Illumination invariant face recognition

Few of the face recognition methods reported in the literature are capable of recognising faces under varying illumination conditions. The paper discusses a method which can achieve a higher recognition rate than those obtained for existing methods. The novelty of this new method is the use of an embossing technique to process a face image before presenting it to a standard face recognition system. Using a large database of face images, the performance of the proposed method is evaluated by comparing it against the performances of three existing methods. The experimental results demonstrate the successfulness of the proposed method

Segmentations of through-the-wall radar images

In this paper, we examine the use of image segmentation approaches for target detection in TWRI. The betweenclass variance thresholding, entropy-based segmentation, and Kmeans clustering are applied to segment target and clutter regions. Real 2D polarimetric images are used to demonstrate that simple histogram-based segmentation methods produce either comparable or improved performance over the Likelihood Ratio Tests (LRT) detector. Specifically, the results show that, for the cases considered, the entropy-based segmentation outperforms the other image segmentation methods and the LRT detector

Multi-resolution Mean-Shift Algorithm for Vector Quantization

The generation of stratified codebooks, providing a subset of vectors at different scale levels, has become necessary with the emergence of embedded coder/decoder for scalable image and video formats. We propose an approach based on mean-shift, invoking the multi-resolution framework to generate codebook vectors. Applied to the entire image, mean-shift is slow because it requires each sample to converge to a mode of the distribution. The procedure can be sped up with three simple assumptions: kernel truncation, code attraction and trajectory attraction. Here we propose to apply the mean-shift algorithm to the four image subbands generated by a DWT, namely the LL, LH, HL and HH subbands. It can be concluded from experimental results that the proposed MR-MS achieves similar PSNR to the LBG algorithm but outperforms it in terms of computation time

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors

A Pyramidal Neural Network For Visual Pattern Recognition

In this paper, we propose a new neural architecture for classification of visual patterns that is motivated by the two concepts of image pyramids and local receptive fields. The new architecture, called pyramidal neural network (PyraNet), has a hierarchical structure with two types of processing layers: Pyramidal layers and one-dimensional (1-D) layers. In the new network, nonlinear two-dimensional (2-D) neurons are trained to perform both image feature extraction and dimensionality reduction. We present and analyze five training methods for PyraNet [gradient descent (GD), gradient descent with momentum, resilient backpropagation (RPROP), Polak-Ribiere conjugate gradient (CG), and Levenberg-Marquadrt (LM)] and two choices of error functions [mean-square-error (mse) and cross-entropy (CE)]. In this paper, we apply PyraNet to determine gender from a facial image, and compare its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM)

Detecting People in Images: An Edge Density Approach

In this paper, we present a new method for detecting visual objects in digital images and video. The novelty of the proposed method is that it differentiates objects from non-objects using image edge characteristics. Our approach is based on a fast object detection method developed by Viola and Jones. While Viola and Jones use Harr-like features, we propose a new image feature - the edge density - that can be computed more efficiently. When applied to the problem of detecting people and pedestrians in images, the new feature shows a very good discriminative capability compared to the Harr-like features

Image quality assessment using a neural network approach

In this paper, we propose a neural network approach to image quality assessment. In particular, the neural network measures the quality of an image by predicting the mean opinion score (MOS) of human observers, using a set of key features extracted from the original and test images. Experimental results, using 352 JPEG/JPEG2000 compressed images, show that the neural network outputs correlate highly with the MOS scores, and therefore, the neural network can easily serve as a correlate to subjective image quality assessment. Using 10-fold cross-validation, the predicted MOS values have a linear correlation coefficient of 0.9744, a Spearman ranked correlation of 0.9690, a mean absolute error of 3.75%, and an rms error of 4.77%. These results compare very favorably with the results obtained with other methods, such as the structural similarity index of Wang et al. [2004]