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Author |
Jaime Moreno; Xavier Otazu |
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Title  |
Image coder based on Hilbert scanning of embedded quadTrees |
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Conference Article |
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Year |
2011 |
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Data Compression Conference |
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470-470 |
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In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. |
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Admin @ si @ MoO2011b |
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2177 |
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Author |
Jaime Moreno; Xavier Otazu |
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Title |
Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder |
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Conference Article |
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Year |
2011 |
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IEEE International Conference on Multimedia and Expo |
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1-6 |
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In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder. |
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1945-7871 |
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978-1-61284-348-3 |
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ICME |
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Admin @ si @ MoO2011a |
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2176 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios |
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Title |
Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs |
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Year |
2012 |
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21st International Conference on Pattern Recognition |
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2664 - 2667 |
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We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint. |
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Tsukuba Science City, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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ADAS |
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Admin @ si @ RSL2012a; |
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2032 |
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Author |
Yasuko Sugito; Javier Vazquez; Trevor Canham; Marcelo Bertalmio |
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Title |
Image quality evaluation in professional HDR/WCG production questions the need for HDR metrics |
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Journal Article |
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Year |
2022 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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31 |
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5163 - 5177 |
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Measurement; Image color analysis; Image coding; Production; Dynamic range; Brightness; Extraterrestrial measurements |
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In the quality evaluation of high dynamic range and wide color gamut (HDR/WCG) images, a number of works have concluded that native HDR metrics, such as HDR visual difference predictor (HDR-VDP), HDR video quality metric (HDR-VQM), or convolutional neural network (CNN)-based visibility metrics for HDR content, provide the best results. These metrics consider only the luminance component, but several color difference metrics have been specifically developed for, and validated with, HDR/WCG images. In this paper, we perform subjective evaluation experiments in a professional HDR/WCG production setting, under a real use case scenario. The results are quite relevant in that they show, firstly, that the performance of HDR metrics is worse than that of a classic, simple standard dynamic range (SDR) metric applied directly to the HDR content; and secondly, that the chrominance metrics specifically developed for HDR/WCG imaging have poor correlation with observer scores and are also outperformed by an SDR metric. Based on these findings, we show how a very simple framework for creating color HDR metrics, that uses only luminance SDR metrics, transfer functions, and classic color spaces, is able to consistently outperform, by a considerable margin, state-of-the-art HDR metrics on a varied set of HDR content, for both perceptual quantization (PQ) and Hybrid Log-Gamma (HLG) encoding, luminance and chroma distortions, and on different color spaces of common use. |
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600.161; 611.007 |
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Admin @ si @ SVG2022 |
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3683 |
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Author |
Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez |
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Title |
Image rain removal and illumination enhancement done in one go |
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Journal Article |
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Year |
2022 |
Publication |
Knowledge-Based Systems |
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KBS |
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252 |
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109244 |
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Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement. |
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Sept 2022 |
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Elsevier |
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ISE; 600.157; 600.168 |
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Admin @ si @ WCS2022 |
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3744 |
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Author |
Susana Alvarez; Xavier Otazu; Maria Vanrell |
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Title |
Image Segmentation Based on Inter-Feature Distance Maps |
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Book Chapter |
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2005 |
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Frontiers in Artificial Intelligence and Applications, IOS Press, 131: 75–82 |
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CAT @ cat @ AOV2005 |
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569 |
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Author |
Yainuvis Socarras |
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Title |
Image segmentation for improving pedestrian detection |
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Report |
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2011 |
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CVC Technical Report |
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167 |
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Bellaterra (Spain) |
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Computer Vision Center |
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Master's thesis |
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ADAS; |
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Admin @ si @ Soc2011 |
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1933 |
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Author |
Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich |
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Title |
Image Segmentation in the Presence of Shadows and Highligts |
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Conference Article |
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2008 |
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10th European Conference on Computer Vision |
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5305 |
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1–14 |
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Marseille (France) |
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ECCV |
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CAT;CIC |
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CAT @ cat @ VVB2008b |
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1013 |
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Author |
Lluis Garrido; M.Guerrieri; Laura Igual |
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Title |
Image Segmentation with Cage Active Contours |
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Journal Article |
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2015 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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24 |
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12 |
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5557 - 5566 |
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Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates |
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In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. |
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1057-7149 |
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MILAB |
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no |
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Admin @ si @ GGI2015 |
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2673 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Image Vegetation Index through a Cycle Generative Adversarial Network |
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Conference Article |
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2019 |
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IEEE International Conference on Computer Vision and Pattern Recognition-Workshops |
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This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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Long beach; California; USA; June 2019 |
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CVPRW |
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MSIAU; 600.130; 601.349; 600.122 |
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Admin @ si @ SSV2019 |
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3272 |
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Author |
Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo |
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Title |
Image vignetting reduction via a maximization of fuzzy entropy |
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Conference Article |
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2017 |
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IEEE International Conference on Fuzzy Systems |
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In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view. |
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Napoles; Italia; July 2017 |
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FUZZ-IEEE |
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LAMP; 600.120 |
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Admin @ si @ LMG2017 |
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2972 |
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Author |
Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy |
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Conference Article |
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2018 |
Publication |
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
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11041 |
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Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification |
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Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems. |
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Granada; September 2018 |
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MICCAIW |
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IAM; 600.096; 600.075; 601.323; 600.145 |
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Admin @ si @ RSB2018b |
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3137 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti |
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Title |
Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control |
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T-UFFC |
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58 |
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1 |
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60-72 |
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3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging |
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Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals. |
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0885-3010 |
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IAM;ADAS |
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IAM @ iam @ HGG2011 |
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1546 |
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Author |
Aura Hernandez-Sabate; David Rotger; Debora Gil |
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Title |
Image-based ECG sampling of IVUS sequences |
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2008 |
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Proc. IEEE Ultrasonics Symp. IUS 2008 |
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1330-1333 |
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Longitudinal Motion; Image-based ECG-gating; Fourier analysis |
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Abstract |
Longitudinal motion artifacts in IntraVascular UltraSound (IVUS) sequences hinders a properly 3D reconstruction and vessel measurements. Most of current techniques base on the ECG signal to obtain a gated pullback without the longitudinal artifact by using a specific hardware or the ECG signal itself. The potential of IVUS images processing for phase retrieval still remains little explored. In this paper, we present a fast forward image-based algorithm to approach ECG sampling. Inspired on the fact that maximum and minimum lumen areas are related to end-systole and end-diastole, our cardiac phase retrieval is based on the analysis of tissue density of mass along the sequence. The comparison between automatic and manual phase retrieval (0.07 ± 0.07 mm. of error) encourages a deep validation contrasting with ECG signals. |
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Beijing (China) |
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IAM;MILAB |
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IAM @ iam @ HRG2008 |
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1553 |
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Author |
Ivan Huerta |
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Title |
Image-Sequence Segmentation in Uncontrolled Environments |
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Report |
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2007 |
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CVC Technical Report #115 |
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CVC (UAB) |
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ISE @ ise @ Hue2007 |
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827 |
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