Real-Time Imaging最新文献

A new adaptive vector filter is proposed for impulse noise suppression and its relationship with the recent impulse reduction filters is investigated. The new filter detects outliers presented in the image through a novel neighborhood evaluation process, which significantly improves the accuracy of noise detection and detail preservation. The computational complexity of the new filter is very competitive. Its two parameters can be configured efficiently using online/offline optimization processes. Extensive simulations indicated that the new filter outperforms other prior-art methods in suppressing impulse noise in natural color images.

In this paper, a novel real-time 3D and color sensor for the mid-distance range (0.1–3 m) based on color-encoded structured light is presented. The sensor is integrated using low-cost of-the-shelf components and allows the combination of 2D and 3D image processing algorithms, since it provides a 2D color image of the scene in addition to the range data. Its design is focused on enabling the system to operate reliably in real-world scenarios, i.e. in uncontrolled environments and with arbitrary scenes. To that end, novel approaches for encoding and recognizing the projected light are used, which make the system practically independent of intrinsic object colors and minimize the influence of the ambient light conditions. The system was designed to assist and complement a face authentication system integrating both 2D and 3D images. Depth information is used for robust face detection, localization and 3D pose estimation. To cope with illumination and pose variations, 3D information is used for the normalization of the input images. The performance and robustness of the proposed system is tested on a face database recorded in conditions similar to those encountered in real-world applications.

Color quantization is the process of reducing the number of colors in an image. That is, color quantization maps a large number of colors into a much smaller number of representative colors while keeping color distortion to an acceptable level. The reduction in the number of colors lowers computational complexity associated with color processing, and achieves higher color image compression for storage and transmission purposes. The existing color quantization methods require that the number of representative or prominent colors be specified by the user. This paper presents a scene-adaptive color quantization method which eases this constraint by determining the number of representative colors automatically. This method utilizes the discrete wavelet transform to achieve a computationally efficient implementation of the multi-scale clustering algorithm in a 3D color space. The performance is evaluated in terms of compression ratio or number of representative colors, color distortion, and computational complexity. It is shown that the developed method outperforms the popular color quantization methods in terms of color distortion.

In this paper, the problem of impulsive noise reduction in multichannel images is addressed. A new filter is proposed on the basis of a recently introduced family of computationally attractive filters with a good detail-preserving ability (FSVF). FSVF is based on privileging the central pixel in each filtering window in order to replace it only when it is really noisy and preserve the original undistorted image structures. The new filter is based on a novel fuzzy metric and it is created by combining the mentioned scheme and the fuzzy metric. The use of the fuzzy metric makes the filter computationally simpler and it allows to adjust the privilege of the central pixel giving the filter an adaptive nature. Moreover, it is shown that the new filter outperforms the classical-order statistics filtering techniques and its performance is similar to FSVF, outperforming it in some cases.

This paper presents a simple, fast coding technique for lossless compression of mosaic video data. The design of a video codec needs to strike a balance between the compression performance and the codec throughput. Aiming to make the encoding throughput high enough for real-time lossless video compression, we propose a hybrid scheme of inter and intraframe coding. Interframe predictive coding is invoked only when the motion between adjacent frames is modest and a simple motion compensation operation can significantly improve the compression performance. Otherwise, still frame compression is performed to keep the complexity low. Experimental results show that the proposed scheme achieves higher lossless video compression ratio than existing methods such as JPEG-LS and JPEG-2000.

This paper introduces a new implementation of wavelet packet decomposition which is combined with SPIHT (Set Partitioning in Hierarchical Trees) compression scheme. We provide the analysis of the problems arising from the application of zerotree quantisation based algorithms (such as SPIHT) to wavelet packet transform coefficients. We established the generalized parent–child relationships for wavelet packets, providing complete tree structures for SPIHT. The proposed algorithm can be used for both wavelet dyadic and Wavelet Packet decomposition (WP-SPIHT). An extensive evaluation of the algorithm was performed and it has been shown that WP-SPIHT significantly outperforms base-line SPIHT coder for texture images. For these images the suboptimal WP cost-function enables good enough energy compaction that is efficiently exploited by the WP-SPIHT.

In this paper, the real-time segmentation of surgical instruments with color images used in minimally invasive surgery is addressed. This work has been developed in the scope of the robotized laparoscopic surgery, specifically for the detection and tracking of gray regions and accounting for images of metallic instruments inside the abdominal cavity. With this environment, the moving background due to the breathing motion, the non-uniform and time-varying lighting conditions and the presence of specularities are the main difficulties to overcome. Then, to achieve an automatic color segmentation suitable for robot control, we developed a technique based on a discriminant color feature with robustness capabilities with respect to intensity variations and specularities. We also designed an adaptive region growing with automatic region seed detection and a model-based region classification, both dedicated to laparoscopy. The foreseen application is a good training ground to evaluate the proposed technique and the effectiveness of this work has been demonstrated through experimental results with endoscopic image sequences to efficiently locate boundaries of a landmark-free needle-holder at half the video-rate.

In this paper an efficient procedure for 3D digital curve smoothing is presented. It is described by linear operators which allow to perform the constrained, position invariant, least-squares smoothing of 3D digital curves minimizing the undersampling, digitizing and quantizing error and to calculate various curve characteristics and invariants related to the original digitized curve. They are represented by sparse symmetric circulant Toeplitz matrices with integer coefficients which can be efficiently realized in serial as well as in parallel manner.

A new secret sharing scheme suitable for encrypting color images is introduced. The scheme, which can be viewed as a cost-effective, private-key cryptosystem, encrypts the secret color image into two color shares with dimensions identical to those of the original secret input. Cryptographic operations performed at the bit levels are used to alter both the spectral correlation among the RGB color components and the spatial correlation of the neighboring color vectors in the secret image. The original image is decrypted with perfect reconstruction using inverse logical operations which are applied to the noise-like color shares. The scheme can be used in secure transmission of digital imaging material over untrusted networks or it can serve as stand-alone image encryption solution.