Abstract:Line scratch detection in old films is a particularly challenging problem due to the variable spatiotemporal characteristics of this defect. Some of the main problems include sensitivity to noise and texture, and false detections due to thin vertical structures belonging to the scene. We propose a robust and automatic algorithm for frame-by-frame line scratch detection in old films, as well as a temporal algorithm for the filtering of false detections. In the frame-by-frame algorithm, we relax some of the hypo… Show more
“…Other works related to using local statistical gradient estimation 24 , overcome challenging problems such as line scratch detection in old films. By exploiting first- or second-order derivative features 25 , 26 , studied the mid-level representation of edges for generalized boundaries detection.…”
Section: Related Workmentioning
confidence: 99%
“…The deformation/rotation not only facilitates pixels having the same or similar orientations to be covered by the mask, but also help discriminate a target pixel from pixels that have dissimilar gradient orientations, ensuring resilience to noises and interferences 28 . In contrast to the prior works on perceptual contours, this study provides a unified approach that outperforms in tracking curved contours (vs. gestalt-based methods 7 , 10 , 11 , 28 ), less susceptible to noises (vs. gradient-based methods 12 , 24 – 27 ) more interpretable and mathematically rigorous (vs. deep learning methods 13 – 15 ), lower parameters uncertainty (vs. all prior arts). Figure 3 ( a ) Receptive field effective enlarged after a repetitive sequence of deform-and-rotate.…”
This paper presents a novel bio-inspired edge-oriented approach to perceptual contour extraction. Our method does not rely on segmentation and can unsupervised learn to identify edge points that are readily grouped, without invoking any connecting mechanism, into object boundaries as perceived by human. This goal is achieved by using a dynamic mask to statistically assess the inter-edge relations and probe the principal direction that acts as an edge-grouping cue. The novelty of this work is that the mask, centered at a target pixel and driven by EM algorithm, can iteratively deform and rotate until it covers pixels that best fit the Bayesian likelihood of the binary class w.r.t a target pixel. By creating an effect of enlarging receptive field, contiguous edges of the same object can be identified while suppressing noise and textures, the resulting contour is in good agreement with gestalt laws of continuity, similarity and proximity. All theoretical derivations and parameters updates are conducted under the framework of EM-based Bayesian inference. Issues of stability and parameter uncertainty are addressed. Both qualitative and quantitative comparison with existing approaches proves the superiority of the proposed method in terms of tracking curved contours, noises/texture resilience, and detection of low-contrast contours.
“…Other works related to using local statistical gradient estimation 24 , overcome challenging problems such as line scratch detection in old films. By exploiting first- or second-order derivative features 25 , 26 , studied the mid-level representation of edges for generalized boundaries detection.…”
Section: Related Workmentioning
confidence: 99%
“…The deformation/rotation not only facilitates pixels having the same or similar orientations to be covered by the mask, but also help discriminate a target pixel from pixels that have dissimilar gradient orientations, ensuring resilience to noises and interferences 28 . In contrast to the prior works on perceptual contours, this study provides a unified approach that outperforms in tracking curved contours (vs. gestalt-based methods 7 , 10 , 11 , 28 ), less susceptible to noises (vs. gradient-based methods 12 , 24 – 27 ) more interpretable and mathematically rigorous (vs. deep learning methods 13 – 15 ), lower parameters uncertainty (vs. all prior arts). Figure 3 ( a ) Receptive field effective enlarged after a repetitive sequence of deform-and-rotate.…”
This paper presents a novel bio-inspired edge-oriented approach to perceptual contour extraction. Our method does not rely on segmentation and can unsupervised learn to identify edge points that are readily grouped, without invoking any connecting mechanism, into object boundaries as perceived by human. This goal is achieved by using a dynamic mask to statistically assess the inter-edge relations and probe the principal direction that acts as an edge-grouping cue. The novelty of this work is that the mask, centered at a target pixel and driven by EM algorithm, can iteratively deform and rotate until it covers pixels that best fit the Bayesian likelihood of the binary class w.r.t a target pixel. By creating an effect of enlarging receptive field, contiguous edges of the same object can be identified while suppressing noise and textures, the resulting contour is in good agreement with gestalt laws of continuity, similarity and proximity. All theoretical derivations and parameters updates are conducted under the framework of EM-based Bayesian inference. Issues of stability and parameter uncertainty are addressed. Both qualitative and quantitative comparison with existing approaches proves the superiority of the proposed method in terms of tracking curved contours, noises/texture resilience, and detection of low-contrast contours.
“…Since the introductory illustration of a-contrario methods on alignment detection [10], some recent works developed the underlying idea [18,7], but a large interest developed around using this fundamental pattern grounded in the Gestalt continuity principle in order to detect related elements such as segments [29,30,1], vanishing points [4,17] or scratches [19]. Concomitantly, acontrario methods have been developed for detecting more complex patterns, such as circles and ellipses [2,22] as well as coherent clusterings in a broader sense [28,8,25,26,21,32].…”
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.
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