Amar Aggoun

Analytical model of a three-dimensional integral image recording system that uses circular- and hexagonal-based spherical surface microlenses.

A mathematical model for a three-dimensional omnidirectional integral recording camera system that uses either circular- or hexagonal-based spherical surface microlens arrays is derived. The geometry of the image formation and recording process is fully described. Matlab is then used to establish the number of recorded micro-intensity distributions representing a single object point and their dependence on spatial position. The point-spread function for the entire optical process for both close and remote imaging is obtained, and the influence of depth on the point-spread dimensions for each type of microlens and imaging condition is discussed. Comparisons of the two arrangements are made, based on the illustrative numerical results presented.

A Novel Birefrigent Photonic Crystal Fiber Surface Plasmon Resonance Biosensor

A numerical analysis of a novel birefringent photonic crystal fiber (PCF) biosensor constructed on the surface plasmon resonance (SPR) model is presented in this paper. This biosensor configuration utilizes circular air holes to introduce birefringence into the structure. This PCF biosensor model shows promise in the area of multiple detection using HE^x₁₁ and HE^y₁₁ modes to sense more than one analyte. A numerical study of the biosensor is performed in two interrogation modes: amplitude and wavelength. Sensor resolution values with spectral interrogation yielded 5 × 10^-5 RIU (refractive index units) for HE^x₁₁ modes and 6 × 10^-5 RIU for HE^y₁₁ modes, whereas 3 × 10^-5 RIU for HE^x₁₁ modes and 4 × 10^-5 RIU for HE^y₁₁ modes are demonstrated for the amplitude interrogation.

Adaptive 3D-DCT compression algorithm for continuous parallax 3D integral imaging

Abstract Integral imaging is employed as part of a three-dimensional imaging system, allowing the display of full colour images with continuous parallax within a wide viewing zone. A significant quantity of data is required to represent a captured integral 3D image with high resolution. A lossy compression scheme has been developed based on the use of a 3D-DCT, which make possible efficient storage and transmission of such images, while maintaining all information necessary to produce a high quality 3D display. In this paper, a novel approach to the problem of compressing the significant quantity of data required to represent integral 3D images is presented. The algorithm is based on using a variable number of microlens images (or sub-images) in the computation of the 3D-DCT. It involves segmentation of the planar mean image formed by the mean values of the microlens images and it takes advantage of the high cross-correlation between the sub-images generated by the microlens array. The algorithm has been simulated on several integral 3D images. It was found that the proposed algorithm improves the rate-distortion performance when compared to baseline JPEG and previously reported 3D-DCT compression scheme with respect to compression ratio and subjective and objective image quality.

Immersive 3D Holoscopic Video System

We demonstrated a 3D holoscopic video system for 3DTV application. We showed that using a field lens and a square aperture significantly reduces the vignetting problem associated with a relay system and achieves over 95 percent fill factor. The main problem for such a relay system is the nonlinear distortion during the 3D image capturing, which can seriously affect the reconstruction process for a 3D display. The nonlinear distortion mainly includes lens radial distortion (intrinsic) and microlens array perspective distortion (extrinsic). This is the task of future work. Our results also show that the SS coding approach performs better than the standard HEVC scheme. Furthermore, we show that search and retrieval performance relies on the depth maps quality and that the multimodal fusion boosts the retrieval performance.

Compression of 3D Integral Images Using 3D Wavelet Transform

Integral imaging is a technique capable of displaying 3D images with continuous parallax in full natural color. It has been reported by many research groups and is becoming a viable alternative for 3D television. With the development of 3D integral imaging, image compression becomes mandatory for the storage and transmission of 3D integral images. In this paper, the use of the lifting scheme in the application of a 3D Wavelet Transform for the compression of 3D Integral Images is proposed. The method requires the extraction of different viewpoint images from an integral image. The 3D wavelet decomposition is computed by applying three separate 1D transforms along the coordinate axes of the given sequence of Viewpoint Images. The spatial wavelet decompositions on a single viewpoint and on the inter-viewpoint images are performed using the biorthogonal Cohen-Debauchies-Feauveau 9/7 and 5/3 filter banks, respectively. All the resulting wavelet coefficients from application of the 3D wavelet decomposition are arithmetic encoded. Simulations are performed on a set of different grey level 3D Integral Images using a uniform scalar quantizer with deadzone. The results for the average of the four intensity distributions are presented and compared with previous use of 2D DWT and 3D-DCT based schemes. It was found that the algorithm achieves better rate-distortion performance and reconstructs the images with much better image quality at very low bit rates.

Depth Mapping of Integral Images Through Viewpoint Image Extraction With a Hybrid Disparity Analysis Algorithm

Integral imaging is a technique capable of displaying 3D images with continuous parallax in full natural color. It is one of the most promising methods for producing smooth 3D images. Extracting depth information from integral image has various applications ranging from remote inspection, robotic vision, medical imaging, virtual reality, to content-based image coding and manipulation for integral imaging based 3D TV. This paper presents a method of generating a depth map from unidirectional integral images through viewpoint image extraction and using a hybrid disparity analysis algorithm combining multi-baseline, neighborhood constraint and relaxation strategies. It is shown that a depth map having few areas of uncertainty can be obtained from both computer and photographically generated integral images using this approach. The acceptable depth maps can be achieved from photographic captured integral images containing complicated object scene.

Light field geometry of a standard plenoptic camera

The Standard Plenoptic Camera (SPC) is an innovation in photography, allowing for acquiring two-dimensional images focused at different depths, from a single exposure. Contrary to conventional cameras, the SPC consists of a micro lens array and a main lens projecting virtual lenses into object space. For the first time, the present research provides an approach to estimate the distance and depth of refocused images extracted from captures obtained by an SPC. Furthermore, estimates for the position and baseline of virtual lenses which correspond to an equivalent camera array are derived. On the basis of paraxial approximation, a ray tracing model employing linear equations has been developed and implemented using Matlab. The optics simulation tool Zemax is utilized for validation purposes. By designing a realistic SPC, experiments demonstrate that a predicted image refocusing distance at 3.5 m deviates by less than 11% from the simulation in Zemax, whereas baseline estimations indicate no significant difference. Applying the proposed methodology will enable an alternative to the traditional depth map acquisition by disparity analysis.

A 3D Dct Compression Algorithm For Omnidirectional Integral Images

A compression scheme for omnidirectional integral image data is described which uses a three dimensional DCT to exploit the intra-sub-image correlation together with the horizontal and vertical inter-sub-image correlation, resulting in a very efficient de-correlation of the source intensity distribution. The nature of the recorded intensity distribution data with respect to redundancies present and the structure of the data representing the image object are investigated. A three dimensional scalar quantisation array is applied to the DCT coefficients, which are then entropy encoded by a Huffman-based coder. The results obtained after applying the 3D DCT based scheme to OII data are presented and discussed, and compared with simulations produced using the JPEG scheme

Speaker identification using multimodal neural networks and wavelet analysis

The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content. In this study, the authors designed and implemented a novel text-independent multimodal speaker identification system based on wavelet analysis and neural networks. Wavelet analysis comprises discrete wavelet transform, wavelet packet transform, wavelet sub-band coding and Mel-frequency cepstral coefficients (MFCCs). The learning module comprises general regressive, probabilistic and radial basis function neural networks, forming decisions through a majority voting scheme. The system was found to be competitive and it improved the identification rate by 15% as compared with the classical MFCC. In addition, it reduced the identification time by 40% as compared with the back-propagation neural network, Gaussian mixture model and principal component analysis. Performance tests conducted using the GRID database corpora have shown that this approach has faster identification time and greater accuracy compared with traditional approaches, and it is applicable to real-time, text-independent speaker identification systems.

Bit-level pipelined digit-serial array processors

A new architecture for high performance digit-serial vector inner product (VIP) which can be pipelined to the bit-level is introduced. The design of the digit-serial vector inner product is based on a new systematic design methodology using radix-2/sup n/ arithmetic. The proposed architecture allows a high level of bit-level pipelining to increase the throughput rate with minimum initial delay and minimum area. This will give designers greater flexibility in finding the best tradeoff between hardware cost and throughput rate. It is shown that sub-digit pipelined digit-serial structure can achieve a higher throughput rate with much less area consumption than an equivalent bit-parallel structure. A twin-pipe architecture to double the throughput rate of digit-serial multipliers and consequently that of the digit-serial vector inner product is also presented. The effect of the number of pipelining levels and the twin-pipe architecture on the throughput rate and hardware cost are discussed. A twos complement digit-serial architecture which can operate on both negative and positive numbers is also presented.