An interesting problem that has concerned forensic scientist for many years, is their need for accurate, reliable
and objective methods for performing fracture matching examinations. The aim of these fracture matching
methods is to determine if two broken object halves can be matched together, e.g., when one half is recovered
at a crime scene, while the other half is found in the possession of a suspect. In this paper we discuss the use
of a commercial white-light profilometer system for obtaining 2D/3D image surface scans of multiple fractured
objects. More specifically, we explain the use of this system for digitizing the fracture surface of multiple facing
halves of several snap-off blade knives. Next, we discuss the realization and evaluation of several image processing
methods for trying to match the obtained image scans corresponding to each of the broken off blade elements
used in our experiments. The algorithms that were tested and evaluated include: global template matching
based on image correlation and multiple template matching based on local image correlation, using so-called
"vote-map" computation. Although many avenues for further research still remain possible, we show that the
second method yields very good results for allowing automated searching and matching of the imaged fracture
surfaces for each of the examined blade elements.
Until recently, the forensic or investigative reconstruction of
shredded documents has always been dismissed as an important but
unsolvable problem. Manual reassembly of the physical remnants can always be considered, but for large amounts of shreds this problem can quickly become an intangible task that requires vast amounts of time and/or personnel. In this paper we propose and discuss several image processing techniques that can be used to enable the reconstruction of strip-shredded documents stored within a database of digital images. The technical content of this paper mainly revolves around the use of feature based matching and grouping methods for classifying the initial database of shreds, and the subsequent procedure for computing more accurate pairing results for the obtained classes of shreds. Additionally, we discuss the actual reassembly of the different shreds on top of a common image canvas.
We illustrate our algorithms with example matching and reconstruction
results obtained for a real shred database containing various types of shredded document pages. Finally, we briefly discuss the impact of our findings on secure document management strategies and the possibilities for applying the proposed techniques within the context of forensic investigation.
Traditional watershed and marker-based image segmentation algorithms are very sensitive to noise. The main reason for this is that these
segmentation algorithms are locally dependent on some type of edge indicator input image that is traditionally computed on a pixel-by-pixel basis. Additionally, as a result of raw watershed segmentation, the original image can be seriously oversegmented, and it may be difficult to reduce the oversegmentation and the impact of noise without also inducing several undesired region merges. This last problem is a typical result of local "edge gaps" that may appear along the topographic watershed mountain rims. Through these gaps the marker or watershed labels can easily leak into neighboring segments. We propose a novel pair of algorithms that uses "thick fluid" label propagation in order to try and solve these problems. The thick fluid technique is based on considering information from multiple adjacent pixels along the topographic watershed mountain rims that separate the different objects in an initial pre-segmented image.
The normalized cut algorithm is a graph partitioning algorithm that has previously been used successfully for image segmentation. It is originally applied to pixels by considering each pixel in the image as a node in the graph. In this paper we investigate the feasibility of applying the normalized cut algorithm to micro segments by considering each micro segment as a node in the graph. This will severely reduce the computational demand of the normalized cut algorithm, due to the reduction of the number of nodes in the graph. The foundation of the translation to micro segments will be the region adjacency graph. A floating point based rainfalling watershed algorithm will create the initial micro segmentation. We will first explain the rainfalling watershed algorithm. Then we will review the original normalized cut algorithm for image segmentation and describe the changes that are needed when we apply the normalized cut algorithm to micro segments. We investigate the noise robustness of the complete segmentation algorithm on an artificial image and show the results we obtained on photographic images. We also illustrate the computational demand reduction by comparing the running times.
The aim of this paper is to present a methodology to generate a partition of an image and a hierarchical region merging scheme to improve the meaningfulness of the segmentation, by reducing excessive object fragmentation. The segmentation method is based on the watershed transform applied to the image gradient magnitude. Prior to the actual segmentation, the image is smoothed to decrease the amount of detail detected by the watershed transform. To further improve the segmentation result, we use an iterative region merging process that uses a graph to represent the image partitions. In this process the most similar pair of adjacent regions is sequentially merged according to a predefined similarity metric. We investigate the use of a combined region merging criterion that takes into account both the intensity similarity and the contrast at the boundary of two adjacent regions. Results obtained illustrate the good combined performance of this segmentation and merging methods and the usefulness of the combined similarity function.
This paper investigates the use of computer vision techniques to aid in the semi-automatic reconstruction of torn or ripped-up documents.
First, we discuss a procedure for obtaining a digital database of a given set of paper fragments using a flatbed image scanner, a brightly coloured scanner background, and a region growing algorithm.
The contour of each segmented piece of paper is then traced around using a chain code algorithm and the contours are annotated by calculating a set of feature vectors. Next, the contours of the fragments are matched against each other using the annotated feature information and a string matching algorithm. Finally, the matching results are used to reposition the paper fragments so that a jigsaw
puzzle reconstruction of the document can be obtained. For each of the three major components, i.e., segmentation, matching, and global document reconstruction, we briefly discuss a set of prototype GUI
tools for guiding and presenting the obtained results. We discuss the performance and the reconstruction results that can be obtained, and show that the proposed framework can offer an interesting set of tools to forensic investigators.
In this paper we discuss a combination of several image processing and computer vision components for the purpose of semi-automatically delineating and tracking moving objects. First, we introduce our motion based segmentation framework which uses an improved watershed technique to obtain an image pre-segmentation, and an improved block or segment matching technique to obtain an initial estimation of the motion field. The initial pre-segmentation and motion estimation results are then fed into an additional component which reduces the typical watershed oversegmentation until only a few coherently moving objects remain. Next, we discuss two tools that can be used to improve or correct the obtained segmentation results. Also, we investigate a simple, yet efficient object oriented approach for tracking moving segments; we discuss the concept of truncated segment matching, which combines characteristics of both traditional block matching and feature based motion estimation processes. Additionally, we use polynomial motion models to describe and predict the observed motion. The proposed segment matching approach is shown to allow controllable and relatively fast computation, which is illustrated with image segmentation and video tracking results. Finally, we briefly discuss the use of these techniques within the domain of investigative and surveillance oriented applications.
In this paper we discuss a new implementation of a floating point based rainfalling watershed algorithm. First, we analyze and compare our proposed algorithm and its implementation with two implementations based on the well-known discrete Vincent- Soille flooding watershed algorithms. Next, we show that by carefully designing and optimizing our algorithm a memory (bandwidth) efficient and high speed implementation can be realized. We report on timing and memory usage results for different compiler settings, computer systems and algorithmic parameters. Our optimized implementation turns out to be significantly faster than the two Vincent-Soille based implementations with which we compare. Finally, we include some segmentation results to illustrate that visually acceptable and almost identical segmentation results can always be obtained for all algorithms being compared. And, we also explain how, in combination with other pre- or post- processing techniques, the problem of oversegmentation (a typical problem of all raw watershed algorithms) can be (partially) overcome. All these properties make that our proposed implementation is an excellent candidate for use in various practical applications where high speed performance and/or efficient memory usage is needed.
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