The virtual cockpit application aims to perform ergonomics studies on future aircraft cockpits, directly from digital mock-ups. This is made possible by immersing a user in a digital mock-up with the help of virtual reality devices. The challenge is then to reproduce a realistic and exact visual environment. To do that, some visual criteria have to be determined: the necessary values of visual acuity, temporal resolution, and field of view. Some of them are easy to find, but the field-of-view requirement is far more subjective and complex to evaluate. We present tests which have helped us to determine the necessary and sufficient values of field of view for such an application. We advise then to use a field of view higher than 75 deg, for ergonomic studies and good performances of the users, and up to 133 deg to increase visual comfort. These results will be used for the specification of a head-mounted display for the application.

We present a 64-×64-pixel CMOS image sensor chip that can acquire a 16-gray-level image containing both spatial and temporal information of the moving luminous object under observation. The image can next be processed using any image processing software in order to determine the speed, trajectory, and direction of motion of the moving object. The architecture of the sensor and an example of its application to the determination of the speed of a moving laser spot are described.

Automatic shot boundary detection is a field, where many techniques and methods have been proposed and have claimed to perform reliably, especially for abrupt scene cut detection. However, all the proposed methods share a common drawback: the necessity of a threshold value, which is used as a reference for detecting scene changes. The determination of the appropriate value or the dynamic reestimation of this threshold parameter remains the most challenging issue for the existing shot boundary detection algorithms. We introduce a novel method for shot boundary detection of discrete cosine transform (DCT)-based and low-bit-rate encoded clips, which exploits the perceptual blockiness effect detection on each frame without using any threshold parameter, therefore minimizing the processing demands required for algorithm implementation.

An imaging spectrometer covering the 400– to 1000–nm band is conceived and developed. The system is based on an acousto-optic tunable filter (AOTF) attached to a high-performance digital camera. The AOTF enables the selection of spectral bands with an rf signal in the range of 70 to 218 MHz. It includes a telecentric optical system that enhances system efficiency. Additionally, a smart choice of integration time reduces the dependence of the efficiency on the frequency. Calibration includes filter characterization and compensation of crystal nonconstant diffraction efficiency and spatial nonhomogeneity. The system is controlled by a PC application specifically developed for this purpose, providing wide versatility, while enabling transparent and intuitive management to nonexpert users. The spectrometer is validated by estimating the light absorption of leaves and their chlorophyll content.

Design principles are formulated to develop visually optimal multiprimary subpixel architectures. Two new hexagonal subpixel architectures optimized for multiprimary color displays are shown, each designed according to these principles. These new multiprimary pixel architectures are considered to be useful to eliminate the color fringe artifact. They are expected to yield images of better visual quality than previous three primary color architectures including the RGB stripe architecture. A new image rendering method is also formulated for multiprimary subpixel architectures. This method can be used with and without subpixel rendering. An error function is defined enabling proper chromaticity reproduction and enhanced luminance resolution.

A miniaturized laser-scanning endoscope is presented that makes use of a composite laser beam for color imaging. A novel approach is followed in the device, which is based on scanning the target tissue with the laser beam using two miniaturized MEMS (microelectromechanical systems) micromirrors and employs specific collection, detection, and postprocessing of the scattered light for reconstructing a color image of the tissue. A resolution of the order of 16 line pairs/mm is achieved for a working distance of 50 mm while the focal depth is larger than 5 mm. Key considerations of the system design are presented, along with results on the operation of the micromirrors, an analysis of the optical design of the endoscope head, and remarks on the assessment of image quality.

We present a new method for computerized tomography (CT)-based investigation of pulmonary airways. The developed approach is based on a fully automated 3-D reconstruction of the bronchial tree from volumetric spiral CT data. The 3-D reconstruction procedure combines mathematical morphology-based 2-D segmentation, performed on axial images, together with 3-D topology-based restoration and filtering. The 3-D bronchial tree is reconstructed up to the sixth subdivision order and is investigated by using a volume-rendering technique, where bronchi voxels are assigned the gray-level value of their projection in the original CT images. Such a visualization modality, referred to as CT bronchography, provides radiologists with a perception of the sole 3-D bronchial tree structure and preserves the original data contrast. Tested on a data set corresponding to 20 clinical examinations, the proposed method shows robustness with respect to various pathologies such as stenosis, bronchiectasis, mucoid impactions, and infiltrative diseases. It emphasizes the interest in considering CT bronchography as a complementary tool for clinical diagnosis and follow-up of airway diseases.

To prevent large error accumulation in multiple image registration considering real-time applications, we propose a new fast global mosaic method using sequential block matching in regularly spaced grid features. We use a specific graph structure called a sequential graph defined in each grid and propose a new shortest-path search algorithm to find exact matchings in a sequential manner. Using a sequential structure, the search range to find matchings can be reduced, and the new sequential shortest-path search algorithm reduces the number of block matchings. Also, by introducing uncertainty information of the point feature, we use regularly spaced grids efficiently instead of using corner features and line structures. The framework presented here has an efficient structure for implementation in real time because it is fast, sequential, and consistent in computation time and system memory.

Various applications such as industrial product inspection or low signal-to-noise situations (as in thermal imaging) employ a time delay and integration (TDI) scanning imaging technique. Due to common vibration sources such as the camera platform motion or the thermal detector's cooling system, the acquired image may be degraded by severe shift-variant geometric distortions and motion blur. We use these vibrations in terms of superresolution to create an improved high-resolution video sequence from the degraded lower resolution sequence, in two main stages: subpixel motion estimation with respect to translations and rotations, used for point spread function (PSF) estimation, followed by an efficient implementation of the projection onto convex sets (POCS) method. We generalize and considerably improve a previous technique for restoration of a single image captured by a translational vibrated staggered-TDI camera (Hochman et al., 2004). The proposed method is implemented with both simulated videos and real degraded thermal videos. A comparative analysis shows an advantage of the proposed method over others in restoring the vibrated videos.

We describe a new fusion method for time-frequency distribution (TFD) that increases the ability to detect and classify time-varying signals while suppressing signal-dependent artifacts and noise. This is achieved by applying a least-squares algorithm to estimate the second-order approximation Volterra series coefficients of the outputs of selected TFDs. These coefficients are used for the fusion of the selected TFDs and generate a new TFD. The proposed fusion method is compared with four other fusion methods in terms of resolution and signal-to-noise ratio (SNR) in the time-frequency (TF) plane. Five representative TFDs are fused to generate a new TFD and their performances are analyzed. The results show that the new fusion method considerably increases sharpness (resolution) and strength (SNR) of the signal in the TF plane and, furthermore, achieves better signal description over other fusion methods and the traditional TFDs.

We propose a fuzzy logic recursive scheme for motion detection and spatiotemporal filtering that can deal with the Gaussian noise and unsteady illumination conditions in both the temporal and spatial directions. Our focus is on applications concerning tracking and denoising of image sequences. We process an input noisy sequence with fuzzy logic motion detection to determine the degree of motion confidence. The proposed motion detector combines the membership of the temporal intensity changes, appropriately using fuzzy rules, where the membership degree of motion for each pixel in a 2-D sliding window is determined by a proposed membership function. Both the fuzzy membership function and the fuzzy rules are defined in such a way that the performance of the motion detector is optimized in terms of its robustness to noise and unsteady lighting conditions. We simultaneously perform tracking and recursive adaptive temporal filtering, where the amount of filtering is inversely proportional to the confidence in the existence of motion. Finally, temporally filtered frames are further processed by a proposed spatial filter to obtain a denoised image sequence. Our main contribution is a robust novel fuzzy recursive scheme for motion detection and temporal filtering. We evaluate the proposed motion detection algorithm using two criteria: (1) robustness to noise and to changing illumination conditions and (2) motion blur in temporal recursive denoising. Additionally, we make comparisons in terms of noise reduction with other state of the art video denoising techniques.

Thirty human observers are tested in a visual detection task under the rating receiver operating characteristic (ROC) paradigm. Since most of the observers were not able to use the rating categories as instructed, their data are excluded from the analysis. From the 30 observers, the four best and most experienced are selected to do long experiments (with 1000 presentations). However, the assumption of Gaussians with equal variance of classical ROC theory is not satisfied by the new data. Nevertheless, assuming Gaussian underlying distributions with unequal variances, the three well-known indices of performance d_m, d_e, and d_x are calculated in terms of the geometry of the linear fits in normal deviated coordinates. The areas under the ROC curves are also calculated. We found that the power-law ROC, which assumes underlying exponential distributions, fit well our data. Under the exponential-exponential assumption, we derive a new performance (detectability) index that is easily calculated from experimental data and that depends on a single parameter. The obtained values of the proposed index are in agreement with the corresponding values of the d_m, d_e, and d_x indices, but with the new index it is much easier to discriminate between observers with similar performance with considerably lower uncertainty.

The effectiveness of a thresholding algorithm strongly depends on the image statistical characteristics. In a completely unsupervised context, this makes it difficult to choose the most appropriate algorithm to binarize a given image. This issue is considered through a novel thresholding strategy based on the fusion of an ensemble of different thresholding algorithms and formulated within a Markov random field (MRF) framework. The obtained experimental results suggest that in general the fusion of an ensemble of thresholding algorithms leads to a robust thresholding system, and in particular the proposed MRF strategy represents an effective solution to carry out the fusion process.

A new image segmentation algorithm that uses the simulation of a charged fluid is developed. Conceptually, a charged fluid consists of charged elements, each of which exerts a repelling electric force on the others. The charged fluid behaves like a liquid such that it flows through and around different obstacles. The boundary of the segmented object is determined by the image gradient, which is modeled as potential wells that stop the propagating front. The simulation is evolved in two steps that are governed by Poisson's equation. The first step distributes the elements of the charged fluid along the propagating interface until an electrostatic equilibrium is achieved. The second step advances the propagating front of the charged fluid such that it deforms into a new shape in response to the equilibrium electric potential and the image potential. The procedure is repeated until the propagating front resides on the boundary of objects being segmented. The electric potential of the simulated system is rapidly calculated using the finite-size particle (FSP) method implemented via the fast Fourier transform (FFT) algorithm. Experimental results using phantom images, photographic pictures, and medical images demonstrate the utility of this new algorithm in a wide variety of image processing applications.

The problem of separating linear features from a textured background is of importance in many applications. It has been shown that the Fourier transform can be used in conjunction with polar transformation to "lift" linear features from the background texture. However, while the Fourier transform works well with lines that are spread throughout the entire image, it is less effective when the linear features are of varied length and thickness. We propose approaches based on a windowed Fourier transform and wavelet packet decomposition to lift randomly located lines of varied lengths and thickness. The reasoning underlying the development of the approaches is presented along with comparative examples.

We deal with the problem of time-efficient extraction of structural features in a large class of structural texture images. The proposed approach of multiscale morphological texture modeling describes explicitly and concisely both shape and intensity parameters in the structural texture model. The modeling is based on a morphological skeletal representation of structural texture cells as objects of interest and the genomic growth of a texture region starting from a seed cell. This representation offers the advantage of concise description of texture cells as compared to the existing edge-based or contour-based approaches. A computationally efficient estimation of the structural texture parameters for texture segmentation tasks is proposed. The model parameter estimation and subsequent feature extraction rely on cell localization and scale-based locally adaptive binarization of the localized cells using isotropic matched filtering. The multiscale isotropic matched filter (MIMF) provides a scale- and orientation-invariant detection of structural cells regarded as multiple objects of interest in texture regions. Results of experiments pertaining to the parameter estimation from synthetic and real texture images as well as the segmentation of texture regions based on structural features are also provided.

Palette images are widely used on the World Wide Web (WWW) and in game-cartridge applications. Many images used on the WWW are stored and transmitted after they are compressed losslessly with the standard graphics interchange format (GIF), or portable network graphics (PNG). Well-known 2-D compression schemes, such as JPEG-LS and JPEG-2000, fail to yield better compression than GIF or PNG due to the fact that the pixel values represent indices that point to color values in a look-up table. To improve the compression performance of JPEG-LS and JPEG-2000 techniques, several researchers have proposed various reindexing algorithms. We investigate various compression techniques for color palette images. We propose a new technique comprised of a traveling salesman problem (TSP)-based reindexing scheme, Burrows-Wheeler transformation, and inversion ranks. We show that the proposed technique yields better compression gain on average than all the other 1-D compressors and the reindexing schemes that utilize JPEG-LS or JPEG-2000.

We propose an original technique to reduce the number of colors contained in an image. This method uses the "baker's transformation," which obtains a statistically suitable mixture of the pixels of the image. From this mixture, we can extract several samples, which present the same characteristics as the initial image. The concept we imagine is to consider these samples as potential pallets of colors. These pallets make it possible to do an adaptive quantization of the effective number of colors. We consider, and we put in competition, three methods to obtain a single pallet. We present the baker's transformation and we present methods to have a single pallet. The results illustrate the good visual quality reached by the quantized images. Finally, we present a comparison between our method and three classical methods of quantization.

An efficient compression strategy is indispensable for 3-D digital echocardiography, which can provide more accurate diagnostic information than 2-D echocardiography without geometric assumption, but unfortunately requires a huge storage space. We describe a new set partitioning in hierarchical trees (SPIHT) algorithm that is based on the wavelet packet transform and use it to compress 3-D echocardiographic images. The new algorithm originates from the well-known SPIHT algorithm that is based on a wavelet transform but outperforms it for the compression of 3-D echocardiographic images. Experimental results are presented to verify the effectiveness of the new SPIHT algorithm. Results show that the new algorithm achieves a very high compression ratio while retaining good quality 3-D echocardiographic images.

Digital still color video images play an important role in a wireless endoscopy system. It is vital to efficiently compress the medical images with high quality in such systems to decrease communication bandwidth and save the power in the capsule. We present a novel low-complexity and high-efficiency image compression algorithm for digital color image sensors with Bayer color filter arrays (CFAs), which is applied in the wireless endoscopy system we designed. The proposed algorithm includes three parts: lossless, near-lossless, and lossy compression. In the near-lossless compression part, the average compression rate is 3.5 bits/pixel with an image quality [peak SNR (PSNR)] greater than 46.37 dB when seven standard color images are compressed. Furthermore, the average compression rate can reach 2.18 bits/pixel with a PSNR larger than 46.89 dB when digestive tract images are compressed. In addition, the algorithm can provide lossless compression for the region of interest (ROI) and high-quality compression for other regions. A new lossy compression structure and a lowpass filter are applied in this lossy compression algorithm to achieve high compression performance. The proposed lossless, near-lossless, and lossy compression algorithm can provide lower compression rates in (bits per pixel) and lower complexities than any conventional interpolation-first algorithms and other existing similar compression-first algorithms.

The possibility of perceptual compression using live eye tracking has been anticipated for some time by many researchers. Among the challenges of real-time eye-gaze-based perceptual video compression are how to handle the fast nature of eye movements with the relative complexity of video transcoding and also take into account the delay associated with transmission in the network. Such a delay requires additional consideration in perceptual encoding because it increases the size of the area that requires high-quality coding. We present a hybrid scheme, one of the first to our knowledge, that combines eye tracking with fast in-line scene analysis to drastically narrow the high acuity area without the loss of eye-gaze containment.

With the rapid growth of multimedia technology, more and more multimedia content is disseminated in networks or stored in databases. Image data is one of the multimedia types to be seen or accessed by users on the Internet or from databases. Searching the related images by querying image content is helpful for the management and usage of an image database. Therefore, research on image indexing techniques is an important topic. We propose an efficient content-based image retrieval (CBIR) system. Basically, the proposed method is a discrete cosine transform (DCT)-coefficient-based technique that extracts content features using some DCT coefficients. In addition, our method also uses entropy to classify images in the database so that it can reduce the search space to decrease the processing time. The proposed system has the property of robustness to rotation, translation, cropping, noise corruption, etc. The indexing time is only about 4 to 10% compared to most recently published results. According to our experiment results, the system is highly efficient in terms of robustness, precision, and a processing speed.

We present a two-in-one visual cryptography (VC) scheme that not only shares an image of moderate confidentiality between two noisy transparencies, but also hides in these two transparencies a more confidential text file describing the image. None of the transparencies alone can reveal anything about the image or text. Later, people can view the image by simply stacking the two transparencies; on the other hand, after certain simple computations, the more confidential text data can also be extracted. We also introduce an alternative version in which the decoding of both the image and text requires no computer.

Error concealment (EC) in video communication is becoming increasingly important because of the growing interest in video delivery over unreliable channels such as wireless networks. We propose a spatial EC technique for intracoded frames in video. The proposed algorithm utilizes a small neighborhood of correctly received pixels to find the directions of edges passing through the missing block. The missing block is then restored by multidirectional interpolation along these directions. Experimental results demonstrate that this method is much more efficient than the existing methods and is computationally simple.

This PDF file contains the editorial “Introduction to Color Imaging Science” for JEI Vol. 15 Issue 02