Resumo: Tendo como locus o uso poético da Realidade Aumentada, neste artigo são apresentadas articulações entre a performance e a tecnologia da Realidade Aumentada com conteúdos digitais autorreferenciais coadunados na proposta artística ativista Inter Faces. Para a reflexão em torno dos processos de criação, é feita a confrontação com os trabalhos artísticos body e Parade to Hope. Pretende-se, com esta análise, apresentar os desafios e as soluções encontradas durante o processo de criação de Inter Faces, de modo a contribuir para o debate sobre a expansão, popularização e poetização da Realidade Aumentada na performance.

Abstract: In view of the poetic use of augmented reality, this article presents links between performance and augmented reality technology with self-referenced digital content, supported by the activist artistic proposal Inter Faces. For a reflection on creative processes, the artistic works body and Parade to Hope are compared. With this analysis we intend to present the challenges and solutions found during the creation process of Inter Faces, seeking to contribute to the debate on the expansion, popularization and poetization of augmented reality in performance.

Résumé: Ayant l’utilisation poétique de la réalité augmentée comme locus, cet article présente des articulations entre la performance et la technologie de la réalité augmentée avec un contenu numérique auto-référencé soutenu par la proposition artistique activiste Inter Faces. Pour une réflexion sur les processus créatifs, on fait la confrontation avec les oeuvres artistiques body et Parade to Hope. On prévu, avec cette analyse, de présenter les défis et les solutions trouvés au cours du processus de création de la recherche Inter Faces, afin de contribuer au débat sur l’expansion, la vulgarisation et la poétisation de la réalité augmentée en performance.

Abstract Introduction Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.

INTRODUCTION: Breast cancer is the second most common type of cancer in the world, being more common among women and representing 22% of all new cancer cases every year. The sooner it is diagnosed, the better the chances of a successful treatment are. Mammography is one way to detect non-palpable tumors that cause breast cancer. However, it is known that the sensitivity of this exam can vary considerably due to factors such as the specialist's experience, the patient's age and the quality of the images obtained in the exam. The use of computational techniques involving artificial intelligence and image processing has contributed more and more to support the specialists in obtaining a more precise diagnosis. METHODS: This paper proposes a methodology that exclusively uses texture analysis to describe features of masses in digitized mammograms. To increase the efficiency of texture feature extraction, the diversity index's capability to detect patterns of species co-occurrence is used. For this purpose, the Gleason and Menhinick indexes are used. Finally, the extracted texture is classified using the Support Vector Machine, looking to differentiate the malignant masses from the benign. RESULTS: The best result was obtained using the Gleason index, with 86.66% accuracy, 90% sensitivity, 83.33% specificity and an area under the ROC Curve (Az) of 0.86. CONCLUSION: Both indexes showed statistically similar performance; however, the Gleason index was slightly superior.