Guopu Zhu

Boundary-based image segmentation using binary level set method

A novel binary level set method for boundary-based image segmentation is proposed, which is extended from region-based binary level set methods. The proposed binary level set method is based on the geometric active contour framework, which is a traditional level set method applied in boundary-based image segmentation. However, being different from the geometric active contour, the proposed binary level set method replaces the traditional level set function with a binary level set function to reduce the expensive computational cost of redistancing the traditional level set function. The experiments and complexity analysis show that the proposed binary level set method is more efficient than the geometric active contour for image segmentation while giving similar results to the geometric active contour.

An Effective Method for Detecting Double JPEG Compression With the Same Quantization Matrix

Detection of double JPEG compression plays an important role in digital image forensics. Some successful approaches have been proposed to detect double JPEG compression when the primary and secondary compressions have different quantization matrices. However, detecting double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective error-based statistical feature extraction scheme is presented to solve this problem. First, a given JPEG file is decompressed to form a reconstructed image. An error image is obtained by computing the differences between the inverse discrete cosine transform coefficients and pixel values in the reconstructed image. Two classes of blocks in the error image, namely, rounding error block and truncation error block, are analyzed. Then, a set of features is proposed to characterize the statistical differences of the error blocks between single and double JPEG compressions. Finally, the support vector machine classifier is employed to identify whether a given JPEG image is doubly compressed or not. Experimental results on three image databases with various quality factors have demonstrated that the proposed method can significantly outperform the state-of-the-art method.

Gradient vector flow active contours with prior directional information

Active contours, or snakes, have been widely used in image processing and computer vision for image segmentation and object tracking. However, they usually have poor performance in segmenting images with complex object shape and complex background, and also in dealing with the issue of weak-edge-leakage. To guide the front of active contour toward the desired object boundary and prevent it from moving over the weak edges with strong neighbors, we present a novel external force field, referred to as gradient and direction vector flow (G&DVF), which integrates the gradient vector flow (GVF) and the prior directional information provided by a user. The proposed method is sufficiently general and simple to implement. The experiments conducted on image segmentation demonstrate that the proposed method is insensitive to image clutters/noise and capable of driving the fronts of active contours to conform to complex shapes and addressing the issue of weak-edge-leakage in some cases.

Dual geometric active contour for image segmentation

A dual geometric active contour applied mainly in image segmentation is proposed. Two contours of the dual geometric active contour evolve from the interior and exterior of the segmented object, respectively, to the desired boundary. The inner and outer contours represented by level set functions interact with each other during the evolutions of the active contour to avoid getting in local minima traps of the functional optimizations. The experiments on image segmentation show that the proposed dual geometric active contour can relieve the problem in initialization.

Fragility Analysis of Adaptive Quantization-Based Image Hashing

Fragility is one of the most important properties of authentication-oriented image hashing. However, to date, there has been little theoretical analysis on the fragility of image hashing. In this paper, we propose a measure called expected discriminability for the fragility of image hashing and study this fragility theoretically based on the proposed measure. According to our analysis, when Gray code is applied into the discrete-binary conversion stage of image hashing, the value of the expected discriminability, which is dominated by the quantization stage of image hashing, is no more than 1/2. We further evaluate the expected discriminability of the image-hashing scheme that uses adaptive quantization, which is the most popular quantization scheme in the field of image hashing. Our evaluation reveals that if deterministic adaptive quantization is applied, then the expected discriminability of the image-hashing scheme can reach the maximum value (i.e., 1/2). Finally, some experiments are conducted to validate our theoretical analysis and to compare the performance of several quantization schemes for image hashing.

Rapid and brief communication: Efficient edge-based object tracking

Object tracking has been widely applied to video surveillance, robot localization and human-computer interaction. In this paper, an edge-based tracking algorithm is proposed. We extract the feature points by efficiently utilizing the image edges in the object region. Then the parameter vector of the objects motion model is estimated based on minimizing the sum-of-squared differences between the reference feature points in the reference frame and the observed feature points in the tracking sequence frame. The experiments show that the edge-based tracking algorithm proposed by us can track object efficiently under uniform and varying illumination conditions.

Efficient Illumination Insensitive Object Tracking by Normalized Gradient Matching

We propose a novel tracking algorithm by minimizing the sum-of-squared differences (SSD) between the normalized image gradients of the template image and the input image from the test image sequence. The proposed tracking algorithm is efficient to implement since it is based on the framework of the inverse compositional algorithm, a computationally efficient tracking technique. The experiments show that the proposed tracking algorithm is superior to the intensity-based inverse compositional algorithm in tracking objects under varying illumination conditions.

Edge Perpendicular Binary Coding for USM Sharpening Detection

Unsharp masking (USM) sharpening is a basic technique for image manipulation and editing. In recent years, the detection of USM sharpening has attracted attention from image forensics point of view. After USM sharpening, overshoot artifacts, which shape image texture, are generated along image edges. By utilizing the special characteristic of the texture modification caused by the USM sharpening, a novel method called edge perpendicular binary coding is proposed in this letter to detect USM sharpening. Extensive experiments have been conducted to show the superiority of the proposed method over the existing methods.

A Study on the Randomness Measure of Image Hashing

How to measure the security of image hashing is still an open issue in the field of image authentication. Some works have been conducted on the security measure of image hashing. One of the most important works is the randomness measure proposed by Swaminathan, which uses differential entropy as a metric to evaluate the security of randomized image features and has been applied mainly in the security analysis of the feature extraction stage of image hashing. It is meaningful to measure the randomness of the image features over the secret-key set for the security of image hashing because the image features extracted by image hashing should be generated randomly and difficult to guess. However, as is well known, differential entropy is not invariant to scaling; thus it might not be enough to evaluate the security of randomized image features. In this paper, we show the fact that if the image features of an image hash function are scaled by a constant that is large than one, then the tradeoff between the robustness and the fragility of the image hash function will not change at all, but the security indicated by the randomness measure will increase. The above-mentioned fact seems to contradict the following. First, the security of image hashing, which conflicts with robustness and fragility, cannot increase freely. Secondly, a deterministic operation, such as deterministic scaling, does not change the security of image hashing in terms of the difficulty of guessing the secret key or randomized image features. Therefore, the randomness measure should be modified to be invariant to scaling at least.

Estimation of distribution algorithms making use of both high quality and low quality individuals

To demonstrate the usefulness of low quality individuals for estimation of distribution algorithms, estimation of distribution algorithms using both high quality and low quality individuals are tested on several benchmark problems and their results are compared with those obtained by estimation of distribution algorithms where only high quality individuals are used. The usefulness of low quality individuals for speeding up the search of estimation of distribution algorithms is confirmed by the experimental results.