|
Pedro Martins, Paulo Carvalho, & Carlo Gatta. (2012). Context Aware Keypoint Extraction for Robust Image Representation. In 23rd British Machine Vision Conference (100.pp. 1–100.12).
|
|
|
Josep M. Gonfaus, Theo Gevers, Arjan Gijsenij, Xavier Roca, & Jordi Gonzalez. (2012). Edge Classification using Photo-Geo metric features. In 21st International Conference on Pattern Recognition (pp. 1497–1500).
Abstract: Edges are caused by several imaging cues such as shadow, material and illumination transitions. Classification methods have been proposed which are solely based on photometric information, ignoring geometry to classify the physical nature of edges in images. In this paper, the aim is to present a novel strategy to handle both photometric and geometric information for edge classification. Photometric information is obtained through the use of quasi-invariants while geometric information is derived from the orientation and contrast of edges. Different combination frameworks are compared with a new principled approach that captures both information into the same descriptor. From large scale experiments on different datasets, it is shown that, in addition to photometric information, the geometry of edges is an important visual cue to distinguish between different edge types. It is concluded that by combining both cues the performance improves by more than 7% for shadows and highlights.
|
|
|
Francesco Ciompi. (2012). Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound (Petia Radeva, & Oriol Pujol, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: In this thesis we tackle the problem of automatic characterization of human coronary vessel in Intravascular Ultrasound (IVUS) image modality. The basis for the whole characterization process is machine learning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes (ECOC) framework is used as central element for the design of multi-class classifiers.
Two main topics are tackled in this thesis. First, the automatic detection of the vessel borders is presented. For this purpose, a novel context-aware classifier for multi-class classification of the vessel morphology is presented, namely ECOC-DRF. Based on ECOC-DRF, the lumen border and the media-adventitia border in IVUS are robustly detected by means of a novel holistic approach, achieving an error comparable with inter-observer variability and with state of the art methods.
The two vessel borders define the atheroma area of the vessel. In this area, tissue characterization is required. For this purpose, we present a framework for automatic plaque characterization by processing both texture in IVUS images and spectral information in raw Radio Frequency data. Furthermore, a novel method for fusing in-vivo and in-vitro IVUS data for plaque characterization is presented, namely pSFFS. The method demonstrates to effectively fuse data generating a classifier that improves the tissue characterization in both in-vitro and in-vivo datasets.
A novel method for automatic video summarization in IVUS sequences is also presented. The method aims to detect the key frames of the sequence, i.e., the frames representative of morphological changes. This novel method represents the basis for video summarization in IVUS as well as the markers for the partition of the vessel into morphological and clinically interesting events.
Finally, multi-class learning based on ECOC is applied to lung tissue characterization in Computed Tomography. The novel proposed approach, based on supervised and unsupervised learning, achieves accurate tissue classification on a large and heterogeneous dataset.
|
|
|
Karel Paleček, David Geronimo, & Frederic Lerasle. (2012). Pre-attention cues for person detection. In Cognitive Behavioural Systems, COST 2102 International Training School (pp. 225–235). LNCS. Springer Berlin Heidelberg.
Abstract: Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.
|
|
|
Joan Arnedo-Moreno, & Agata Lapedriza. (2010). Visualizing key authenticity: turning your face into your public key. In 6th China International Conference on Information Security and Cryptology (pp. 605–618). LNCS.
Abstract: Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process.
|
|
|
Petia Radeva, Michal Drozdzal, Santiago Segui, Laura Igual, Carolina Malagelada, Fernando Azpiroz, et al. (2012). Active labeling: Application to wireless endoscopy analysis. In High Performance Computing and Simulation, International Conference on (pp. 174–181).
Abstract: Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”.
|
|
|
Cristhian Aguilera, M.Ramos, & Angel Sappa. (2012). Simulated Annealing: A Novel Application of Image Processing in the Wood Area. In Marcos de Sales Guerra Tsuzuki (Ed.), Simulated Annealing – Advances, Applications and Hybridizations (pp. 91–104).
|
|
|
Miguel Oliveira, V.Santos, & Angel Sappa. (2012). Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition. In IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles.
|
|
|
Pedro Martins, Paulo Carvalho, & Carlo Gatta. (2012). Stable Salient Shapes. In International Conference on Digital Image Computing: Techniques and Applications.
|
|
|
Rui Hua, Oriol Pujol, Francesco Ciompi, Marina Alberti, Simone Balocco, J. Mauri, et al. (2012). Stent Strut Detection by Classifying a Wide Set of IVUS Features. In Computed Assisted Stenting Workshop.
|
|
|
Adela Barbulescu, Wenjuan Gong, Jordi Gonzalez, Thomas B. Moeslund, & Xavier Roca. (2012). 3D Human Pose Estimation Using 2D Body Part Detectors. In 21st International Conference on Pattern Recognition (pp. 2484–2487).
Abstract: Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as support vector machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additional input data, such as silhouettes, or controlled camera settings. We present a framework that is capable of estimating the 3D pose of a person from single images or monocular image sequences without requiring background information and which is robust to camera variations. The framework models the non-linearity present in human pose estimation as it benefits from flexible learning approaches, including a highly customizable 2D detector. Results on the HumanEva benchmark show how they perform and influence the quality of the 3D pose estimates.
|
|
|
Jaime Moreno, & Xavier Otazu. (2011). Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder. In IEEE International Conference on Multimedia and Expo (pp. 1–6).
Abstract: 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.
|
|
|
Jaime Moreno, & Xavier Otazu. (2011). Image coder based on Hilbert scanning of embedded quadTrees. In Data Compression Conference (p. 470).
Abstract: 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.
|
|
|
Fadi Dornaika, & Bogdan Raducanu. (2012). Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation. In S.E. Carter (Ed.), Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions (pp. 157–178). NOVA Publishers.
|
|
|
Jordi Roca, Maria Vanrell, & C. Alejandro Parraga. (2012). What is constant in colour constancy? In 6th European Conference on Colour in Graphics, Imaging and Vision (pp. 337–343).
Abstract: Color constancy refers to the ability of the human visual system to stabilize
the color appearance of surfaces under an illuminant change. In this work we studied how the interrelations among nine colors are perceived under illuminant changes, particularly whether they remain stable across 10 different conditions (5 illuminants and 2 backgrounds). To do so we have used a paradigm that measures several colors under an immersive state of adaptation. From our measures we defined a perceptual structure descriptor that is up to 87% stable over all conditions, suggesting that color category features could be used to predict color constancy. This is in agreement with previous results on the stability of border categories [1,2] and with computational color constancy
algorithms [3] for estimating the scene illuminant.
|
|