Hand Geometry- and Palmprint-Based Biometric System with Image Deblurring

Motion blur is common in video tracking and detection, and severe motion blur can lead to failure in tracking and detection. In this work, a motion-blur hysteresis phenomenon (MBHP) was discovered, which has an impact on tracking and detection accuracy as well as image annotation. In order to accurately quantify MBHP, this paper proposes a motion-blur dataset construction method based on a motion-blur operator (MBO) generation method and self-similar object images, and designs APSF, a MBO generation method. The optimized sub-pixel estimation method of the point spread function (SPEPSF) is used to demonstrate the accuracy and robustness of the APSF method, showing the maximum error (ME) of APSF to be smaller than others (reduced by 86%, when motion-blur length > 20, motion-blur angle = 0), and the mean square error (MSE) of APSF to be smaller than others (reduced by 65.67% when motion-blur angle = 0). A fast image matching method based on a fast correlation response coefficient (FAST-PCC) and improved KCF were used with the motion-blur dataset to quantify MBHP. The results show that MBHP exists significantly when the motion blur changes and the error caused by MBHP is close to half of the difference of the motion-blur length between two consecutive frames. A general flow chart of visual tracking displacement detection with error compensation for MBHP was designed, and three methods for calculating compensation values were proposed: compensation values based on inter-frame displacement estimation error, SPEPSF, and no-reference image quality assessment (NR-IQA) indicators. Additionally, the implementation experiments showed that this error can be reduced by more than 96%.

Motion blur is one of the most common causes of image degradation. Restoration of such images is highly dependent on accurate estimation of motion blur parameters. To estimate these parameters, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present a novel algorithm to estimate direction and length of motion blur, using Radon transform and fuzzy set concepts. The most important advantage of this algorithm is its robustness and precision in noisy images. This method was tested on a wide range of different types of standard images that were degraded with different directions (between and ) and motion lengths (between and pixels). The results showed that the method works highly satisfactory for SNR dB and supports lower SNR compared with other algorithms.

This paper proposes a technique for restoring the motion blurred images. Restoration of blurred images is very important problem in tracking and identification of criminals, where image of a human face or number plate of a running vehicle taken in hit and run situation gets blurred due to relative motion between the imaging system and object/face. For restoration of motion blurred images, knowledge of the point spread function (PSF) is very important. The motion blur PSF is characterized by two parameters, namely blur direction and blur length. This paper also presents a method to identify the parameters of the PSF from the blurred and noisy images using the log spectrum of the blurred images. These parameters are used to restore the images. The experimental results demonstrate that the image can successfully be restored from the image with substantial amount of natural and artificial blur.

An image frame in image sequences, in general, suffers from degradation due to spatially varying motions. In this paper, we propose a new image degradation model for space-variant motion blur and a spatially adaptive image restoration algorithm to remove such motion blur. For the proposed image degradation model, we mathematically analyze boundary effect which arises on the border of two image segments with different motions. We extend the already proposed model for a moving object in an arbitrary direction. In order to represent the point spread function (PSF) for motion blur in an arbitrary direction, we develop a method which distributes energy of samples in the PSF into the neighboring integer grids. In order to remove the above mentioned motion blur, we propose an object-based adaptive regularized image restoration algorithm. Both in synthetically and naturally motion blurred images, the proposed image restoration algorithm gives acceptable performance in removing motion blur and, as a result, in restoring important features, such as numbers and characters, which cannot be recognized in the input blurred image.

A computational camera uses a combination of optics and processing to produce images that cannot be captured with traditional cameras. Over the last decade, a range of computational cameras have been proposed, which use various optics designs to encode and use computation to decode useful visual information. What is often missing, however, is the quantitative analysis of the relation between camera design and the captured visual information, and little systematic work has been done to evaluate and optimize these computational camera designs. While the optics of computational cameras may be quite complicated, many of them can be effectively characterized by their point spread functions (PSFs): the intensity distribution on an image sensor as a response to a point light source in a scene. This thesis explores the techniques to characterize, evaluate and optimize computational cameras via PSF engineering for computer vision tasks, including image recovery, 3D reconstruction, and image refocusing. I first explore PSF engineering techniques to recover image details from blurry images. Image blur is a problem commonly seen in photography due to a number of reasons, including defocus, lens aberration, diffraction, atmospheric turbulence, object motion, and etc. Image blur is often formulated as a convolution of the latent blur-free image and a PSF, and deconvolution (or deblurring) techniques have to be used to recover image details from a blurry region. Here, I propose a comprehensive framework of PSF evaluation for the purpose of image deblurring, in which the effects of image noise, deblurring algorithm, and the structure of natural images are all taken into account. In defocus blur, the shape of defocus PSF is largely determined by the aperture pattern of the camera lens. By using an evaluation criterion derived from the comprehensive framework, I optimize the aperture pattern to preserve many more image details when defocus occurs. Both through simulations and experiments, I demonstrate the significant improvement gained by using optimized aperture patterns. While defocus blur causes a loss in image detail, it also encodes depth information of scenes in images. A typical depth from defocus (DFD) technique computes depth from two or more images that are captured with circular aperture lenses of different focus settings. Circular apertures produce circular defocus PSFs. In this thesis, I show that the use of a circular aperture severely restricts the accuracy of DFD, and propose a comprehensive framework of PSF evaluation for depth recovery. With this framework, we can derive a criterion for evaluating a pair of apertures with respect to the precision of depth recovery. This criterion is optimized using a genetic algorithm and gradient descent search to arrive at a pair of high resolution apertures. The two coded apertures are found to complement each other in the scene frequencies they preserve. With this property it becomes possible to not only recover depth with greater fidelity but also to obtain a high quality all-focused image from the two defocused images. While depth recovery can significantly benefit from optimized aperture patterns, its overall performance is rigidly limited by the lens aperture's physical size. To transcend this limitation, I propose a novel depth recovery technique using an optical diffuser - referred to as depth from diffusion (DFDiff). I show that DFDiff is analogous to conventional DFD, in which the scatter angle of the diffuser determines the system's effective aperture. High precision depth estimation can be achieved by choosing a proper diffuser and no longer requires the large lenses that DFD requires. Even a consumer camera with a low-end small lens can be used to do high-precision depth estimation when coupled with an optical diffuser. In my detailed analysis of the image formation properties of a DFDiff system, I show a number of examples demonstrating greater precision in depth estimation when using DFDiff. While the finite depth of field (DOF) of a lens camera leads to defocus blur, it also produces artistic visual experience. Many of today's displays are interactive in nature, which opens up a possibility for new kind of visual representations. Users could, for example, interactively refocus images to different depths, so that they can experience the artistic narrow DOF images while simultaneously making available the image detail for the entire image. To enable image refocusing, one typical approach is to capture the entire light field. But this method has the drawback of a significant sacrifice in spatial resolution due to the dimensionality gap: the captured information (light field) is 4D, while the required information (focal stack) is only 3D.In this thesis, I present an imaging system that directly captures focal stacks by a sweeping focal plane. First, I describe how to synchronize focus sweeping with image capturing so that the summed DOF of a focal stack efficiently covers the entire depth range. Then, I take a customized algorithm to enable a seamless refocusing experience, even in textureless regions or with moving objects. Prototype cameras are presented to capture real space-time focal stacks. There is also an interactive refocusing viewer available online at www.focalsweep.com.

A self-adaptive learning method for motion blur kernel estimation of the single image

The problem of blind estimation of motion blur parameters from a single image is addressed. The blur direction and extent of motion-blurred image, which are introduced by relative motion between a camera and its object scene, are needed in the methods of image restoration, such as blind deconvolution. As an extension to the fractional-order derivative, a noncausal fractional-order directional derivative operator is devised, which is robust to noise. Based on this new operator, a novel method identifying blur parameters is developed in this work. The performance comparison between the proposed method and the state-of-the-art method is also presented, demonstrating that the former provides better immunity to noise and capacity to identify motion blur extent, especially for large blur length.

The focal spot size of x-ray tubes as well as the pixel size and scintillator thickness limit the spatial resolution of projection images as they result in blurring and degradation of the system’s point spread function. Deblurring of those images has been a topic of research for several decades. However, it is not solved in general. In this manuscript the application of a convolutional neural network for the deblurring of x-ray projection images is presented and compared to a standard deblurrig technique. The advantages of the neural network in terms of image quality and applicability are demonstrated with simulations and measurements originating from table top and gantry based micro-CT systems.

Linear systems theory combined with Fourier transform techniques provides a powerful means to characterize electro-optical systems. We present the description and analysis of a particular scanning IR system whose characteristic Point Spread Function (PSF) is space-variant. This peculiarity of the PSF arises from the scanning geometry; the IR sensor spins as it drops toward the ground. A system impulse response, or Point Spread Function (PSF), is derived from effects including diffraction, motion blur, chromatic aberration and detector area. Other effects may be taken into account by cascading PSF's. Once the system PSF is determined, the output of the system is the convolution of the input irradiance with the PSF. Alternatively, the output is the familiar inverse Fourier transform of the product of transforms. However, the space-variance of the PSF makes the latter option impossible and the former difficult. The convolution progresses in a spiral manner, with the PSF width changing continuously. The method is tested using Fortran programs and a digital image processor. The digital images produced show expected results, revealing a marked increase in resolution as the sensor nears the ground.

The motion blur simulation technique is widely used in remote sensing of an image chain simulation. However, the traditional method, which models the motion blur through a point spread function (PSF), is not precise enough when the imaging area is rugged or the motion of the platform is unstable. A physically based simulation model of motion blur is proposed. The model uses an image motion vector (IMV) field to describe the relative motion presented on the image plane during the exposure time. Based on the IMV field, the opto-electrons blurring model is built to simulate the blurring effect. A physical experiment was made to validate the model. The experiment result demonstrates that the simulation result generated by the model provided is more precise than the traditional PSF method, and a more complex motion status can be pre- sented by the proposed method. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.8.083539)

Motion blur is a common degradation which usually brings difficulties to subsequent image process and analysis of a machine vision system. In this paper, an effective restoration scheme is proposed for high-overlapped image sequence with composite motion blurs which refer to the blurs caused during the exposure time by dual relative motions among the background of the scene, the moving objects and the scanning camera, resulting in additional local blurs in the global motion blurred image. Taking full advantages of high-overlapping ratios between neighboring frames, the global motion is estimated first to differentiate the local motion blurred region from the global blurred background. The local motion is then estimated from the extracted local motion blurred region. And the space-variant Point Spread Functions (PSFs) are calculated respectively based on different motion estimation results as the initiations for the Richardson-Lucy-based sectional restorations. A series of experiments and comparisons are carried out to testify the validity of the proposed method. The restoration results show that the proposed method can enhance the image quality effectively. 

In this paper, we propose a novel algorithm for the optical flow estimation of a moving object by using the motion blur in a scene. The proposed technique is applied to the visual tracking system for the object moving on the 2D plane that is perpendicular to the axis of lens of a camera. To estimate the motion parameters, we use the motion blur, which has been treated as noises under the conventional method. And we use the feature of motion blur in the frequency domain. The first step is the estimation of orientation of motion, which is achieved by computing the log power spectrum of a local image patch, and operating the coordinate transformation. The second step is the estimation of velocity by using the cepstral analysis. It is achieved by detecting the period of sine function relating to the length of motion blur. In order to evaluate the feasibility of the proposed algorithm, we investigate the accuracy of the estimated optical flow with artificial blurred images and real images. And we apply the proposed algorithm to the tracking vision system, which consists of a camera performing the pan motion, a video processing board, and a standard PC. Through some experiments, we show that the proposed technique is advantageous to the high-speed-tracking system.

Biometric recognition through 3D ultrasound hand geometry

Human iris is one of the most reliable biometrics because of its uniqueness, stability and noninvasive nature.Thus it has attracted the attention of biometrics based identification and verification research and development community.Iris recognition has been a fast growing, challenging and interesting area in real-time applications.A large number of iris recognition algorithms have been developed for decades.This paper presents a comparative study of the performance from the iris authentication using Discrete Cosine Transform (DCT), Haar wavelet transform and a proposed Column Mean Method based features.Here iris recognition is done using the image feature set extracted from DCT, Haar Wavelet transform and Column Mean.Analysis was performed with the mentioned methods, consisting of the False Acceptance Rate (FAR) and the Genuine Acceptance Rate (GAR).The proposed technique is tested on an iris image database having 384 images.The iris recognition systems that produce very low error rates were successfully designed.DCT and Column Mean Methods give better performance with the accuracy of 98.44% and Haar Wavelet Transform gives a performance with the accuracy of 97.66%.The proposed Column Mean Method is superior in having faster recognition rate.

Among various factors degrading captured image quality, motion blur is the most common. The user becomes aware of the blur only after viewing pictures on a higher resolution display. The causes of motion blur are object movement, camera shake, or any relative speed between the object and the camera. To avoid this problem, many anti-shaking or image stabilization techniques have been developed. However, a detecting mechanism for motion blur is still lacking. Hence, this paper will address some possible solutions and evaluate their performance. The purpose of a motion blur detector is to classify the digital image as blurred or clear and inform users. This function can supply information for users to decide to retake the picture immediately instead of turning the camera to playback mode to check. For achieving higher error tolerance and adaptation to different image capturing circumstances, a machine learning technique is employed. Different digital image processing schemes are explored to find the most discriminative features. Among the many machine techniques, Support Vector Machine (SVM) has been implemented. To achieve the best performance for SVM, inherent information extraction from motion blurred images is extremely important. Thus, several signal transformations including discrete Fourier, discrete cosine, and Radon transformation have been explored. A comparison of the performance of different feature vectors, kernel function, and parameters will also been addressed in this paper.