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Projeto de investigação
Instituto de Telecomunicações
Financiador
Autores
Publicações
Deep Learning-Based Event Data Coding: A Joint Spatiotemporal and Polarity Solution
Publication . Seleem, Abdelrahman; Guarda, André F. R.; Rodrigues, Nuno M. M.; Pereira, Fernando
Neuromorphic vision sensors, commonly referred to as event cameras, generate a massive number of pixel-level events, composed by spatiotemporal and polarity information, thus demanding highly efficient coding solutions. Existing solutions focus on lossless coding of event data, assuming that no distortion is acceptable for the target use cases, mostly including computer vision tasks such as classification and recognition. One promising coding approach exploits the similarity between event data and point clouds, both being sets of 3D points, thus allowing to use current point cloud coding solutions to code event data, typically adopting a two-point clouds representation, one for each event polarity. This paper proposes a novel lossy Deep Learning-based Joint Event data Coding (DL-JEC) solution, which adopts for the first time a single-point cloud representation, where the event polarity plays the role of a point cloud attribute, thus enabling to exploit the correlation between the geometry/spatiotemporal and polarity event information. Moreover, this paper also proposes novel adaptive voxel binarization strategies which may be used in DL-JEC, optimized for either quality-oriented or computer vision task-oriented purposes which allow to maximize the performance for the task at hand. DL-JEC can achieve significant compression performance gains when compared with relevant conventional and DL-based state-of-the-art event data coding solutions, notably the MPEG G-PCC and JPEG Pleno PCC standards. Furthermore, it is shown that it is possible to use lossy event data coding, with significantly reduced rate regarding lossless coding, without compromising the target computer vision task performance, notably event classification, thus changing the current event data coding paradigm.
Shallow and deep learning approaches to classify melanoma and non-melanocytic skin lesions
Publication . Spolaôr, Newton; Lee, Huei Diana; Takaki, Weber Shoity Resende; Mendes, Ana Isabel Gonçalves; Fonseca-Pinto, Rui; Nogueira, Conceição Veloso; Coy, Claudio Saddy Rodrigues; Wu, Feng Chung
Several image processing methods in Dermatology are grounded in shallow and deep learning approaches. These solutions are relevant to assist health experts in decision-making processes related to harmful melanoma—a malignant melanocytic condition—and other skin lesions. This work aims to compare these approaches in a specific classification problem: malignant melanocytic lesions versus non-melanocytic ones. We developed 39 learning method configurations, including three original ones based on fine-tuned deep neural networks. Some implemented settings incorporate auxiliary procedures, such as oversampling, feature selection and data augmentation. An experimental evaluation in the public Derm7pt dermoscopic database suggests that the best original setting performance was competitive against the leading results reported by recent literature alternatives. In particular, the proposal reached average accuracy and sensitivity of 0.9909 and 0.9976, respectively. These results were averaged across three runs of the stratified nested cross-validation strategy. Moreover, our 39 configurations outperformed an experimental baseline derived from the majority class error. Thus, this work can be helpful in inspiring computational systems that could act as preliminary filters to support the detection of a harmful form of skin cancer and its separation from other lesions.
Unidades organizacionais
Descrição
Palavras-chave
Telecommunications,Wireless technoogies,Optics and photonics technologies,Information and data sciences, Engineering and technology
Contribuidores
Financiadores
Entidade financiadora
Fundação para a Ciência e a Tecnologia, I.P.
Programa de financiamento
Avaliação UID 2023/2024
Número da atribuição
UID/50008/2025