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Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing automated malignant BC detection system applied on patient's imagery can help dealing with this problem more efficiently, making diagnosis more scalable and less prone to errors. Not less importantly, such kind of research can be extended to other types of cancer, making even more impact to help saving lives...
This work focuses on cost reduction methods, applied on forest species recognition systems as a case-study. Current state-of-the-art shows that the accuracy of these systems, generally employing texture recognition approaches, have increased considerably in the past years. However, the cost in time to perform the recognition of input samples has also increased proportionally. By taking into account...
This paper presents the results of the HDSRC 2014 competition on handwritten digit string recognition in challenging datasets organized in conjunction with ICFHR 2014. The general objective of this competition is to identify, evaluate and compare recent developments in Western Arabic digit string recognition with varying length. In addition, this competition introduces two new challenging datasets...
Forest species recognition has been traditionally addressed as a texture classification problem, and explored using standard texture methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Gabor Filters. Deep learning techniques have been a recent focus of research for classification problems, with state-of-the art results for object recognition and other tasks, but are not...
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