Image compression using a stochastic competitive learning algorithm (scola)

We introduce a new stochastic competitive learning algorithm (SCoLA) and apply it to vector quantization for image compression. In competitive learning, the training process involves presenting, simultaneously, an input vector to each of the competing neurons, which then compare the input vector to their own weight vectors and one of them is declared the winner based on some deterministic distortion measure. Here a stochastic criterion is used for selecting the winning neuron, whose weights are then updated to become more like the input vector. The performance of the new algorithm is compared to that of frequency-sensitive competitive learning (FSCL); it was found that SCoLA achieves higher peak signal-to-noise ratios (PSNR) than FSC

Novel image enhancement technique using shunting inhibitory cellular neural networks

This paper describes a method for improving image quality in a color CMOS image sensor. The technique simultaneously acts to compress the dynamic range, reorganize the signal to improve visibility, suppress noise, identify local features, achieve color constancy, and lightness rendition. An efficient hardware architecture and a rigorous analysis of the different modules are presented to achieve high quality CMOS digital camera

Classification of bandlimited fsk4 and fsk8 signals

This paper compares two types of classifiers applied to bandlimited FSK4 and FSK8 signals. The first classifier employs the decision-theoretic approach and the second classifier is a neural network structure. Key features are extracted using a zero crossing sampler. A novel decision tree is proposed and optimum threshold values are found for the decision theoretic approach. For the neural network, the optimum structure is found to be the smallest structure to give 100% overall success rate. The performance of the both classifiers has been evaluated by simulating bandlimited FSK4 and FSK8 signals corrupted by Gaussian noise. It is shown that the neural network outperforms the decision-theoretic approach particularly for SN

An improved SVD-based wall clutter mitigation method for through-the-wall radar imaging

This paper presents an improved SVD-based method for wall clutter mitigation in through-the-wall radar imaging. The dominant wall singular components are identified from the singular value spectrum. A subspace projection method is then applied to remove the strong wall clutter, residing in the dominant singular components, and separate the target signal from noise. The remaining wall clutter residual, which is mixed with the target signal, is suppressed by segmenting the range profile of the signal residing in the subspace orthogonal to the wall and noise subspaces. A Gaussian mixture is used to model the range profile, and the optimum segmentation threshold is found by minimizing the Bayes error. Experiments results show that the proposed method is more effective at reducing wall clutter and preserving the targets than some of the existing wall clutter mitigation methods

A wide dynamic range cmos imager with extended shunting inhibition image processing capabilities

A CMOS imager based on a novel mixed-mode VLSI implementation of biologically inspired shunting inhibition vision models is presented. It can achieve a wide range of image processing tasks such as image enhancement or edge detection via a programmable shunting inhibition processor. Its most important feature is a gain control mechanism allowing local and global adaptation to the mean input light intensity. This feature is shown to be very suitable for wide dynamic range imager

Skin color detection for face localization in human-machine communications

This paper presents the proposed user interface design for computers whereby users can navigate in a 3D graphics scene and change camera viewpoint via head movement. This human-machine communication relies very much on the performance of its face localization module, which must determine head pose and track head movement. We have employed the skin color detection approach to face localization. The approach is studied and presented. The experimental results show that our chosen methodology is very effective. Furthermore, we demonstrate that skin color detection approach can cope with the variations of skin color and lighting condition

Special Libraries, November 1962

Volume 53, Issue 9https://scholarworks.sjsu.edu/sla_sl_1962/1008/thumbnail.jp

Pedestrian sensing using time-of-flight range camera

This paper presents a new approach to detect pedestrians using a time-of-flight range camera, for applications in car safety and assistive navigation of the visually impaired. Using 3-D range images not only enables fast and accurate object segmentation and but also provides useful information such as distances to the pedestrians and their probabilities of collision with the user. In the proposed approach, a 3-D range image is first segmented using a modified local variation algorithm. Three state-of-the-art feature extractors (GIST, SIFT, and HOG) are then used to find shape features for each segmented object. Finally, the SVM is applied to classify objects into pedestrian or non-pedestrian. Evaluated on an image data set acquired using a time-of-flight camera, the proposed approach achieves a classification rate of 95.0%

Pedestrian lane detection for assistive navigation of blind people

Navigating safely in outdoor environments is a challenging activity for vision-impaired people. This paper is a step towards developing an assistive navigation system for the blind. We propose a robust method for detecting the pedestrian marked lanes at traffic junctions. The proposed method includes two stages: regions of interest (ROI) extraction and lane marker verification. The ROI extraction is performed by using colour and intensity information. A probabilistic framework employing multiple geometric cues is then used to verify the extracted ROI. The experimental results have shown that the proposed method is robust under challenging illumination conditions and obtains superior performance compared to the existing methods. © 2012 ICPR Org Committee

Video classification based on spatial gradient and optical flow descriptors

Feature point detection and local feature extraction are the two critical steps in trajectory-based methods for video classification. This paper proposes to detect trajectories by tracking the spatiotemporal feature points in salient regions instead of the entire frame. This strategy significantly reduces noisy feature points in the background region, and leads to lower computational cost and higher discriminative power of the feature set. Two new spatiotemporal descriptors, namely the STOH and RISTOH are proposed to describe the spatiotemporal characteristics of the moving object. The proposed method for feature point detection and local feature extraction is applied for human action recognition. It is evaluated on three video datasets: KTH, YouTube, and Hollywood2. The results show that the proposed method achieves a higher classification rate, even when it uses only half the number of feature points compared to the dense sampling approach. Moreover, features extracted from the curvature of the motion surface are more discriminative than features extracted from the spatial gradient