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.

We present a new fuzzy logic-based approach for automatic optimized features location. The technique is used for improved automatic alignment and classification of silicon wafers and chips that are used in the electronics industry. The proposed automatic image processing approach was realized and experimentally demonstrated in real industrial application with typical wafers. The automatic features location and grading supported the industrial requirements and could replace human expert-based inspection that currently is performed manually.

A novel minutiae-based method is proposed to match deformed fingerprints. We combine minutiae structures and descriptors to obtain multiple pairs of reference minutiae and globally align two sets of minutiae to get a common overlapping region based on these pairs of reference minutiae. At the matching stage, our proposed approach aligns local areas in two fingerprints with new reference minutiae pairs that are selected from matched minutiae pairs. After registration of the fingerprints according to the local correspondence, the number of matched minutiae can be counted using tight thresholds. Experimental results confirm that the proposed algorithm is effective for fingerprint matching with nonlinear distortions.

Given the potential demonstrated by research into bone-tissue engineering, the use of medical image data for the rapid prototyping (RP) of scaffolds is a subject worthy of research. Computer-aided design and manufacture and medical imaging have created new possibilities for RP. Accurate and efficient design and fabrication of anatomic models is critical to these applications. We explore the application of RP computational methods to the repair of a pediatric skull defect. The focus of this study is the segmentation of the defect region seen in computerized tomography (CT) slice images of this patient's skull and the three-dimensional (3-D) surface rendering of the patient's CT-scan data. We see if our segmentation and surface rendering software can improve the generation of an implant model to fill a skull defect.

Automatic image segmentation is a fundamental and challenging work in image analysis. We present a stochastic contour approach that draws the contour by multiple agents stochastically, each driven by a simple policy. A contour confidence map is formed, and the image is partitioned hierarchically according to the probability of being surrounded by an average contour. The segmentation is formed by truncating the hierarchical tree based on the dissimilarity increment. The average contour formed in the stochastic contour approach no longer depends on the initial conditions and tolerates less guaranteed convergence. The stochastic contour evolution provides perturbation to jump out of local minima, while the average contour handles model uncertainty naturally. No training process is involved in this approach. The experimental evaluation on a large amount of images with diverse visual properties has shown robustness and good performance of our technique.

The measurement of rapid and microsize changes in fruit diameter can be used to understand how plants respond to diurnal variation in water content and long-term growth conditions. The most current techniques involve physical measurements. The contact of the physical sensor places a stress on fruit and affects normal fruit growth. To solve this problem, we present a noncontact optical method for measuring fruit diameter in crop fields accurately. A rough-to-fine strategy is considered, where a binary image is first obtained and used to find candidate fruit body edge points; then a Zernike moment operator is used to determine edges of the fruit body with subpixel accuracy. Finally, the fruit diameter is computed from the edge pixels of the fruit body. Measuring experiments performed during the bloom stage of grapes show high sensitivity of the proposed method. This allows for clear detection of diurnal patterns of grape diameter changes and precise monitoring of very slight variations in fruit growth rates. Experiments show that the developed system is robust, accurate, and effective. The proposed technique has proven to be an effective tool to better detect physiological disorders in plants.

Although depth information of a scene is not stored while taking a picture, some depth information is retained in the captured image. A method is proposed for generating the depth map of a single image based on different scene categories. The image is first classified into a category based on color and texture information, and then the depth map of the image is generated according to the different scene categories. The depth map can be used to generate stereo binocular image pairs by left- and right-shifting the original image. Then, the stereoscopic image with three-dimensional (3-D) visual effect can be viewed through a 3-D stereo display. The experiments showed that the proposed method works well, producing a satisfactory stereoscopic effect.

We consider the problem of classifying textures. First, we consider images where the orientation of the texture is known. Then, we consider the classification of textures where the orientation is unknown. Last, classification in real scenes is considered. A wide variety of techniques are tested using the Outex framework. We introduce a new grayscale multiscale texture classification method based on a class of morphological filters called sieves. The method, denoted Tex-Mex because it extracts TEXture features using Morphological EXtrema filters, is shown to be among the best performing texture feature extraction methods. Tex-Mex features can be computed rapidly and are shown to be more robust and compact than the alternatives. Furthermore, they may be applied over windows of arbitrary size and orientation, a useful attribute when classifying texture in real scenes.

In the National Museum of Japanese History, 215,759 artifacts are stored and used for research and exhibitions. In museums, due to the limitation of space in the galleries, a guidance system is required to satisfy visitors' needs and to enhance their understanding of the artifacts. We introduce one exhibition using imaging technology to improve visitors' understanding of a kimono (traditional Japanese clothing) exhibition. In the imaging technology introduced, one data projector, one display with touch panel interface, and magnifiers were used as exhibition tools together with a real kimono. The validity of this exhibition method was confirmed by results from a visitors' interview survey. Second, to further develop the interactive guidance system, an augmented reality system that consisted of cooperation between the projector and a digital video camera was also examined. A white paper board in the observer's hand was used as a projection screen and also as an interface to control the images projected on the board. The basic performance of the proposed system was confirmed; however continuous development was necessary for applying the system to actual exhibitions.

We combine an optimal filtering approach with a multiple description coding (MDC) approach to enhance the quality of video at the receiver. Optimal filter coefficients are computed at the encoder and added to each description. At the decoder, optimal filter coefficients are obtained directly from the bit stream and employed to improve the quality. Experimental results show that the proposed approach enables better MDC performance for video in case of packet losses.

Our goal was to evaluate the error contributed by photon fluence measurements to the detective quantum efficiency (DQE) of an x-ray imaging system. The investigation consisted of separate error analyses for the exposure and spectrum measurements that determine the photon fluence. Methods were developed for each to determine the number of measurements required to achieve an acceptable error. A new method for calculating the magnification factor in the exposure measurements was presented and compared to the existing method. The new method not only produces much lower error at small source-to-image distances (SIDs) such as clinical systems, but is also independent of SID. The exposure and spectra results were combined to determine the photon fluence error contribution to the DQE of 4%. The error in this study is small because the measurements resulted from precisely controlled experimental procedures designed to minimize the error. However, these procedures are difficult to follow in clinical environments, and application of this method on clinical systems could therefore provide important insight into error reduction. This investigation was focused on the error in the photon fluence contribution to the DQE, but the error analysis method can easily be extended to a wide range of applications.

We present a novel hybrid method that includes a modulated scheme for reducing the distortion of the reconstructed image and a two-layered structure for grouping participants with different weights in a secret image sharing problem. Conventional secret image sharing methods suffer from truncation distortion, which is the difference between a pixel value and its truncated result. We first present a histogram modulation scheme to modulate the truncated pixels to prevent the truncation distortion. A two-layer secret image sharing scheme groups the participants in the first layer and weights them differently in the second layer. The proposed modulated secret image sharing method merges these two schemes. Experimental results reveal that the proposed method efficiently reduces the truncation distortion and groups the participants with different possessing weights.

We present a set of multiscale, multidirectional, rotation-invariant features for color texture characterization. The proposed model is based on the ranklet transform, a technique relying on the calculation of the relative rank of the intensity level of neighboring pixels. Color and texture are merged into a compact descriptor by computing the ranklet transform of each color channel separately and of couples of color channels jointly. Robustness against rotation is based on the use of circularly symmetric neighborhoods together with the discrete Fourier transform. Experimental results demonstrate that the approach shows good robustness and accuracy.

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

We propose a novel approach for the automatic representation of pictures achieving a more effective organization of personal photo albums. Images are analyzed and described in multiple representation spaces, namely, faces, background, and time of capture. Faces are automatically detected, rectified, and represented, projecting the face itself in a common low-dimensional eigenspace. Backgrounds are represented with low-level visual features based on an RGB histogram and Gabor filter bank. Faces, time, and background information of each image in the collection is automatically organized using a mean-shift clustering technique. Given the particular domain of personal photo libraries, where most of the pictures contain faces of a relatively small number of different individuals, clusters tend to be semantically significant besides containing visually similar data. We report experimental results based on a data set of about 1000 images where automatic detection and rectification of faces lead to approximately 400 faces. Significance of clustering has been evaluated, and results are very encouraging.

Human perception of material colors depends heavily on the nature of the light sources that are used for illumination. One and the same object can cause highly different color impressions when lit by a vapor lamp or by daylight, respectively. On the basis of state-of-the-art colorimetric methods, we present a modern approach for the calculation of color-rendering indices (CRI), which were defined by the International Commission on Illumination (CIE) to characterize color reproduction properties of illuminants. We update the standard CIE method in three main points: first, we use the CIELAB color space; second, we apply a linearized Bradford transformation for chromatic adaptation; and finally, we evaluate color differences using the CIEDE2000 total color difference formula. Moreover, within a real-world scene, light incident on a measurement surface is composed of a direct and an indirect part. Neumann and Schanda [Proc. CGIV'06 Conf., Leeds, UK, pp. 283-286 (2006)] have shown for the cube model that diffuse interreflections can influence the CRI of a light source. We analyze how color-rendering indices vary in a real-world scene with mixed direct and indirect illumination and recommend the usage of a spectral rendering engine instead of an RGB-based renderer for reasons of accuracy of CRI calculations.

This PDF file contains the editorial “Digital Color Management Encoding Solutions, Second Edition” for JEI Vol. 18 Issue 04