Search

During the past years, research has focused on the reconstruction of three-dimensional point cloud models from unordered and uncalibrated sets of images. Most of the proposed solutions rely on the structure-from-motion algorithm, and their performances significantly degrade whenever exchangeable image file format information about focal lengths is missing or corrupted. We propose a preprocessing strategy that permits estimating the focal lengths of a camera more accurately. The basic idea is to cluster the input images into separate subsets according to an array of interpolation-related multimedia forensic clues. This operation permits having a more robust estimate and improving the accuracy of the final model. [ABSTRACT FROM AUTHOR]

A skew-estimation method using straight lines in document images is presented. Unlike conventional approaches exploiting the properties of text, we formulate the skew-estimation problem as an estimation task using straight lines in images and focus on robust and accurate line detection. To be precise, we adopt a block-based edge detector followed by a progressive line detector to take clues from a variety of sources such as text lines, boundaries of figures/tables, vertical/horizontal separators, and boundaries of textblocks. Extensive experiments on the datasets of skewed images and competition results reveal that the proposed method works robustly and yields accurate skew-estimation results. [ABSTRACT FROM AUTHOR]

Multimodal imaging systems have recently been drawing attention in fields such as medical imaging, remote sensing, and video surveillance systems. In such systems, estimating depth has become possible due to the promising progress of multimodal matching techniques. We perform a systematic performance evaluation of similarity measures frequently used in the literature for dense multimodal stereovision. The evaluated measures include mutual information (MI), sum of squared distances, normalized cross-correlation, census transform, local self-similarity (LSS) as well as descriptors adopted to multimodal settings, like scale invariant feature transform (SIFT), speeded-up robust features (SURF), histogram of oriented gradients (HOG), binary robust independent elementary features, and fast retina keypoint (FREAK). We evaluate the measures over datasets we generated, compiled, and provided as a benchmark and compare the performances using the Winner Takes All method. The datasets are (1) synthetically modified four popular pairs from the Middlebury Stereo Dataset (namely, Tsukuba, Venus, Cones, and Teddy) and (2) our own multimodal image pairs acquired using the infrared and the electro-optical cameras of a Kinect device. The results show that MI and HOG provide promising results for multimodal imagery, and FREAK, SURF, SIFT, and LSS can be considered as alternatives depending on the multimodality level and the computational complexity requirements of the intended application. [ABSTRACT FROM AUTHOR]

The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets. ©2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.3.033015] [ABSTRACT FROM AUTHOR]

Variational methods have attracted a lot of attention in the literature, especially for image and mesh segmentation. The methods aim at minimizing the energy to optimize both edge and region detections. We propose a spectral mesh decomposition algorithm to obtain disjoint but meaningful regions of an input mesh. The related optimization problem is nonconvex, and it is very difficult to find a good approximation or global optimum, which represents a challenge in computer vision. We propose an alternating split Bregman algorithm for mesh segmentation, where we extended the image-dedicated model to a three-dimensional (3D) mesh one. By applying our scheme to 3-D mesh segmentation, we obtain fast solvers that can outperform various conventional ones, such as graph-cut and primal dual methods. A consistent evaluation of the proposed method on various public domain 3-D databases for different metrics is elaborated, and a comparison with the state-of-the-art is performed. [ABSTRACT FROM AUTHOR]

Images taken under underwater condition usually have color cast and serious loss of contrast and visibility. Degraded underwater images are inconvenient for observation and analysis. In order to address these problems, an underwater image-enhancement method is proposed. A simple yet effective underwater image color cast removal algorithm is first presented based on the optimization theory. Then, based on the minimum information loss principle and inherent relationship of medium transmission maps of three color channels in an underwater image, an effective visibility restoration algorithm is proposed to recover visibility, contrast, and natural appearance of degraded underwater images. To evaluate the performance of the proposed method, qualitative comparison, quantitative comparison, and color accuracy test are conducted. Experimental results demonstrate that the proposed method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images. Additionally, the proposed method is comparable to and even better than several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Passive forgery detection aims to detect traces of image tampering without the need for prior information. With the increasing demand for image content protection, passive detection methods able to identify image tampering areas are increasingly needed. However, most current passive approaches either work only for image-level JPEG compression detection and cannot localize region-level forgery, or suffer from high-false detection rates in localizing altered regions. This paper proposes an effective approach based on discrete cosine transform coefficient analysis for the detection and localization of altered regions of JPEG compressed images. This approach can also work with altered JPEG images resaved in JPEG compressed format with different quality factors. Experiments with various tampering methods such as copy-and-paste, image completion, and composite tampering, show that the proposed approach is able to effectively detect and localize altered areas and is not sensitive to image contents such as edges and textures. [ABSTRACT FROM AUTHOR]

Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. The existing algorithms are mainly graph based, which cast the reassembly problem as a K-vertex disjoint path problem in a directed complete graph, which is an NP-complete problem. Based on the padding bytes in BMP files, we present a method to exclude most impossible paths, which can improve the accuracy and decrease the time complexity of the existing graph-based methods. According to the alignment rule of BMP format, padding bytes must be appended to the end of each row to bring up the length of the row to a multiple of 4 bytes. Hence the fragment, being a vertex of the path which correctly reassembles a file, has a property; its byte values at padding positions must be the padding values. Only the fragments with such property can be candidate fragments for the vertex. On the test dataset which is constructed based on 330 image files, taking eight classical methods as examples, we show that the proposed method produces an accuracy improvement ranging from 32% to 55%, and reduces the run time to a scope from 1/6 to 1/237. © 2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.3.033002] [ABSTRACT FROM AUTHOR]