Muhammad Tariq Mahmood

Intelligent reversible watermarking and authentication: Hiding depth map information for 3D cameras

In this paper, we propose an application for 3D cameras by reversibly hiding the depth map in the corresponding 2D images. The proposed technique is prospective in cameras capable of capturing simultaneously the 2D image and resultant depth map of an object. 3D cameras equipped with self-embedding capability can serve two additional purposes, protection of the captured image and secure transmission of its depth map. The reversible watermarking, in addition to other features, guarantees the lossless recovery of an original image and separation of its depth map when required. For this purpose, a reversible watermarking scheme, based on genetic algorithm (GA), has been proposed which computes suitable threshold for each block of coefficients in the wavelet domain. Thus, a tradeoff is made between watermark imperceptibility and capacity using GA. The threshold map is embedded in addition to the actual payload and thus the proposed approach does not require histogram pre-processing in order to avoid overflow/underflow. The same threshold map has been used for authentication purpose by correlating it with the low-frequency coefficients of the 2D transformed image. Further to exploit the inherent redundancy in the depth map, which is the actual payload in this case, lossless compression has been employed before its embedding. Similarly, besides secret key-based permutation, a cryptographic layer is overlaid on the watermarking layer for security purposes. Experiments conducted on images and depth maps, obtained using a 3D camera and an optical microscopic system, validate the proposed concept.

Optimal depth estimation by combining focus measures using genetic programming

Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape From Focus (SFF) is one of the passive optical methods for 3D shape recovery that uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in the image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we develop Optimal Composite Depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is constructed by optimally combining the primary information extracted using one/or more focus measures. The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of the developed nonlinear function is investigated using both the synthetic and the real world image sequences. Experimental results demonstrate that the proposed estimator is more useful in computing accurate depth maps as compared to the existing SFF methods. Moreover, it is found that the heterogeneous function is more effective than homogeneous function.

PCA-based method for 3D shape recovery of microscopic objects from image focus using discrete cosine transform.

This article introduces a new algorithm for shape from focus (SFF) based on discrete cosine transform (DCT) and principal component analysis (PCA). DCT is applied on a small 3D neighborhood for each pixel in the image volume. Instead of summing all focus values in a window, AC parts of DCT are collected and then PCA is applied to transform this data into eigenspace. The first feature, containing maximum variation is employed to compute the depth. DCT and PCA are computationally intensive; however, the reduced data elements and algorithm iterations have made the new approach competitive and efficient. The performance of the proposed approach is compared with other methods by conducting experiments using image sequences of a synthetic and two microscopic objects. The evaluation is gauged on the basis of unimodality, monotonicity, and resolution of the focus curve. Two other global statistical metrics, root mean square error (RMSE) and correlation have also been applied for synthetic image sequence. Besides, noise sensitivity and computational complexity are also compared with other algorithms. Experimental results demonstrate the effectiveness and the robustness of the new method. Microsc. Res. Tech., 2008.

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.

Nonlinear Approach for Enhancement of Image Focus Volume in Shape From Focus

Mostly, shape-from-focus algorithms use local averaging using a fixed rectangle window to enhance the initial focus volume. In this linear filtering, the window size affects the accuracy of the depth map. A small window is unable to suppress the noise properly, whereas a large window oversmoothes the object shape. Moreover, the use of any window size smoothes focus values uniformly. Consequently, an erroneous depth map is obtained. In this paper, we suggest the use of iterative 3-D anisotropic nonlinear diffusion filtering (ANDF) to enhance the image focus volume. In contrast to linear filtering, ANDF utilizes the local structure of the focus values to suppress the noise while preserving edges. The proposed scheme is tested using image sequences of synthetic and real objects, and results have demonstrated its effectiveness.

Focus measure based on the energy of high-frequency components in the S transform

Focus measure plays a fundamental role in the shape from focus technique. In this Letter, we suggest a focus measure in the S-transform domain that is based on the energy of high-frequency components. A localized spectrum by using variable window size provides a more accurate method of measuring image sharpness as compared to other focus measures proposed in spectral domains. An optimal focus measure is obtained by selecting an appropriate frequency-dependent window width. The performance of the proposed focus measure is compared with those of existing focus measures in terms of three-dimensional shape recovery. Experimental results demonstrate the effectiveness of the proposed focus measure.

A novel technique for removal of high density impulse noise from digital images

Mostly researchers use all pixels within a window to filter out the impulse noise. They increase the size of neighboring pixels with the increase of noise density. However, this estimate of all neighboring pixels does not give promsing results for high level of noise density. In contrast, in the paper, we propose impulse noise removal scheme that emphasizes on few noise-free pixels. The proposed iterative algorithm search the noise-free pixels within a small neighborhood. The noisy-pixel is then repalced with the average estimated from noise-free pixels. The iterative process continues until all noisy-pixels of the corrupted image are filtered. The performance of the proposed method is tested using various impulse noise corrupted images. The simulation results show the proposed scheme is capable of removing high density of impulse noise effectively while preserving the fine image details.

Shape from focus using principal component analysis in discrete wavelet transform

We introduce a new approach for 3-D shape recovery based on discrete wavelet transform (DWT) and principal component analysis (PCA). A small 3-D neighborhood is considered to incorporate the effect of pixels from previous as well as next frames. The intensity values of the pixels in the neighborhood are then arranged into a vector. DWT is applied on each vector to decompose it into approximation and wavelet coefficients. PCA is then applied on modified energies of wavelet components. The first feature in the eigenspace, as it contains maximum variation, is employed to compute the depth. The performance of the proposed approach is tested and is compared with existing methods by using synthetic and real image sequences. The evaluation is gauged on the basis of unimodality and monotonicity of the focus curve. Resolution, accuracy, root mean square error (RMSE), and correlation metrics have been applied to evaluate the performance. Experimental results and comparative analysis demonstrate the effectiveness of the proposed method.

Impulse noise filtering based on noise-free pixels using genetic programming

Generally, the impulse noise filtering schemes use all pixels within a neighborhood and increase the size of neighborhood with the increase in noise density. However, the estimate from all pixels within neighborhood may not be accurate. Moreover, the larger window may remove edges and fine details as well. In contrast, we propose a novel impulse noise removal scheme that emphasizes on few noise-free pixels and small neighborhood. The proposed scheme searches noise-free pixels within a small neighborhood. If at least three pixels are not found, then the noisy pixel is left unchanged in current iteration. This iterative process continues until all noisy pixels are replaced with estimated values. In order to estimate the optimal value of the noisy pixel, genetic programming-based estimator is developed. The estimator (function) is composed of useful pixel information and arithmetic functions. Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.

Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.