Jorge Bernal, Joan M. Nuñez, F. Javier Sanchez, & Fernando Vilariño. (2014). Polyp Segmentation Method in Colonoscopy Videos by means of MSA-DOVA Energy Maps Calculation. In 3rd MICCAI Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging (Vol. 8680, pp. 41–49).
Abstract: In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation.
Keywords: Image segmentation; Polyps; Colonoscopy; Valley information; Energy maps
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Sandra Jimenez, Xavier Otazu, Valero Laparra, & Jesus Malo. (2013). Chromatic induction and contrast masking: similar models, different goals? In Human Vision and Electronic Imaging XVIII (Vol. 8651).
Abstract: Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Oualid M. Benkarim, Petia Radeva, & Laura Igual. (2014). Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation. In 8th Conference on Articulated Motion and Deformable Objects (Vol. 8563, pp. 138–147). LNCS. Springer International Publishing.
Abstract: The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
Keywords: MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation
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Marc Bolaños, Maite Garolera, & Petia Radeva. (2014). Video Segmentation of Life-Logging Videos. In 8th Conference on Articulated Motion and Deformable Objects (Vol. 8563, pp. 1–9).
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Juan Ramon Terven Salinas, Joaquin Salas, & Bogdan Raducanu. (2014). Robust Head Gestures Recognition for Assistive Technology. In Pattern Recognition (Vol. 8495, pp. 152–161). LNCS. Springer International Publishing.
Abstract: This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
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Carles Sanchez, Jorge Bernal, Debora Gil, & F. Javier Sanchez. (2013). On-line lumen centre detection in gastrointestinal and respiratory endoscopy. In Klaus Miguel Angel and Drechsler Stefan and González Ballester Raj and Wesarg Cristina and Shekhar Marius George and Oyarzun Laura M. and L. Erdt (Ed.), Second International Workshop Clinical Image-Based Procedures (Vol. 8361, pp. 31–38). LNCS. Springer International Publishing.
Abstract: We present in this paper a novel lumen centre detection for gastrointestinal and respiratory endoscopic images. The proposed method is based on the appearance and geometry of the lumen, which we defined as the darkest image region which centre is a hub of image gradients. Experimental results validated on the first public annotated gastro-respiratory database prove the reliability of the method for a wide range of images (with precision over 95 %).
Keywords: Lumen centre detection; Bronchoscopy; Colonoscopy
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Katerine Diaz, Francesc J. Ferri, & W. Diaz. (2013). Fast Approximated Discriminative Common Vectors using rank-one SVD updates. In 20th International Conference On Neural Information Processing (Vol. 8228, pp. 368–375). LNCS. Springer Berlin Heidelberg.
Abstract: An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz
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Fadi Dornaika, Alireza Bosaghzadeh, & Bogdan Raducanu. (2013). Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition. In Human Behavior Understanding 4th International Workshop (Vol. 8212, pp. 124–135). Springer International Publishing.
Abstract: Workshop on Human Behavior Understanding
Human-machine interaction is a hot topic nowadays in the communities of multimedia and computer vision. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. Recently, graph-based label propagation for multi-observation face recognition was proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot adapt optimally to the data. In this paper, we propose a novel approach for efficient and adaptive graph construction that can be used for multi-observation face recognition as well as for other recognition problems. Experimental results performed on Honda video face database, show a distinct advantage of the proposed method over the standard graph construction methods.
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Francesco Ciompi, Simone Balocco, Carles Caus, Josepa Mauri, & Petia Radeva. (2013). Stent shape estimation through a comprehensive interpretation of intravascular ultrasound images. In 16th International Conference on Medical Image Computing and Computer Assisted Intervention (Vol. 8150, pp. 345–352). LNCS. Springer Berlin Heidelberg.
Abstract: We present a method for automatic struts detection and stent shape estimation in cross-sectional intravascular ultrasound images. A stent shape is first estimated through a comprehensive interpretation of the vessel morphology, performed using a supervised context-aware multi-class classification scheme. Then, the successive strut identification exploits both local appearance and the defined stent shape. The method is tested on 589 images obtained from 80 patients, achieving a F-measure of 74.1% and an averaged distance between manual and automatic struts of 0.10 mm.
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Naveen Onkarappa, & Angel Sappa. (2013). Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 483–490). LNCS. Springer Berlin Heidelberg.
Abstract: Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
Keywords: Optical flow; regularization; Driver Assistance Systems; Performance Evaluation
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Marcelo D. Pistarelli, Angel Sappa, & Ricardo Toledo. (2013). Multispectral Stereo Image Correspondence. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 217–224). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.
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Fahad Shahbaz Khan, Joost Van de Weijer, Sadiq Ali, & Michael Felsberg. (2013). Evaluating the impact of color on texture recognition. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8047, pp. 154–162). Springer Berlin Heidelberg.
Abstract: State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.
Keywords: Color; Texture; image representation
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Debora Gil, Agnes Borras, Sergio Vera, & Miguel Angel Gonzalez Ballester. (2013). A Validation Benchmark for Assessment of Medial Surface Quality for Medical Applications. In 9th International Conference on Computer Vision Systems (Vol. 7963, pp. 334–343). LNCS. Springer Berlin Heidelberg.
Abstract: Confident use of medial surfaces in medical decision support systems requires evaluating their quality for detecting pathological deformations and describing anatomical volumes. Validation in the medical imaging field is a challenging task mainly due to the difficulties for getting consensual ground truth. In this paper we propose a validation benchmark for assessing medial surfaces in the context of medical applications. Our benchmark includes a home-made database of synthetic medial surfaces and volumes and specific scores for evaluating surface accuracy, its stability against volume deformations and its capabilities for accurate reconstruction of anatomical volumes.
Keywords: Medial Surfaces; Shape Representation; Medical Applications; Performance Evaluation
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Patricia Marquez, Debora Gil, Aura Hernandez-Sabate, & Daniel Kondermann. (2013). When Is A Confidence Measure Good Enough? In 9th International Conference on Computer Vision Systems (Vol. 7963, pp. 344–353). LNCS. Springer Link.
Abstract: Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality.
Keywords: Optical flow, confidence measure, performance evaluation
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Gioacchino Vino, & Angel Sappa. (2013). Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach. In 10th International Conference on Image Analysis and Recognition (Vol. 7950, pp. 354–363). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
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