This paper describes a computer vision system based on active IR illumination for real-time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using generalized regression neural networks (GRNNs). With GRNNs, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. To further improve the gaze estimation accuracy, we employ a hierarchical classification scheme that deals with the classes that tend to be misclassified. This leads to a 10% improvement in classification error. The angular gaze accuracy is about 5° horizontally and 8° vertically. The effectiveness of our gaze tracker is demonstrated by experiments that involve gaze-contingent interactive graphic display.

Eye and gaze tracking for interactive graphic display

With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

/pdf/brief-review-of-image-denoising-techniques-itrqcyj2rg.pdf

Brief review of image denoising techniques

This paper presents an optimized watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The singular values of a binary watermark are embedded in singular values of the LL3 sub-band coefficients of the host image by making use of multiple scaling factors (MSFs). The MSFs are optimized using a newly proposed Firefly Algorithm having an objective function which is a linear combination of imperceptibility and robustness. The PSNR values indicate that the visual quality of the signed and attacked images is good. The embedding algorithm is robust against common image processing operations. It is concluded that the embedding and extraction of the proposed algorithm is well optimized, robust and show an improvement over other similar reported methods.

Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm

https://www.ecse.rpi.edu/~cvrl/zhiwei/html/papers/cviueyetracking.pdf

Robust real-time eye detection and tracking under variable lighting conditions and various face orientations

A review on CT image noise and its denoising

Removing noise from the original signal is still a challenging problem for researchers. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper presents a review of some significant work in the area of image denoising. After a brief introduction, some popular approaches are classified into different groups and an overview of various algorithms and analysis is provided. Insights and potential future trends in the area of denoising are also discussed.

/pdf/survey-of-image-denoising-techniques-30yvbbargx.pdf

Survey of Image Denoising Techniques

This paper describes a robust method for discriminating 3D face pose (face orientation) from a video sequence featuring views of a human head under variable lighting and facial expression conditions. The wavelet transform is used to decompose the image into multiresolution face images containing both spatial and spatial-frequency information. Principal component analysis (PCA) is used to project a low-resolution sub-band face pose onto a pose eigen-space where the first 3 eigen coefficients are found most sensitive to pose and follow a trajectory as the pose changes. Any unknown pose of an query image can then be estimated by finding the Euclidean distance of the first 3 eigen coefficients of the query image from the estimated trajectory. The wavelet transform reduces the computational load on the PCA and makes the algorithm robust against illumination changes and facial expression. An accuracy of 84% was obtained for test images unseen during training under different environment conditions, facial expressions, and even different human subjects.

3D face pose discrimination using wavelets

Watermarking algorithms have a basic requirement that the watermark amplitude should be as high as possible for robustness and at the same time the watermark should not introduce any perceptible artifacts. Thus, the design of watermarking algorithms involves a tradeoff between imperceptibility and robustness. This paper proposes a novel state of the art algorithm, which is based on wavelet and fuzzy logic, to determine an optimal value for the watermark amplitude to be inserted in a 3D model. The system being adaptive to the local geometry of the mesh inserts an 8 bit grey scale image as watermark as compared to inserting a binary image in existing algorithms. Simulation results prove it to be robust against smoothing, cropping, affine operations and noise attacks.

/pdf/adaptive-fuzzy-watermarking-for-3d-models-26vo8fqit2.pdf

Adaptive Fuzzy Watermarking for 3D Models

An adaptive watermarking method based on the human visual system model and the fuzzy inference system in wavelet domain is proposed. Fuzzy logic is used for data fusion and builds a HVS model for spatial masking in wavelet domain. Modeling spatial masking is a complicated task and there is no single theoretical formulation to precisely compute the perceptual value for a corresponding wavelet coefficient. Fuzzy logic is used for data fusion and operates on the HVS model for spatial masking in wavelet domain. The fuzzy input variables (brightness, luminosity, texture) are computed for each wavelet coefficient in the image. The output of the fuzzy system is a single value which gives a perceptual value for each corresponding wavelet coefficient. The fuzzy based watermarks are robust to attacks and at the same time achieve a high level of imperceptibility.

/pdf/fuzzy-perceptual-watermarking-for-ownership-verification-2uq45ubsio.pdf

Fuzzy Perceptual Watermarking For Ownership Verification.

In this paper we suggest a novel method which is robust and efficient in extracting eye windows using Wavelets and Neural Networks. Wavelet analysis is used as a pre-processor for a back propagation neural network with conjugate gradient learning. The inputs to the neural network are the wavelet maxima neighborhood coefficients of face images at a particular scale. The output of the neural network is the classification of the input into an eye or non-eye region. An accuracy of 88% is observed for test images under different environment conditions not included during training.

/pdf/eye-detection-using-wavelets-and-ann-54etjhs71q.pdf

Mukesh C. Motwani

Papers

Survey of Image Denoising Techniques

3D face pose discrimination using wavelets

Adaptive Fuzzy Watermarking for 3D Models

Fuzzy Perceptual Watermarking For Ownership Verification.

Eye Detection using Wavelets and ANN