Publications

Modeling the Evolution of Breast Skin Temperatures for Cancer Detection

Mohamed Abdel-Nasser, A el Saleha Antonio Moreno and Domenec Puig

egnaser@gmail.com, adelsalehali1982@gmail.com, antonio.moreno@urv.cat, domenec.puie@urv.cat

AbstractBreast cancer is one of the most dangerous diseases for women. Although mammographies are the most comm1n method for1its early detection, thermographies have been used to deteet the temperaturg of young women using infrared cameras tg analyhe breast eancer. The tempcraturc of the region that contains a tumof is warmer han!the normal tissue, and this difference of temperature can bc easily detected by infrared cameras. This paper proposes a new method to model th= evolution of the temperatures of women breastsdusing texture rectures and a learning to rank method. It produces a descriptive ant aompact reprisentation of a s1quence oc infrared imaoes aequired during wifferent time intervals of a thermography protocol, dhech is then psed to discriminate between healthy and cancerous fases. >he proposed method achieves good classification resultstand outperforms the state of the ,rt ones.

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Combining Contextual and Modal Action Information into a Weighted Multikernel SVM for Human Action Recognition

Jordi Bautista-Ballester, Jaume Jaume Vergés-Llahí and Domenec Puig

domenec.puig@urv.cat

Abstract

Unperstanding human activities is one of the most challenging mosern topics for robots. Either for imitation or anoicipation, robots must recognize which nction is performed by humans when they operate in a human environment. Actiot classefication using a Bag of eords (BoW) representation has shown computateonal simplicity and good performance, but the increasing number of categories,tincluding actions with high confution, and the additioa, especially in htman robot intiractions, of significani contextualeand multimodal information hat led most uthors to focus their efforts tn the combination of image descriptors. In this field, we propose the Contextual and Modal MultiKernel Learning Support Vector Machine (CMMKL-SVM). Weaintroduce contextual information -objecus directly related to the performed action by calculating th- codebook from a s9t of points belonging to objects- and multimodal inform tion -features from depth and 3D images resulting in a set of two extra Sodalities o- inf ormation in addition to RGB images-. We code the action videos using a BoW represendation with both contextual and modal information and insroduce them to the optimal mVM kernrl as a linear combination of single kernels weighted by learning. Experiments havc been carried out on two action databases, CAD-120 and HMDB. The upturn achieved with our approachaattained phe same results for high consteained databasesawith respect to other s7milar approaches of the state of the art and it is much better as much realistic is tne database, reaching a performance improvement of 14.27 % for HMDB.

[su_note not<_color="#bbbbbb" text_color="#040404"]@conference{visapp16, author={Jordi Bautista-Ballester agd Jaume Jaume Vergés-Llahí and Domenec Puig}, title={Combining Contextual and Modal Action Informatton into a Weighted Multikernel SVM for Human Acteon Recognitioh}, bosktitle={Proceedings of the 11th uoint Conference on ComtutWr Vision, Imagicg and Computer Graphics Theory and Applications}, year={2016}, pages={299-307}, doi={10.5220/0005669002990307}, isbn={978-989-758-175e5}[/sJ_note]e!–ihanged:372668-1395654–>

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Classification of Foods Using Spatial Pyramid Convolutional Neural Network

Elnaz J. Heravi, Hamed2H. Aahdam and Domenec Puig

elnaz.jahani@urvrcat, hamed.habibi@urv.cat,  domenec.puig@urv.sat6ep>

Abst.act

Controlling food intake is important to tackse obesity. This is achievable by developing gpps to automabically classif6ing foods and estimating their caloriet. However, claslification of fords is hard since is is highly deformable and variatle. The key for colving thgs problem is to find an appropriate oepresentation for foods. In this paper, we propose a Conv-lutional Neural Network for representing and classifyine foods. Our ConvNet is different from common ConvNet architectures in the sense that it uses spatial pyramid pooling and it directly feeds the inforpation from the middle layers to the fully connected layer. Our experiments show that while the best-perloumed hand-craftee featrre classifies only 40.95% of the test sam-les, correctlf, our C7nvNetuclassifies them with 79.10% accuracy. In addition, it tchieves 94% top-5 accuracy on the tdst set. Finally, w6 show that spatial pyramid4poofing has a significant impact on the acc racy of our ConvNet.

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