4,082 research outputs found
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
Scratches Removal in Digitised Aerial Photos Concerning Sicilian Territory
In this paper we propose a fast and effective method to detect and restore scratches in aerial photos from a
photographic archive concerning Sicilian territory. Scratch removal is a typical problem for old movie films but similar defects can be seen in still images. Our solution is based on a semiautomatic detection process and an unsupervised restoration algorithm. Results are comparable with those obtained with commercial restoration tools
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
Reevaluating Adversarial Examples in Natural Language
State-of-the-art attacks on NLP models lack a shared definition of a what
constitutes a successful attack. We distill ideas from past work into a unified
framework: a successful natural language adversarial example is a perturbation
that fools the model and follows some linguistic constraints. We then analyze
the outputs of two state-of-the-art synonym substitution attacks. We find that
their perturbations often do not preserve semantics, and 38% introduce
grammatical errors. Human surveys reveal that to successfully preserve
semantics, we need to significantly increase the minimum cosine similarities
between the embeddings of swapped words and between the sentence encodings of
original and perturbed sentences.With constraints adjusted to better preserve
semantics and grammaticality, the attack success rate drops by over 70
percentage points.Comment: 15 pages; 9 Tables; 5 Figure
Efficient Evaluation of the Number of False Alarm Criterion
This paper proposes a method for computing efficiently the significance of a
parametric pattern inside a binary image. On the one hand, a-contrario
strategies avoid the user involvement for tuning detection thresholds, and
allow one to account fairly for different pattern sizes. On the other hand,
a-contrario criteria become intractable when the pattern complexity in terms of
parametrization increases. In this work, we introduce a strategy which relies
on the use of a cumulative space of reduced dimensionality, derived from the
coupling of a classic (Hough) cumulative space with an integral histogram
trick. This space allows us to store partial computations which are required by
the a-contrario criterion, and to evaluate the significance with a lower
computational cost than by following a straightforward approach. The method is
illustrated on synthetic examples on patterns with various parametrizations up
to five dimensions. In order to demonstrate how to apply this generic concept
in a real scenario, we consider a difficult crack detection task in still
images, which has been addressed in the literature with various local and
global detection strategies. We model cracks as bounded segments, detected by
the proposed a-contrario criterion, which allow us to introduce additional
spatial constraints based on their relative alignment. On this application, the
proposed strategy yields state-of the-art results, and underlines its potential
for handling complex pattern detection tasks
Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive
The German Broadcasting Archive (DRA) maintains the cultural heritage of
radio and television broadcasts of the former German Democratic Republic (GDR).
The uniqueness and importance of the video material stimulates a large
scientific interest in the video content. In this paper, we present an
automatic video analysis and retrieval system for searching in historical
collections of GDR television recordings. It consists of video analysis
algorithms for shot boundary detection, concept classification, person
recognition, text recognition and similarity search. The performance of the
system is evaluated from a technical and an archival perspective on 2,500 hours
of GDR television recordings.Comment: TPDL 2016, Hannover, Germany. Final version is available at Springer
via DO
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework
Malaria is a potentially fatal plasmodium parasite injected by female
anopheles mosquitoes that infect red blood cells and millions worldwide yearly.
However, specialists' manual screening in clinical practice is laborious and
prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL)
framework, comprising the stacking of new Boosted-BR-STM convolutional neural
networks (CNN) and the ensemble ML classifiers, is developed to screen malaria
parasite images. The proposed Boosted-BR-STM is based on a new
dilated-convolutional block-based split transform merge (STM) and feature-map
Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and
boundary operations to learn the malaria parasite's homogeneity, heterogeneity,
and boundary with patterns. Furthermore, the diverse boosted channels are
attained by employing Transfer Learning-based new feature-map SB in STM blocks
at the abstract, medium, and conclusion levels to learn minute intensity and
texture variation of the parasitic pattern. The proposed DBEL framework
implicates the stacking of prominent and diverse boosted channels and provides
the generated discriminative features of the developed Boosted-BR-STM to the
ensemble of ML classifiers. The proposed framework improves the discrimination
ability and generalization of ensemble learning. Moreover, the deep feature
spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML
classifiers for comparative analysis. The proposed DBEL framework outperforms
the existing techniques on the NIH malaria dataset that are enhanced using
discrete wavelet transform to enrich feature space. The proposed DBEL framework
achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC
(0.997), which suggest it to be utilized for malaria parasite screening.Comment: 26 pages, 10 figures, 9 Table
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