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J.M. Sanchez, X. Binefa, & Jordi Vitria. (2002). Shot Partitioning Based Recognition of Tv Commercials. Multimedia Tools and Applications, 18: 233–247, Kluwer Academic Publishers (IF: 0.421).
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Hugo Jair Escalante, Victor Ponce, Sergio Escalera, Xavier Baro, Alicia Morales-Reyes, & Jose Martinez-Carranza. (2017). Evolving weighting schemes for the Bag of Visual Words. Neural Computing and Applications - Neural Computing and Applications, 28(5), 925–939.
Abstract: The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
Keywords: Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision
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Manuel Graña, & Bogdan Raducanu. (2015). Special Issue on Bioinspired and knowledge based techniques and applications. NEUCOM - Neurocomputing, , 1–3.
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Juan Ramon Terven Salinas, Bogdan Raducanu, Maria Elena Meza de Luna, & Joaquin Salas. (2016). Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices. NEUCOM - Neurocomputing, 175(B), 866–876.
Abstract: During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset.
Keywords: Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices
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Fosca De Iorio, C. Malagelada, Fernando Azpiroz, M. Maluenda, C. Violanti, Laura Igual, et al. (2009). Intestinal motor activity, endoluminal motion and transit. NEUMOT - Neurogastroenterology & Motility, 21(12), 1264–e119.
Abstract: A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion.
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