About me

I am a second year PhD student and a graduate research assistant in the College of Information and Computer Sciences at the University of Massachusetts Amherst under the supervision of Subhransu Maji in the Computer Vision Lab. Previously, I spent three years as a graduate research assistant under the supervision of Pablo Arbeláez in the Biomedical Computer Vision Group at the Universidad de Los Andes. My interests are in Pattern Recognition in Computer Vision using Artificial Intelligence.



Finding Four-Leaf Clovers: A Benchmark for Fine-Grained Object Localization
Laura Bravo*, Alejandro Pardo*, Gustavo Perez*, Pablo Arbelaez
The Sixth Workshop on Fine-Grained Visual Categorization (FGVC6), CVPR 2019.

We present the Four-Leaf Clover (FLC) dataset, a new experimental framework for studying fine-grained object localization problems. We built the FLC dataset with the contribution of trained hobbyists, who were assigned the task of spotting four-leaf clovers on a fixed geographical extension over two clover seasons, one season for the train set and another for the test set. We then annotated each object instance for the tasks of object detection, semantic segmentation, instance segmentation, object parsing and semantic boundary detection. Our dataset is composed of more than 100,000 images, containing 2,151 carefully annotated clover instances of four, five or six leaves. The FLC dataset is extremely challenging and adapted to fine-grained object localization problems due to its small inter-class variance and its very large intra-class variation. We perform extensive experiments with state-of-the-art methods in order to establish strong baselines for each of the tasks.

pdf | poster | project page | dataset | BibTex

Automated Detection of Lung Nodules with Three-dimensional Convolutional Neural Networks
Gustavo Perez, Pablo Arbelaez →(oral)
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 2017.

Lung cancer is the cancer type with highest mortality rate worldwide. It has been shown that early detection with computer tomography (CT) scans can reduce deaths caused by this disease. Manual detection of cancer nodules is costly and time-consuming. We present a general framework for the detection of nodules in lung CT images. Our method consists of the pre-processing of a patient’s CT with filtering and lung extraction from the entire volume using a previously calculated mask for each patient. From the extracted lungs, we perform a candidate generation stage using morphological operations, followed by the training of a three-dimensional convolutional neural network for feature representation and classification of extracted candidates for false positive reduction. We perform experiments on the publicly available LIDC-IDRI dataset. Our candidate extraction approach is effective to produce precise candidates with a recall of 99.6%. In addition, false positive reduction stage manages to successfully classify candidates and increases precision by a factor of 7.000.

pdf | project page | spie | BibTex