Laurindo de Sousa Britto Neto
@ufpi.br
Federal University of Piauí
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Signal Processing, Computer Graphics and Computer-Aided Design, Human-Computer Interaction
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
José Nazareno Alves Rodrigues, Rodrigo De Melo Souza Veras, Laurindo De Sousa Britto Neto, Pedro Henrique Ximenes Ramalho Barros, Wilana Da Silva Moura, Kelson James Silva De Almeida, and Kelson Romulo Teixeira Aires
Institute of Electrical and Electronics Engineers (IEEE)
Parkinson’s Disease (PD) is one of the most prevalent neurological conditions, affecting a significant portion of the elderly population worldwide. Characterized by motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor symptoms like hypomimia (reduced facial expressiveness), PD presents significant diagnostic challenges, especially in its early stages. In this context, this systematic review (SR) aims to explore machine learning and deep learning techniques in detecting PD through facial image analysis. The study identifies the key works in the field, the computational techniques employed, and the datasets used. The main findings suggest that facial phenotyping can be a strong indicator for the early diagnosis of PD. Various approaches, including Convolutional Neural Networks (CNNs) and other deep learning models, have shown promise in detecting subtle changes in facial expressions associated with hypomimia. Integrating multimodal data sources, such as voice recordings, hand-drawn sketches, movement analysis, and facial images, has also demonstrated the potential to enhance diagnostic accuracy. This review highlights the potential of facial image analysis as an innovative approach for the early detection of PD, which could lead to more timely and effective interventions.
André Machado, Rodrigo Veras, Kelson Aires, and Laurindo de Sousa Britto Neto
Institute of Electrical and Electronics Engineers (IEEE)
In recent years, Computer Vision and Machine Learning techniques have been extensively explored in the creation of assistive systems for the visually impaired.One of the most challenging tasks for visually impaired people is object recognition.In this paper, we conducted a systematic review to identify the current state of the art in designing these assistive systems.Due to the huge amount of object categories, we focused on recognizing products, such as those found in grocery stores, pantries and refrigerators.We analyze the techniques used, noting the efficiency and economy of hardware resources such as processing, memory and battery. Thus we verify if they can be used in wearable systems and adapted to existing devices of the Internet of Things (IoT), enabling the proposition of efficient and accessible assistive product recognition systems.
Valeska Uchoa, Kelson Aires, Rodrigo Veras, Anselmo Paiva, and Laurindo Britto
IEEE
Face recognition is a challenging Computer Vision task. In this paper, we propose a method for face recognition by applying data augmentation and transfer learning in pre-trained Convolutional Neural Networks (CNNs). Our main focus is to analyze the power of data augmentation for face recognition systems with CNN transfer learning. We extracted features from the images and trained a KNN classifier using the VGG-Face CNN. For the input dataset, we applied several transformations to generate 12 different versions of datasets used to evaluate which combination produces better results. We ran experiments using data augmentation on the LFW dataset. We also created a proprietary dataset composed of 12 subjects. The tests have shown that the classifier trained with the dataset Saturation presented the best results with an accuracy of 98.43%. For the proprietary dataset, the best accuracy was 95.41% obtained with the Brightness, Contrast, and Saturation Combined version. In this way, we conclude that the data augmentation is essential for the face recognition task. However, a study needs to be performed for each application, since the augmentation operations that improve the classification results are dependent on the input set. Furthermore, we show that with a few samples available, it is possible to have a face recognition system with high accuracy.
Leonardo P. Sousa, Rodrigo M. S. Veras, Luis H. S. Vogado, Laurindo S. Britto Neto, Romuere R. V. Silva, Flavio. H. D. Araujo, and Fatima N. S. Medeiros
IEEE
Around the world, there are many people with disabilities; it is estimated that 39 million people are blind and 246 million have limited vision, giving a total of 285 million visually impaired people. The use of information and communication technologies can help disabled people to achieve greater independence, quality of life and inclusion in social activities by increasing, maintaining or improving their functional capacities. In this context, this paper presents an automatic methodology for identifying banknotes that can be widely used by people with visual impairment. For this, we evaluated a set of four point-of-interest detectors, two descriptors, seven ways of generating the image signature, and six classification methodologies, which can be used as a basis for the development of applications for the identification of banknotes. Experiments performed on US Dollar (USD), Euro (EUR) and Brazilian Real Banknotes (BRL) obtained rates of accuracy of 99.78%, 99.12%, and 96.92%, respectively.
Luais Santos, Rodrigo Veras, Kelson Aires, Laurindo Britto, and Vinaicius Machado
IEEE
One of the least invasive means of diagnosing certain diseases is through the use of imaging tests. Automated analysis usually begins by detecting the regions to be investigated and extracting relevant information such as shape and texture. In this paper, we propose a semi-supervised clustering algorithm called Seeded Fuzzy C-means to segment regions of interest in medical images. Seeded Fuzzy C-means is based on the well-known Fuzzy C-means; however, it also uses information provided by the physician to insert constraints during group choices. In this way, the element is placed in a group with a higher degree of certainty. We evaluated the proposed algorithm on a total of 2,200 images in leukemia, skin cancer, cervical cancer, and glaucoma image databases. Two semi-supervised clustering algorithms were implemented from literature to perform this analysis. The results illustrate the ability of the algorithm to efficiently assist physicians in detecting regions of interest, as it achieved an “Excellent” Kappa index in most of the tests.
Laurindo de Sousa Britto Neto, Vanessa Regina Margareth Lima Maike, Fernando Luiz Koch, Maria Cecília Calani Baranauskas, Anderson de Rezende Rocha, and Siome Klein Goldenstein
Springer International Publishing
Vanessa Regina Margareth Lima Maike, Laurindo de Sousa Britto Neto, Siome Klein Goldenstein, and Maria Cecília Calani Baranauskas
Springer International Publishing
Vanessa Regina Margareth Lima Maike, Laurindo de Sousa Britto Neto, Maria Cecília Calani Baranauskas, and Siome Klein Goldenstein
Springer International Publishing
Rafael B. Gomes, Lucas M. Oliveira, Laurindo S. Britto‐Neto, Tiago S. Santos, Gilbran S. Andrade, Bruno M. Carvalho, and Luiz M. G. Gonçalves
Wiley
AbstractStylized rendering is the process of generating images or videos that can have the visual appeal of pieces of art, expressing the visual and emotional characteristics of artistic styles. A major problem in stylizing videos is the absence of temporal coherence, something that results in flickering of the structural drawing elements (such as brush strokes or curves), also known as swimming. This article describes the AnimVideo rendering tool that was developed for stylizing videos with temporal coherence. The temporal coherence is achieved by first fully segmenting the input video with a fast fuzzy segmentation algorithm that uses hybrid color spaces and motion information. The result of the segmentation algorithm is used to constrain the result of an optical flow algorithm, given as dense optical flow maps that are then used to correctly move, remove, or add structural drawing elements. The combination of these two methods is referred to as constrained optical flow, and we also provide the option of initializing the optical flow computation with displacement maps computed by homographies that map objects in adjacent frames. Also, we briefly describe some stylized rendering methods that were implemented in the tool. Finally, experimental results are shown, including snapshots of the tool's interface and illustrative examples of the produced renderings that validates the proposed techniques. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 100–110, 2009.
Laurindo S. Britto Neto and Bruno M. Carvalho
IEEE
Laurindo S. Britto Neto Bruno M. Carvalho
IEEE
Non-photorealistic rendering (NPR) is a class of techniques defined by what they do not aim, the realistic rendering of artificial scenes. NPR techniques, on the other hand, aim to reproduce artistic techniques renderings, trying to express feelings and moods on the rendered scenes. This paper introduces a new style of NPR based on a typical art craft from the northeastern region of Brazil, that uses colored sand to compose landscapes inside glass bottles. A method for generating two-dimensional (2D) virtual sand textures is introduced, as well as techniques for combining them in a point-by-point or in an area basis