Johan De Bock

Measuring the wicking behavior of textiles by the combination of a horizontal wicking experiment and image processing

A horizontal wicking experiment is proposed to measure the wicking behavior of textiles. A syringe supplying a continuous flow of distilled water is in contact with the absorbing fabric resulting in a wicking region. The increase in wicking area or the wicking area after a certain time is recorded with a digital camera. The picture analyzing process is automated by the use of two complementary image segmentation algorithms: morphological segmentation and region merging. Where commercial image analyzing software fails due to the specific porous structure of textiles, the developed algorithms succeed in calculating the wicking area semiautomatically. It is shown that the newly developed technique is able to test the wicking behavior of, e.g., a cotton fabric and a plasma treated polyester nonwoven.

A fast sequential rainfalling watershed segmentation algorithm

In this paper we present a new implementation of a rainfalling watershed segmentation algorithm. Our previous algorithm was a one-run algorithm. All the steps needed to compute a complete watershed segmentation were done in one run over the input data. In our new algorithm we tried another approach. We separated the watershed algorithm in several low-complexity relabeling steps that can be performed sequentially on a label image. The new implementation is approximately two times faster for parameters that produce visually good segmentations. The new algorithm also handles plateaus in a better way. First we describe the general layout of a rainfalling watershed algorithm. Then we explain the implementations of the two algorithms. Finally we give a detailed report on the timings of the two algorithms for different parameters.

Image segmentation using Watersheds and Normalized Cuts

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.

Semiautomatic reconstruction of strip-shredded documents

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.

Fast and Memory Efficient 2-D Connected Components Using Linked Lists of Line Segments

In this paper we present a more efficient approach to the problem of finding the connected components in binary images. In conventional connected components algorithms, the main data structure to compute and store the connected components is the region label image. We replace the region label image with a singly-linked list of line segments (or runs) for each region. This enables us to design a very fast and memory efficient connected components algorithm. Most conventional algorithms require (at least) two raster scans. Those that only need one raster scan, require irregular and unbounded image access. The proposed algorithm is a single pass regular access algorithm and only requires access to the three most recently processed image lines at any given time. Experimental results demonstrate that our algorithm is considerably faster than the fastest conventional algorithm. Additionally, our novel region coding data structure uses much less memory in typical cases than the traditional region label image. Even in worst case situations the processing time of our algorithm is linear with the number of pixels in an image.

Computer vision techniques for semi-automatic reconstruction of ripped-up documents

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.

JPGcarve: An Advanced Tool for Automated Recovery of Fragmented JPEG Files

In this paper, we present a new tool for forensic recovery of single and multi-fragment JPEG/JFIF data files. First, we discuss the basic design and the technical methods composing our proposed data carving algorithm. Next, we compare the performance of our method with the well-known Adroit Photo Forensics (APF) state-of-the art tool. This comparison is centered on both the carving results as well as the obtained data processing speed, and is evaluated in terms of the results that can be obtained for several well-known reference data sets. Important to note is that we specifically focus on the fundamental recovery and fragment matching performance of the tools by forcing them to use various assumed cluster sizes. We show that on all accounts our new tool can significantly outperform APF. This improvement in data processing speed and carving results can be mostly attributed to novel methods to iterate and reduce the data search space and to a novel parameterless method to determine the end of a fragment based on the pixel data. Finally, we discuss several options for future research.

Line segment based watershed segmentation

In this paper we present an overview of our novel line segment based watershed segmentation algorithm. Most of the existing watershed algorithms use the region label image as the main data structure for its ease of use. These type of watershed algorithms have a relatively large memory footprint and need unbounded memory access. For our new watershed algorithm we replaced the traditional region label image with a data structure that stores the regions in linked line segments. Consequently, the new algorithm has a much smaller memory footprint. Using the new data structure also makes it possible to create an efficient algorithm that only needs one scan over the input image and that only needs the last 3 lines and a small part of the data structure in memory.

A fast dynamic border linking algorithm for region merging

In this paper we present our region merging algorithm that is built with special attention on speed but still includes all the necessary functionality to use a wide range of both region based and border based dissimilarity criteria. The algorithm includes a novel method to dynamically link the common borders between two segments during the region merging. The main part of the paper will concentrate on the efficient data structures and functions that are needed to obtain a fast region merging algorithm. Also, all the special situations that can occur in the segment topology are completely covered. We give a detailed report on the execution times of the algorithm and show some of the segmentation results we obtained.

Watershed data aggregation for mean-shift video segmentation

Object segmentation is considered as an important step in video analysis and has a wide range of practical applications. In this paper we propose a novel video segmentation method, based on a combination of watershed segmentation and mean-shift clustering. The proposed method segments video by clustering spatio-temporal data in a six-dimensional feature space, where the features are spatio-temporal coordinates and spectral attributes. The main novelty is an efficient data aggregation method employing watershed segmentation and local feature averaging. The experimental results show that the proposed algorithm significantly reduces the processing time by mean-shift algorithm and results in superior video segmentation where video objects are well defined and tracked throughout the time.