Hybrid System for Robust Faces Detection

The automatic detection of faces is a very important problem. The effectiveness of biometric authentication based on face mainly depends on the method used to locate the face in the image. This paper presents a hybrid system for faces detection in unconstrained cases in which the illumination, pose, occlusion, and size of the face are uncontrolled. To do this, the new method of detection proposed in this paper is based primarily on a technique of automatic learning by using the decision of three neural networks, a technique of energy compaction by using the discrete cosine transform, and a technique of segmentation by the color of human skin. A whole of pictures （faces and no faces） are transformed to vectors of data which will be used for learning the neural networks to separate between the two classes. Discrete cosine transform is used to reduce the dimension of the vectors, to eliminate the redundancies of information, and to store only the useful information in a minimum number of coefficients while the segmentation is used to reduce the space of research in the image. The experimental results have shown that this hybridization of methods will give a very significant improvement of the rate of the recognition, quality of detection, and the time of execution.

FPGA Implementation of Approximate 2D Discrete Cosine Transforms

Discrete cosine transform (DCT) is frequently used in image and video signal processing due to its high energy compaction property. Humans are able to perceive and identify the information from slightly erroneous images. It is enough to produce approximate outputs rather than absolute outputs which in turn reduce the circuit complexity. Numbers of applications like image and video processing need higher dimensional DCT algorithms. So the existing architectures of one dimensional (1D) approximate DCTs are reviewed and extended to two dimensional (2D) approximate DCTs. Approximate 2D multiplier-free DCT architectures are coded in Verilog, simulated in Modelsim to evaluate the correctness, synthesized to evaluate the performance and implemented in virtexE Field Programmable Gate Array (FPGA) kit. A comparative analysis of approximate 2D DCT architectures is carried out in terms of speed and area.

Evaluation of asphalt mixture behavior incorporating warm mix additives and reclaimed asphalt pavement

Incorporation of warm mix asphalt （WMA） and reclaimed asphalt pavement （RAP） has benefited the asphalt industry in many ways such as reducing the demand for virgin materials, lowering energy requirement during the asphalt production and construction, in addition to reducing greenhouse-gas emissions. This study evaluated the effects of Evotherm and RH-WMA and RAP on mixtures＇ behavior in terms of the compaction energy index （CEI）, indirect tensile strength （ITS） and resilient modulus. The results showed that warm mix additives reduced the CEI, ITS and resilient modulus; while RAP increased the corresponding values. Statistical analysis showed that mixtures incorporating Evotherm and RAP had significant effects on CEI, while the effects of RH-WMA on the corresponding value were found to be statistically insignificant. General Linear Model showed that Evotherm, RAP and RH-WMA exhibited no significant effects on the ITS and resilient modulus. The one-way analysis of variance showed that Evotherm influenced mixture behavior significantly, while RAP and RH-WMA effects were found to be statistically insignificant. Regression equations with high accuracy levels were proposed to predict CEI, ITS and resilient modulus with respect to modification of mixture variables such as mix constituents （Evotherm, RH-WMA, and RAP） and compaction temperature.

Fast Adaptive Wavelet for Remote Sensing Image Compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.