|
|
F. Javier Sanchez, Jorge Bernal, Cristina Sanchez Montes, Cristina Rodriguez de Miguel, & Gloria Fernandez Esparrach. (2017). Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos. MVAP - Machine Vision and Applications, , 1–20.
Abstract: A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance de ning specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classi cation
of bright spot regions. The former de nes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classi cation stage performed by a linear SVM classi er using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being
closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology.
Keywords: Specular highlights; bright spot regions segmentation; region classification; colonoscopy
|
|
|
|
David Vazquez, Jorge Bernal, F. Javier Sanchez, Gloria Fernandez Esparrach, Antonio Lopez, Adriana Romero, et al. (2017). A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images. JHCE - Journal of Healthcare Engineering, , 2040–2295.
Abstract: Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.
Keywords: Colonoscopy images; Deep Learning; Semantic Segmentation
|
|
|
|
David Sanchez-Mendoza, David Masip, & Agata Lapedriza. (2015). Emotion recognition from mid-level features. PRL - Pattern Recognition Letters, 67(Part 1), 66–74.
Abstract: In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception.
Keywords: Facial expression; Emotion recognition; Action units; Computer vision
|
|
|
|
David Masip, Michael S. North, Alexander Todorov, & Daniel N. Osherson. (2014). Automated Prediction of Preferences Using Facial Expressions. Plos - PloS one, 9(2), e87434.
Abstract: We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available.
|
|
|
|
David Masip, M. Bressan, & Jordi Vitria. (2005). Feature extraction methods for real-time face detection and classification. Eurasip Journal on Applied Signal Processing, 13: 2061–2071.
|
|
