A new and improved visual-oriented grayscale frames interpolation method consists of partially changing, step by step using growing structuring elements, the morphological transforms of the foreground content of an input frame with the morphological transforms of the foreground content of an output frame. Better performance comes at the expense of not very great computational complexity. Computer simulations illustrate the results.
A new interactive approach for segmentation and classification of cementitious materials using Scanning Electron Microscope images is presented in this paper. It is based on the denoising of the data with the Block Matching 3D (BM3D) algorithm, Binary Partition Tree (BPT) segmentation and Support Vector Machines (SVM) classification. The latter two operations are both performed in an interactive way. The BPT provides a hierarchical representation of the spatial regions of the data and, after an appropriate pruning, it yields a segmentation map which can be improved by the user. SVMs are used to obtain a classification map of the image with which the user can interact to get better results. The interactivity is twofold: it allows the user to get a better segmentation by exploring the BPT structure, and to help the classifier to better discriminate the classes. This is performed by improving the representativity of the training set, adding new pixels from the segmented regions to the training samples. This approach performs similarly or better than methods currently used in an industrial environment. The validation is performed on several cement samples, both qualitatively by visual examination and quantitatively by the comparison of experimental results with theoretical values.
It is shown that the watermarking algorithm presented in another paper [Ganic and Eskicioglu, J. Electron. Imaging , 043004 (2005)] has a very high probability of a false-positive answer and has its limitations in practice. Furthermore, the intrinsic reasons of the high false-alarm probability are as follows: the basis space of singular value decomposition is image content dependent, there is no one-to-one correspondence between singular value vector and image content, because singular value vectors have no information on the structure of image. Thus, the most important reason is a result of a false conception to insert watermark singular value vectors without information on the structure of the watermark. Finally, some examples are given to prove our results of theoretical analysis.
We propose a frame-matching algorithm for video sequences, when a video sequence is modified from its original through frame removal, insertion, shuffling, and data compression. The proposed matching algorithm defines an effective matching cost function and minimizes cost using dynamic programming. Experimental results show that the proposed algorithm provides a significantly lower probability of matching errors than the conventional algorithm.
We propose a new method that classifies wafer images according to their defect types for automatic defect classification in semiconductor fabrication processes. Conventional image classifiers using global properties cannot be used in this problem, because the defects usually occupy very small regions in the images. Hence, the defects should first be segmented, and the shape of the segment and the features extracted from the region are used for classification. In other words, we need to develop a classification-after-segmentation approach for the use of features from the small regions corresponding to the defects. However, the segmentation of scratch defects is not easy due to the shrinking bias problem when using conventional methods. We propose a new Markov random field-based method for the segmentation of wafer images. Then we design an AdaBoost-based classifier that uses the features extracted from the segmented local regions.
This paper addresses the representation of binary images using mathematical morphology, a nonlinear theory for image processing, based on set theory. The new image representation, called "five-step" skeleton representation, is an extension of the morphological binary skeleton. It consists of calculating the morphological digital binary skeleton using squares or crosses and then reiterating the above procedure for skeleton subsets using lines (horizontal, vertical, 45° and 135°). The theoretical background of the new morphological iterative image representation is presented. Applications and results are illustrated by computer simulations.
Most eye localization methods suffer from illumination variation. To overcome this problem, we propose an illumination normalization technique as a preprocessing step before localizing eyes. This technique requires no training process, no assumption on the light conditions, and no alignment between different images for illumination normalization. Moreover, it is fast and thus effective for real-time applications. Experiment results verify the effectiveness and efficiency of the eye localization scheme with the proposed illumination normalization technique.
To improve the performance of a top-hat transformation for infrared small target enhancement, a new class of top-hat transformation through structuring element construction and operation reorganization is proposed. The structuring element construction and operation reorganization are based on the property of the infrared small target image and thus can greatly improve the performance of small target enhancement. Experimental results verified that it was very efficient.
In the camera manufacturing, special methods are needed to detect blemishes occurring on the camera sensor pixels. A blemish is referred as a region of pixels in the camera sensor that are somewhat darker than the background. The blemishes are difficult to detect accurately, but on the other hand, they cause a significant reduction in camera quality. We present a novel filtering method for the blemish detection. The method is based on image scaling, filtering, and difference image calculation that is very fast and accurate in the detection of blemishes. In addition, the algorithm can cope with unprocessed raw image data, in which various distortions, such as noise and vignetting, can be present.
In multispectral imaging systems, the accuracy of reflectance estimation can be degraded by the nonlinearity in imaging process, which is due to non-Gaussian distribution of the data and nonlinear optoelectronic conversion function of the camera. To deal with nonlinearity, we propose to extend camera responses by high-order polynomials and reduce the overfitting problem by partial least-squares (PLS) regression. Experiment shows that, in terms of both spectral and colorimetric error metrics, the proposed method performs better than Wiener estimation and ordinary polynomial regression, and is similar to polynomial regression with regularization.
Automated detection of the clinically relevant image planes in a three-dimensional echocardiographic (3-DE) data set has potential applications for diagnosis of pediatric congenital heart disease. We propose a method based on template matching to automatically detect the four-chamber image plane (4CIP) in a 3-DE data set. First, a normal four-chamber image is chosen as the template. Second, to find the 4CIP in a 3-D volume, a series of cross sections are extracted from this volume. Then, a coarse-to-fine retrieval is applied to the stack of slices and the image most similar to the template is proposed as the 4CIP of this volume. We tested the method on 28 data sets of normal children and 22 data sets of children suffering from congenital heart disease, and the error rates are 7 and 9 , respectively. As will be shown, the autodetection algorithm can be straightforwardly implemented, has low computational complexity, and is robust to noise.
A simple and effective method is presented for discrete-cosine-transform (DCT)-domain downsizing. Various methods employed in DCT-domain downsizing simply reuse the frequency component of DCT, which shows a severe aliasing effect. The proposed approach extends the downsizing method for alleviating or reducing the aliasing effect with a windowing operation, which adjusts the magnitude of the DCT coefficient. Visual inspection showed satisfactory results, with no complexity overhead and performance degradation regarding the peak-signal-to noise ratio (PSNR) after upsampling.
Cycle spinning, a technique mainly used for wavelet denoising, has also been shown to be successful toward image resolution upscaling in the wavelet domain. We propose a directional variant of the cycle spinning methodology. We obtain estimates of local edge orientation from a wavelet decomposition of the available low-resolution image and use this information to influence the choice of cycle spinning parameters that are employed for resolution upscaling. Our experimental results show that the proposed method outperforms competing methods for a wide range of images offering modest but consistent improvements both in objective as well as subjective terms. Lower computational complexity compared to the conventional cycle spinning is also demonstrated.
Defects detection on images is a current task in quality control and is often integrated in partially or fully automated systems. Assessing the performances of defects detection algorithms is thus of great interest. However, being application and context dependent, it remains a difficult task. This paper describes a methodology to measure the performances of such algorithms on large size images in a semi-automated defect inspection situation. Considering standard problems occuring on real cases, a comparison of typical performance evaluation methods is made. This analysis leads to the construction of a simple and practical ROC-based method. This algorithm extends the pixel-level ROC analysis to an object-based approach by dilating the ground-truth and the set of detected pixels before calculating true positive and false positive rates. These dilations are computed thanks to the knowledge of a human defined ground-truth and gives to true positive and false positive rates more consistent values in the semi-automated inspection context. Moreover, dilation process is designed to be automatically suited to the objects shape in order to be applied on all types of defects.
We present a modified binary level set method for two-phase image segmentation, which is based on the binary level set method originally proposed by X.-C. Tai [Int. J. Comput. Vis. , 61-76 (2007)]. The modified binary level set method is superior to Tai 's binary level set method in preserving the curve evolution property of gradually shrinking and expanding the partition interface represented by a binary level set function in the segmentation process. Some experiments conducted on real images show the good results of the modified binary level set method.
The method of paired comparison based on Thurstone's case V of his law of comparative judgments is often used as a psychophysical method to derive interval scales of perceptual qualities in imaging applications. However, methods for determining confidence intervals and critical distances for significant differences have been elusive, leading some to abandon the simple analysis provided by Thurstone's formulation. Monte Carlo simulations of paired comparison experiments were performed in order to derive an empirical formula for determining error. The results show that the variation in the distribution of experimental results can be well predicted as a function of stimulus number and the number of observations. Using these results, confidence intervals and critical values for comparisons can be made using traditional statistical methods.
Camera synchronization is necessary for multicamera applications. We propose a simple and yet effective approach termed random on-off light source (ROOLS) to synchronize video sequences. It uses a single light source such as an LED to generate a random binary valued signal that is captured by the video cameras. The captured binary-valued sequences are then matched and the temporal offset of the cameras is computed up to subframe interval precision. We test the proposed method on synchronizing video sequences captured under a variety of illumination conditions and the results are verified against the ground truth provided by an LED array clock. The main contribution of the proposed method is that it reliably achieves high-precision synchronization at a low cost of only adding a simple light source. In addition, it is suited for synchronization in both laboratory and outdoor environments.
In image classification, the common texture-based methods are based on image gray levels. However, the use of color information improves the classification accuracy of the colored textures. In this paper, we extract texture features from the natural rock images that are used in bedrock investigations. A Gaussian bandpass filtering is applied to the color channels of the images in RGB and HSI color spaces using different scales. The obtained feature vectors are low dimensional, which make the methods computationally effective. The results show that using combinations of different color channels, the classification accuracy can be significantly improved.
Although stereo matching algorithms based on belief propagation (BP) tend to show excellent matching performance, their huge computational complexity has been the major barrier to real-time applications. In this light, we propose a parallel very large scale integration (VLSI) architecture for BP computation, which has only simple integer operations and shows low matching error rate for the Middlebury database.
By incorporating the image gradient directional information into the geodesic active contour model, we propose a novel active contour model called directional geodesic active contour, which has the advantage of selectively detecting the image edges with different gradient directions. The experiment results show the high performance of the proposed active contour in image segmentation, especially when multiple edges with different gradient directions are present near the object boundary to confuse the active contour.