2,294 research outputs found
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
Color Image Enhancement via Combine Homomorphic Ratio and Histogram Equalization Approaches: Using Underwater Images as Illustrative Examples
The histogram is one of the important characteristics of grayscale images, and the histogram equalization is effective method of image enhancement. When processing color images in models, such as the RGB model, the histogram equalization can be applied for each color component and, then, a new color image is composed from processed components. This is a traditional way of processing color images, which does not preserve the existent relation or correlation between colors at each pixel. In this work, a new model of color image enhancement is proposed, by preserving the ratios of colors at all pixels after processing the image. This model is described for the color histogram equalization (HE) and examples of application on color images are given. Our preliminary results show that the application of the model with the HE can be effectively used for enhancing color images, including underwater images. Intensive computer simulations show that for single underwater image enhancement, the presented method increases the image contrast and brightness and indicates a good natural appearance and relatively genuine color
An evaluation of intrusive instrumental intelligibility metrics
Instrumental intelligibility metrics are commonly used as an alternative to
listening tests. This paper evaluates 12 monaural intrusive intelligibility
metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and
. In addition, this paper investigates the ability of
intelligibility metrics to generalize to new types of distortions and analyzes
why the top performing metrics have high performance. The intelligibility data
were obtained from 11 listening tests described in the literature. The stimuli
included Dutch, Danish, and English speech that was distorted by additive
noise, reverberation, competing talkers, pre-processing enhancement, and
post-processing enhancement. SIIB and HASPI had the highest performance
achieving a correlation with listening test scores on average of
and , respectively. The high performance of SIIB may, in part, be
the result of SIIBs developers having access to all the intelligibility data
considered in the evaluation. The results show that intelligibility metrics
tend to perform poorly on data sets that were not used during their
development. By modifying the original implementations of SIIB and STOI, the
advantage of reducing statistical dependencies between input features is
demonstrated. Additionally, the paper presents a new version of SIIB called
, which has similar performance to SIIB and HASPI,
but takes less time to compute by two orders of magnitude.Comment: Published in IEEE/ACM Transactions on Audio, Speech, and Language
Processing, 201
Characterisation of S185 steel under monotonic loading by a feature tracking method
In this work, the mechanical behaviour of S185 steel under monotonic loading was characterised by using an image feature-based tracking method. Tensile tests on three types of cylindrical specimens were carried out, in particularly using smooth and notched specimens. Target features were painted on the specimen surfaces and tracked in images sequences in order to estimate the experimental load-displacement curves. Finite element analyses of the non-linear behaviour of steel components were also performed, being the parameters of the different plasticity employed determined by fitting the experimental and numerical data
Dynamic Programming Approach to Image Segmentation and its Application to Pre-processing of Mammograms
Images egmentationis an importent componento f imagop rocessings irce significantt ime can be savedi f a region of interest is extracted by al efficient segmentationa lgorithm. A dynamic programming image segmentation algorithnr is presented. The algorithm is applicable to images with a large matrix of gray levels of pixel values and generatesa path separatingt he object from the background.T he report of a.na pplication of the proposed algorithm to digitised mammotramsc omplementsit s description
NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines
The recent progress in artificial intelligence has led to an ever-increasing
usage of images and videos by machine analysis algorithms, mainly neural
networks. Nonetheless, compression, storage and transmission of media have
traditionally been designed considering human beings as the viewers of the
content. Recent research on image and video coding for machine analysis has
progressed mainly in two almost orthogonal directions. The first is represented
by end-to-end (E2E) learned codecs which, while offering high performance on
image coding, are not yet on par with state-of-the-art conventional video
codecs and lack interoperability. The second direction considers using the
Versatile Video Coding (VVC) standard or any other conventional video codec
(CVC) together with pre- and post-processing operations targeting machine
analysis. While the CVC-based methods benefit from interoperability and broad
hardware and software support, the machine task performance is often lower than
the desired level, particularly in low bitrates. This paper proposes a hybrid
codec for machines called NN-VVC, which combines the advantages of an
E2E-learned image codec and a CVC to achieve high performance in both image and
video coding for machines. Our experiments show that the proposed system
achieved up to -43.20% and -26.8% Bj{\o}ntegaard Delta rate reduction over VVC
for image and video data, respectively, when evaluated on multiple different
datasets and machine vision tasks. To the best of our knowledge, this is the
first research paper showing a hybrid video codec that outperforms VVC on
multiple datasets and multiple machine vision tasks.Comment: ISM 2023 Best paper award winner versio
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