Motion Blur

Real-time perception systems for autonomous vehicles remain challenging due to the inherent trade-off between accuracy and computational efficiency. This paper presents DVNet-R, a real-time computer vision framework for autonomous vehicles incorporating optimized object detection and lane perception modules. The multi-stage architecture achieves 28.35 FPS on commodity hardware while maintaining high accuracy across diverse environmental conditions. Our system integrates a YOLOv8-based object detector with an 87.2% [email protected] and a hybrid lane detection pipeline with adaptive region-of-interest selection achieving 93.5% accuracy. We contribute a novel adaptive processing technique that dynamically allocates computational resources based on scene complexity. Comprehensive evaluation across five environmental conditions demonstrates robust performance, particularly in variable lighting and weather scenarios. The modular design allows for targeted optimizations, as validated through our ablation studies. DVNet-R’s differential processing approach reduces computational overhead by 23% compared to baseline methods while maintaining competitive accuracy. This research advances the state of the art in efficient perception systems for autonomous driving.

The paper presents an innovative approach that can assist survey methods by applying AI algorithms to improve the accuracy of point clouds generated from UAV images. Firstly, the work individually analyses several photogrammetric accuracy parameters, including reprojection error, angle of intersection between homologous points, number of cameras for single Tie Point calculation, verifying that a single parameter is not sufficient to filter noise from a photogrammetric point cloud. Therefore, some of the calculated parameters were analysed with the Self-Organizing Map (SOM) and a K-means, to check the impact of the precision parameters for reducing the noise associated with the definition of the 3D model. In the case study, in both machine learning clustering algorithms used, it was observed that the parameter that most influences noise in photogrammetric point clouds is the angle of intersection.

Contemporary digital platforms are frequently critiqued as engines of attention extraction and cultural homogenization. This article addresses a gap in platform studies by proposing a Bergsonian-Deleuzian framework to analyze digital media as sites of "affective flow." Adopting a meta-theoretical methodology grounded in philosophical hermeneutics, the study pursues a central research question: how do distinct digital media architectures engineer affective temporalities, and what are the ethical implications for global cultural circulation? It hypothesizes that platform designs mobilize Bergson's durée and Deleuze's affect images to manufacture qualitative experiences that exceed metric capture, and that cultural specificity functions as an affective intensifier rather than a barrier to global circulation. The study synthesizes empirical findings across three case studies: the algorithmic rhythm of TikTok, the haptic immersion of the VR narrative The Wolves in the Walls, and the curation of Indian streaming services. Key findings show how TikTok manufactures "synthetic duration," how VR employs "subtractive perception" to generate affective depth, and how Indian streaming platforms enable global resonance by preserving culturally specific temporalities beyond linguistic boundaries. Consequently, the article outlines an Ethical Design Praxis grounded in "subtraction," "multiplicity," and "resonance without erasure"-a roadmap for resisting cultural flattening while enabling embodied resonance.

The purpose of this project is mainly to support theoretical work which is still in progress. Two distinct approaches were made in reference to Deleuze’s writings on cinema. Both have been theoretically examined in a preliminary level. The first one which refers to the Time image is associated with film and the second one is Movement image which corresponds to Digital cinema.
The aim of the project is to investigate the mechanism through which the above mentioned distinction expresses itself in moving image practice. The film which will be used as the reference for this investigation is an older 16mm fiction film titled “Time’s up”. This film will act as a canvas that will initiate reworking of its structure in terms of Deleuzian logic in order to demonstrate the degree of dependency of human interaction with moving images on the medium itself and the various constituents that make up sensory reactions. In practical terms this requires a thorough examination of the concepts involved in moving images presented by an analogue film projection compared with the ones presented by digital projection.

Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market and little attention has been directed to the design of alternative CFAs in the literature. Recent works of Hirakawa et al. [1]-[4] introduced new insights in this field by focusing on the spectral properties of CFAs and operating directly in the Fourier domain. However, this new paradigm is generic and leaves open questions about the optimization of the many available parameters. In this work, we investigate the link between them and the light sensitivity and color discrimination capabilities of the CFA. Indeed, these characteristics determine the quality of the whole imaging pipeline, since they directly control its sensitivity to noise. By optimizing the key parameters, we obtain a class of new CFAs with optimal properties, in which the shortest 2×3 pattern shows up as the best compromise. Moreover, a simple and efficient linear demosaicking algorithm is associated to these CFAs, which fully exploits their spectral properties. Practical experiments confirm the superiority of our new design.

Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market and little attention has been directed to the design of alternative CFAs in the literature. Recent works of Hirakawa et al. [1]-[4] introduced new insights in this field by focusing on the spectral properties of CFAs and operating directly in the Fourier domain. However, this new paradigm is generic and leaves open questions about the optimization of the many available parameters. In this work, we investigate the link between them and the light sensitivity and color discrimination capabilities of the CFA. Indeed, these characteristics determine the quality of the whole imaging pipeline, since they directly control its sensitivity to noise. By optimizing the key parameters, we obtain a class of new CFAs with optimal properties, in which the shortest 2×3 pattern shows up as the best compromise. Moreover, a simple and efficient linear demosaicking algorithm is associated to these CFAs, which fully exploits their spectral properties. Practical experiments confirm the superiority of our new design.

Vector field rendering is difficult in 3D because the vector icons overlap and hide each other. We propose four different techniques for visualizing vector fields only near surfaces. The first uses motion blurred particles in a thickened region around the surface. The second uses a voxel grid to contain integral curves of the vector field. The third uses many antialiased lines through the surface, and the fourth uses hairs sprouting from the surface and then bending in the direction of the vector field. All the methods use the graphics pipeline, allowing real time rotation and interaction, and the first two methods can animate the texture to move in the flow determined by the velocity field.

In computer vision and image processing, image deblurring is a crucial phase that attempts to restore the sharpness of the image and clarity of images that have been damaged due to motion blur, defocus, or other factors. The multiple image deblurring methods are thoroughly investigated in this work, along with the performance evaluation metrics that were employed to evaluate each method's efficiency. We started by examining the most recent deblurring techniques, which include both deep learning-based and traditional algorithms. Every technique's advantages, disadvantages, and suitability are examined in different scenarios. Furthermore, we investigate the finer details of performance metrics that are frequently used to rate the quality of deblurred images. These metrics include subjective assessments from user research &objective measurements like PSNR and SSIM. This study aims to elucidate the benefits and drawbacks of current deblurring methods in diverse contexts through extensive experimentation. Moreover, we examine the applicability and consistency of performance evaluation metrics for evaluating the accuracy and perceived quality of deblurred images. By supporting researchers and professionals in identifying suitable techniques and assessment standards for their particular applications, the knowledge gained from this study improves the field of image deblurring.

Blur in images can occur due to low-quality cameras or as an intentional effect to emphasize movement or important objects. However, most blur classification models simply differentiate between blurred and sharp images, without distinguishing intentional blur from unintentional blur. Some unintentionally blurred images hold significant value and should not be discarded outright. This paper introduces a robust classifier that categorizes images into three types: sharp, intentional blur, and unintentional blur. The key idea behind distinguishing between intentional and unintentional blur is analyzing whether the blur affects a salient and semantically meaningful object. To achieve this, the approach integrates blur detection, saliency analysis, and semantic segmentation. The model utilizes spatial pyramid pooling to extract global features, followed by random forest classification. Additionally, Conditional Random Fields (CRF) are employed to identify unintentional blur pixels by incorporating multiple cues. The deblurred unintentional regions can then be merged back into the original image to reconstruct an intentionally blurred version. Experiments conducted on the UBICD dataset demonstrate the effectiveness of the proposed classification method and the potential of unintentional blur removal, achieving promising results.

A photographic shutter regulates the duration of light reaching a photosensitive surface. Modern digital cameras employ diverse mechanical, electronic, or hybrid shutter technologies to synchronise light admission with a sensor's capacity to collect and reset charge. To clarify longstanding terminological inconsistencies, this paper first introduces a systematic, four-dimensional framework for classifying camera shutters alongside standardised terminology for describing key temporal quantities in the image-formation pipeline. It then focuses on progressive (i.e., rolling) exposure mechanisms in CMOS-based cameras, where sequential row-integration results in intra-frame temporal offsets across the image sensor that distort image geometry during camera or subject motion. A simple experimental setup utilising a 1,000 Hz flickering LED enables direct measurement of these temporal offsets, revealing substantial delays in electronic rolling shutters compared to mechanical or hybrid rolling-blind focal-plane types. Here, these effects are described by refining the broadly adopted notion of sensor readout into the proposed concept of shutter transit time. Finally, real-world image-based 3D modelling experiments on architectural case studies in Vienna, Austria, highlight the impact of slow shutter transit times in two typical rolling-shutter sensors. Subsequent bundle adjustments in Agisoft Metashape Professional demonstrate that even imperceptible rolling-shutter artefacts can significantly degrade camera exterior orientation and 3D surface quality, although the use of compensation algorithms markedly improves results. Overall, the paper highlights that sensor readout architecture—rather than exposure duration alone—is a decisive factor in determining the geometric fidelity of image-based 3D surface reconstructions.

The goal of this study was to describe our initial experience with the deep-inspiration breath-hold (DIBH) technique in combined PET/CT of the thorax. This article presents particular emphasis on the technical aspects required for clinical implementation. In the DIBH technique, the patient is verbally coached and brought to a reproducible deep inspiration breath-hold level. The first "Hold" period, which refers to the CT session, is considered as the reference. This is followed by 9- to 20-s independent breath-hold PET acquisitions. The goal is to correct for respiratory motion artifacts and, consequently, improve the tumor quantitation and localization on the PET/CT images and inflate the lungs for possible improvement in the detection of subcentimeter pulmonary nodules. A physicist monitors and records patient breathing during PET/CT acquisition using a motion tracker. Patient breathing traces obtained during acquisition are examined on the fly to assess the reproducibil...

Cette thèse n"aurait pas été possible sans le bienveillant soutien de certaines personnes je ne suis pas non plus capable de dire avec les mots qui conviennent, le rôle qu"elles ont pu jouer à mes côtés pour en arriver là. Cependant, je voudrais les remercier du fond du coeur. Mes premiers remerciements vont d"abord à mon directeur de thèse, Dominique David, qui m"a accompagné tout au long de ma formation. Sa disponibilité et ses généreux secours au cours de certains moments difficiles ont été d"une très grande qualité, et d"un immense réconfort; merci infiniment ! Mes seconds remerciements vont à Laurent Alacoque, mon encadrant direct. Merci de m"avoir fait grandir dans la rigueur et l"argumentation scientifique. Puis mes remerciements vont à Antoine Dupret, notre encadrant des doctorants du laboratoire. Merci pour ta précieuse relecture de ce manuscrit et les suggestions d"amélioration. Merci à notre chef de laboratoire, Michael Tchagaspagnian pour ta confiance renouvelée depuis le début de cette thèse. Je remercie chaleureusement mes rapporteurs Jacques-Olivier Klein et Michel Paindavoine pour leurs relectures de ce manuscrit. Je remercie aussi les autres membres de mon jury Christian Jutten, Virginio Cantoni qui m"ont permis de passer la soutenance de thèse. Mes remerciements vont aussi à tous mes collègues du laboratoire, Tien, Fadoua,

Avec l’integration des appareils photos dans les appareils mobiles, leur democratisation et la reduction de la taille de l’imageur, de l’optique et de la taille pixels, les photos sont de plus en plus sujettes au flou de bouge du aux tremblements de la main. A cette tendance s’ajoute un accroissement constate dans l’exigence de qualite d’image de la part des utilisateurs. Pour reduire ce flou, des systemes de stabilisation d’image ont ete developpes. Neanmoins ceux-ci ne permettent pas de garantir la qualite de nettete des images et souffrent parfois d’une integration limitee. En reponse a ces limitations, ces travaux de recherche proposent, d’une part, un modele de tremblement physiologique permettant de simuler de maniere fidele les flous de bouge et, d’autre part, une etude sur la perception visuelle du flou permettant le developpement d’une metrique de qualite. Enfin des architectures de stabilisations, exploitant ces nouveaux outils, sont proposees. Ces nouvelles architectures ...

Nowadays, records of fatal driving are increasing. It has become an essential part of Intelligent Transport System to detect the fast moving vehicles to avoid fatal driving. Vehicle number plate recognition of fast-moving vehicles can be used to track and notify the driver about the violation of the traffic rules. Speedy vehicles may pose difficulty in capturing the license plate by the surveillance camera. The result is blurred unrecognizable image. To reconstruct the original imager, a Deconvolution practice known as Blind Image Deconvolution is proposed in which blur kernel is constructed using the parameters angle and length of blurred keyed in number plate. This system can construct the image which is unrecognizable by the human.

We present a method that detects stable keypoints from an event stream at high speed with a low memory footprint. Our key observation connects two points: It should be easier to reconstruct the image gradients rather than the image itself from the events, and the Harris corner detector, one of the most reliable keypoint detectors for short baseline regular images, depends on the image gradients, not the image. We therefore introduce a recurrent convolutional neural network to predict image gradients from events. As image gradients and events are correlated, this prediction task is relatively easy and we can keep this network very small. We train our network solely on synthetic data. Extracting Harris corners from these gradients is then very efficient. Moreover, in contrast to learned methods, we can change the hyperparameters of the detector without retraining. Our experiments confirm that predicting image gradients rather than images is much more efficient, and that our approach predicts stable corner points which are easier to track for a longer time compared to state-of-the-art event-based methods.

Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel. We propose a regression approach using convolutional neural networks (CNNs) to predict parameters of linear motion blur kernels, the length and orientation of the blur. We analyze the relationship between length and angle of linear motion blur that can be represented as digital filter kernels. A large dataset of blurred images is generated using a suite of blur kernels and used to train a regression CNN for prediction of length and angle of the motion blur. The coefficients of determination for estimation of length and angle are found to be greater than or equal to 0.89, even under the presence of significant additive Gaussian noise, up to a variance of 10% (SNR of 10 dB). Using our estimated kernel in a non-blind image deblurring method, the sum of squared differences error ratio demonstrates higher cumulative histogram values than comparison methods, with most test images yielding an error ratio of less than or equal to 1.25.

Segmentation of moving objects in dynamic scenes is a key process in scene understanding for both navigation and video recognition tasks. Without prior knowledge of the object structure and motion, the problem is very challenging due to the plethora of motion parameters to be estimated while being agnostic to motion blur and occlusions. Event sensors, because of their high temporal resolution, and lack of motion blur, seem well suited for addressing this problem. We propose a solution to multi-object motion segmentation using a combination of classical optimization methods along with deep learning and does not require prior knowledge of the 3D motion and the number and structure of objects. Using the events within a time-interval, the method estimates and compensates for the global rigid motion. Then it segments the scene into multiple motions by iteratively fitting and merging models using input tracked feature regions via alignment based on temporal gradients and contrast measures...

Biometrics systems accurately identify and distinguish individuals based on their characteristics. Due to the high accuracy and stability, iris has been widely used in the authentication process of authorized persons. One of the most important processes in developing an iris recognition system is iris segmentation since it has a substantial impact on the accuracy of iris matching. Although there are several influencing factors such as overlapping eyelashes, eyelids, and lighting, it must be the area of the iris that is precisely defined. Several attempts towards robust iris localization and segmentation have been made in light of these challenges. A robust iris localization method based on a circular Hough transform and an active contour model was presented in this study. The precisely extracted iris region, and its distinctive characteristics, are extracted using the Gray Level Co-occurrence matrix (GLCM) method, depending on different types of images, some of which were captured using near-infrared (NIR) under specific capture conditions. The other part of the pictures was taken with normal visible wavelength (VW). The co-occurrence matrix is used to estimate the Homogeneity, Energy, Contrast, Correlation, and Entropy collection of second-order statistical features. Experimental results on 100 images from CASIA v1, IITDv1, and UBIRIS v1 iris images databases show that our method achieves high accuracy and small time.

Image-based control or visual servoing is gaining popularity as cameras become key feedback measurement mechanisms. State estimation of motion systems through these image sensors is typically performed by studying the evolution of the centroid of an image feature in time. Any blur in the image feature is treated as undesirable noise and is discarded. We here propose an estimation scheme for fast rate systems with slow rate image sensor measurements that provides fast-rate estimates of the states by exploiting the additional dynamical information encoded in the image blur. Using the information from the blur for estimation provides two advantages (1) faster estimate convergence, and (2) robustness to noise in the camera (such as stray light). By modeling the image sensor as a non linear integral time-domain to pixel-domain functional we recast the estimation problem as a multi-rate estimation problem from non linear output measurements. Based on this formulation we discuss a progress...

This paper basically represents how to deblurring images using a wiener filter [1]. Basically wiener filter is used to produce an estimate of a desired or target random process by linear time-invariant filtering [2] of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process. In here it has been introduced about the image processing capabilities in MATHLAB. Dividing some steps it has been shown how an image can be processed with the help of MATHLAB [3][4] command. Moreover how image can be effected by noise & how noise can be removed from image this also described in this paper properly

This paper introduces a new learning-based approach to motion blur removal. A local linear motion model is first estimated at each pixel using a convolutional neural network (CNN) in a regression setting. These estimates are then used to drive an algorithm that casts non-blind, non-uniform image deblurring as a least-squares problem regularized by natural image priors in the form of sparsity constraints. This problem is solved by combining the alternative direction method of multipliers with an iterative residual compensation algorithm, with a finite number of iterations embedded into a second CNN whose trainable parameters are deconvolution filters. The second network outputs the sharp image, and the two CNNs can be trained together in an end-to-end manner. Our experiments demonstrate that the proposed method is significantly faster than existing ones, and provides competitive results with the state of the art on several synthetic and real datasets.

The original solution of the blur and blur parameters identification problem is presented in this paper. A neural network based on multi-valued neurons is used for the blur and blur parameters identification. It is shown that using simple single-layered neural network it is possible to identify the type of the distorting operator. Four types of blur are considered: defocus, rectangular, motion and Gaussian ones. The parameters of the corresponding operator are identified using a similar neural network. After a type of blur and its parameters identification the image can be restored using several kinds of methods.

Ray tracing is one of the most elegant techniques in computer graphics. Many phenomena that are difficult or impossible with other techniques are simple with ray tracing, including shadows, reflections, and refracted light. Ray directions, however, have been determined precisely, and this has limited the capabilities of ray tracing. By distributing the directions of the rays according to the analytic function they sample, ray tracing can incorporate fuzzy phenomena. This provides correct and easy solutions to some previously unsolved or partially solved problems, including motion blur, depth of field, penumbras, translucency, and fuzzy reflections. Motion blur and depth of field calculations can be integrated with the visible surface calculations, avoiding the problems found in previous methods.

Motion-onset visual evoked potentials (MO VEPs) are robust to dioptric blur when low contrast and low spatial frequency patterns are used for stimulation. To reveal mechanisms of MO VEPs robustness, we studied whether the resistance to defocus persists even when using a high-contrast checkerboard using digital defocus in the emmetropic eyes of 13 subjects (males 20-60 years). We compared the dominant components of MO VEPs to pattern-reversal VEPs (PR VEP), which are sensitive to the blur. For stimulation, we used checkerboard patterns with 15´ and 60´ checks. To defocus the checkerboard, we rendered it with a second-order Zernike polynomial (𝑍 2 0 ) with an equivalent defocus of 0, 2, or 4 D. For PR VEP, the checkerboards were reversed in terms of their contrast. To evoke MO VEP, the checkerboard of 60´ checks moved for 200 ms with a speed of 5 or 10 deg/s in the cardinal directions. The MO VEP did not change in peak time (P ≥ 0.0747) or interpeak amplitude (P > 0.0772) with digital blur. In contrast, for PR VEP, the results showed a decrease in interpeak amplitude (P ≤ 6.65ˑ10-4) and an increase in peak time (P ≤ 0.0385). Thus, we demonstrated that MO VEPs evoked by checkerboard, structure containing high spatial content, can be robust to defocus.

The well-established Modulation Transfer Function (MTF) is an imaging performance parameter that is well suited to describing certain sources of detail loss, such as optical focus and motion blur. As performance standards have developed for digital imaging systems, the MTF concept has been adapted and applied as the spatial frequency response (SFR). The international standard for measuring digital camera resolution, ISO 12233, was adopted over a decade ago. Since then the slanted edge-gradient analysis method on which it was based has been improved and applied beyond digital camera evaluation. Practitioners have modified minor elements of the standard method to suit specific system characteristics, unique measurement needs, or computational shortcomings in the original method. Some of these adaptations have been documented and benchmarked, but a number have not. In this paper we describe several of these modifications, and how they have improved the reliability of the resulting system evaluations. We also review several ways the method has been adapted and applied beyond camera resolution.

Adanya file gambar hasil scan tidak memiliki kualitas yang bagus(blur). Citra blur ini menyebabkan objek yang terdapat didalamnya menjadi tidak terlihat jelas. Tujuan dari penelitian ini adalah untuk meningkatkan kualitas citra pada dokumen dari hasil scan dengan melakukan deblurring dengan menggunakan algoritma Lucy Richardson Deconvolution. Teknik yang digunakan untuk menghilangkan kekaburan citra (deblurring), penapisan dengan merestorasi citra dengan menggunakan metode iteratif, yaitu algoritma Lucy-Richardson. Metode Lucy-Richardson dapat melakukan deblurring pada citra dengan nilai threshold 192. Maksimal blur yang bisa di deblurring adalah sampai dengan 50% blur pada citra menggunakan pengujian PSNR dengan nilai mininum 30dB. Penerapan metode Lucy-Richardson menggunakan 1 parameter (Threshold), sehingga lebih memudahkan untuk mendapatkan hasil citra yang paling optima

Taking smartphone-made videos for photogrammetry is a convenient approach because of the easy image collection process for the object being reconstructed. However, the video may contain a lot of relatively similar frames. Additionally, frames may be of different quality. The primary source of quality variation in the same video is varying motion blur. Splitting the sequence of the frames into chunks and choosing the least motion-blurred frame in every chunk would reduce data redundancy and improve image data quality. Such reduction will lead to faster and more accurate reconstruction of the 3D objects. In this research, we investigated image quality evaluation in the case of human 3D head modeling. Suppose a head modeling workflow already uses a convolutional neural network for the head detection task in order to remove non-static background. In that case, features from the neural network may be reused for the quality evaluation of the same image. We proposed a motion blur evaluatio...

We propose a fuzzy logic recursive scheme for motion detection and spatiotemporal filtering that can deal with the Gaussian noise and unsteady illumination conditions in both the temporal and spatial directions. Our focus is on applications concerning tracking and denoising of image sequences. We process an input noisy sequence with fuzzy logic motion detection to determine the degree of motion confidence. The proposed motion detector combines the membership of the temporal intensity changes, appropriately using fuzzy rules, where the membership degree of motion for each pixel in a 2-D sliding window is determined by a proposed membership function. Both the fuzzy membership function and the fuzzy rules are defined in such a way that the performance of the motion detector is optimized in terms of its robustness to noise and unsteady lighting conditions. We simultaneously perform tracking and recursive adaptive temporal filtering, where the amount of filtering is inversely proportional to the confidence in the existence of motion. Finally, temporally filtered frames are further processed by a proposed spatial filter to obtain a denoised image sequence. Our main contribution is a robust novel fuzzy recursive scheme for motion detection and temporal filtering. We evaluate the proposed motion detection algorithm using two criteria: (1) robustness to noise and to changing illumination conditions and (2) motion blur in temporal recursive denoising. Additionally, we make comparisons in terms of noise reduction with other state of the art video denoising techniques.

Image Processing is a vital segment of present-day advancements since human depends such a great amount on the visual data than many other data. The big challenge to researchers, engineers and specialists is to rapidly separate the significant data from the raw image data. Proposed strategy for vehicles statistic in complex urban movement scenes to determine insufficiencies of conventional checking strategies, which are profoundly computationally costly and turned out to be unsuccessful with expanding unpredictability in urban rush hour traffic situations. The special feature of the proposed system is it will give more accuracy in normal video and blurred video. This system first applies the de-convolution deblurring techniques on video frames improve the quality of the image and then vehicle counting process is invoked which apply background subtraction and segmentation technique to detect the vehicles. Test results of this system based on real-world urban traffic videos show that the accuracy rate of the proposed approach is very high.

Image Processing is a vital segment of present day advancements since human depends such a great amount on the visual data than many other data. The big challenge to researchers, engineers and specialists is to rapidly separate the significant data from the raw image data. Proposed strategy for vehicles statistic in complex urban movement scenes to determine insufficiencies of conventional checking strategies, which are profoundly computationally costly and turned out to be unsuccessful with expanding unpredictability in urban rush hour traffic situations. The special feature of the proposed system is it will give more accuracy in normal video and blurred video. This system first applies the deconvolution deblurring techniques on video frames improve the quality of the image and then vehicle counting process is invoked which apply background subtraction and segmentation technique to detect the vehicles. Test results of this system based on real-world urban traffic videos show that the accuracy rate of the proposed approach is very high.

Previous studies indicated that less motion smear is perceived when a physically stationary target is presented during voluntary eye movements than when similar retinal-image motion occurs during steady fixation. In this study, we assessed whether the perception of motion smear is attenuated also during the involuntary vestibulo-ocular reflex (VOR). Normal observers matched the length of perceived smear in two experimental conditions that were designed to produce similar trajectories of retinal image motion. In the fixation condition, a small bright target was presented for a duration of 50-200 ms in rightward or leftward motion, while the observer remained stationary and maintained fixation. In the VOR condition, the target moved along with the observer, who underwent full-body rotation around a vertical axis in darkness. Horizontal eye movement recordings during VOR trials allowed us to calculate the velocity of retinal image motion on each VOR trial. The principal result was that the extent of perceived motion smear was significantly less during VOR than fixation trials, particularly for target durations of 100 ms or longer. These findings support the conclusion that extra-retinal signals during the involuntary VOR contribute to a reduction of perceived motion smear.

Image sensors are typically characterized by slow sampling rates, which limit their efficacy in signal reconstruction applications. Their integrative nature though produces image blur when the exposure window is long enough to capture relative motion of the observed object relative to the sensor. Image blur contains more information on the observed dynamics than the typically used centroids, i.e., time averages of the motion within the exposure window. Parameters characterizing the observed motion, such as the signal derivatives at specified sampling instants, can be used for signal reconstruction through the derivative sampling extension of the known sampling theorem. Using slow image based sensors as derivative samplers allows for reconstruction of faster signals, overcoming Nyquist limitations. In this manuscript, we present an algorithm to extract values of a signal and its derivatives from blurred image measurements at specified sampling instants, i.e. the center of the exposur...

SummaryDeconvolution problems are naturally represented in the Fourier domain, whereas thresholding in wavelet bases is known to have broad adaptivity properties. We study a method which combines both fast Fourier and fast wavelet transforms and can recover a blurred function observed in white noise with O{n log (n)2} steps. In the periodic setting, the method applies to most deconvolution problems, including certain ‘boxcar’ kernels, which are important as a model of motion blur, but having poor Fourier characteristics. Asymptotic theory informs the choice of tuning parameters and yields adaptivity properties for the method over a wide class of measures of error and classes of function. The method is tested on simulated light detection and ranging data suggested by underwater remote sensing. Both visual and numerical results show an improvement over competing approaches. Finally, the theory behind our estimation paradigm gives a complete characterization of the ‘maxiset’ of the met...

Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the ...