to process the entire image. Test results on real images are presented that show the accuracy of the model.