Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Efficient Method For Scratch Lines Noise Removal From Video

The digitalization and transfer of older films into high definition (HD) formats imply that high quality of restoration is necessary. Now a day�s Digital film restoration is an area under discussion of increasing interest to researchers and film archives alike. Old films, including cultural heritage masterpieces, are being digitally premastered and transferred into novel, higher quality formats and distributed through various means such as DVD, Blu-ray or HD pictures. Detection of Line scratches in old movies is a particularly difficult problem due to the variable spatiotemporal characteristics of this deficiency. Some of the main problems consist of sensitivity to noise and texture, and false detections due to thin vertical structures belonging to the scene. Automatic finding of image damaged regions is the key to automatic video image in-painting. Vertical scratches are the common damages in the old film. As the film is a collection of number of frames arrayed together to produce a motion sequence hence it becomes a lengthy and tedious work to process any video format in any manner. Normally if any scratch or noise generated on films it remains as it is on many frames in sequence in film which can be benefitted by the removal process by initially checking noise area on earlier slide. Hence proposed system is aimed at designing and developing of line scratches detection from old films and remove it. A line scratches detection algorithm based on edge detection is proposed. Edge detection is nothing but an image processing technique for finding the boundaries of objects inside images. The proposed algorithm first uses the operator which has the largest response to the vertical edge in Sobel operator to detect edges, and then uses canny operator to detect edges further. Third, we detect vertical lines in the image through probabilistic Hough transform. Finally, we obtain the true locations of the vertical lines scratches through morphology and width constraints. We contribute for removal of scratches using a new nonlinear continued fraction method dealing with both spatial and temporal information around the scratch is investigated in the restoration stage

Segmentation-assisted detection of dirt impairments in archived film sequences

A novel segmentation-assisted method for film dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal and multistage median filtering approaches and provides efficient and robust detection for a wide variety of test material

Sur la Restauration et l'Edition de Vidéo : Détection de Rayures et Inpainting de Scènes Complexes

The inevitable degradation of visual content such as images and films leads to the goal ofimage and video restoration. In this thesis, we look at two specific restoration problems : the detection ofline scratches in old films and the automatic completion of videos, or video inpainting as it is also known.Line scratches are caused when the film physically rubs against a mechanical part. This origin resultsin the specific characteristics of the defect, such as verticality and temporal persistence. We propose adetection algorithm based on the statistical approach known as a contrario methods. We also proposea temporal filtering step to remove false alarms present in the first detection step. Comparisons withprevious work show improved recall and precision, and robustness with respect to the presence of noiseand clutter in the film.The second part of the thesis concerns video inpainting. We propose an algorithm based on theminimisation of a patch-based functional of the video content. In this framework, we address the followingproblems : extremely high execution times, the correct handling of textures in the video and inpaintingwith moving cameras. We also address some convergence issues in a very simplified inpainting context.La degradation inévitable des contenus visuels (images, films) conduit nécessairementà la tâche de la restauration des images et des vidéos. Dans cetre thèse, nous nous intéresserons àdeux sous-problèmes de restauration : la détection des rayures dans les vieux films, et le remplissageautomatique des vidéos (“inpainting vidéo en anglais).En général, les rayures sont dues aux frottements de la pellicule du film avec un objet lors de laprojection du film. Les origines physiques de ce défaut lui donnent des caractéristiques très particuliers.Les rayures sont des lignes plus ou moins verticales qui peuvent être blanches ou noires (ou parfois encouleur) et qui sont temporellement persistantes, c’est-à-dire qu’elles ont une position qui est continuedans le temps. Afin de détecter ces défauts, nous proposons d’abord un algorithme de détection basésur un ensemble d’approches statistiques appelées les méthodes a contrario. Cet algorithme fournitune détection précise et robuste aux bruits et aux textures présentes dans l’image. Nous proposonségalement une étape de filtrage temporel afin d’écarter les fausses alarmes de la première étape dedétection. Celle-ci améliore la précision de l’algorithme en analysant le mouvement des détections spatiales.L’ensemble de l’algorithme (détection spatiale et filtrage temporel) est comparé à des approchesde la littérature et montre un rappel et une précision grandement améliorés.La deuxième partie de cette thèse est consacrée à l’inpainting vidéo. Le but ici est de remplirune région d’une vidéo avec un contenu qui semble visuellement cohérent et convaincant. Il existeune pléthore de méthodes qui traite ce problème dans le cas des images. La littérature dans le casdes vidéos est plus restreinte, notamment car le temps d’exécution représente un véritable obstacle.Nous proposons un algorithme d’inpainting vidéo qui vise l’optimisation d’une fonctionnelle d’énergiequi intègre la notion de patchs, c’est-à-dire des petits cubes de contenu vidéo. Nous traitons d’abord leprobl’‘eme du temps d’exécution avant d’attaquer celui de l’inpainting satisfaisant des textures dans lesvidéos. Nous traitons également le cas des vidéos dont le fond est en mouvement ou qui ont été prisesavec des caméras en mouvement. Enfin, nous nous intéressons à certaines questions de convergencede l’algorithme dans des cas très simplifiés

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Computer vision researchers are actively studying the use of video in traffic monitoring. TrafficMonitor uses a stationary calibrated camera to automatically track and classify vehicles on roadways. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and Flownet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. In this work, we discuss some state-of-the-art ways to estimating the speed of vehicles, locating vehicles, and tracking objects. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and Flownet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean IoU of "54.27 per cent" and an average fps of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second to "30.19" on GPU with such a mean IoU of "47.65%". Because the GPU was utilised "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance

NASA technology applications team: Applications of aerospace technology

Two critical aspects of the Applications Engineering Program were especially successful: commercializing products of Application Projects; and leveraging NASA funds for projects by developing cofunding from industry and other agencies. Results are presented in the following areas: the excimer laser was commercialized for clearing plaque in the arteries of patients with coronary artery disease; the ultrasound burn depth analysis technology is to be licensed and commercialized; a phased commercialization plan was submitted to NASA for the intracranial pressure monitor; the Flexible Agricultural Robotics Manipulator System (FARMS) is making progress in the development of sensors and a customized end effector for a roboticized greenhouse operation; a dual robot are controller was improved; a multisensor urodynamic pressure catherer was successful in clinical tests; commercial applications were examined for diamond like carbon coatings; further work was done on the multichannel flow cytometer; progress on the liquid airpack for fire fighters; a wind energy conversion device was tested in a low speed wind tunnel; and the Space Shuttle Thermal Protection System was reviewed