Vol. 30 (2021):
  Abstracts and Contents of Papers

29 (2020)	main	forthcoming papers

No. 1/4.

Machine GRAPHICS & VISION, Vol. 30 (2021), No. 1/4

This number as one file
MGV vol. 30, no. 1/4, 2021.

Zaniolo L., Marques O.:
On the use of CNNs with patterned stride for medical image analysis
MGV vol. 30, no. 1/4, 2021, pp. 3-22.

The use of deep learning techniques for early and accurate medical image diagnosis has grown significantly in recent years, with some encouraging results across many medical specialties, pathologies, and image types. One of the most popular deep neural network architectures is the convolutional neural network (CNN), widely used for medical image classification and segmentation, among other tasks. One of the configuration parameters of a CNN is called stride and it regulates how sparsely the image is sampled during the convolutional process. This paper explores the idea of applying a patterned stride strategy: pixels closer to the center are processed with a smaller stride concentrating the amount of information sampled, and pixels away from the center are processed with larger strides consequently making those areas to be sampled more sparsely. We apply this method to different medical image classification tasks and demonstrate experimentally how the proposed patterned stride mechanism outperforms a baseline solution with the same computational cost (processing and memory). We also discuss the relevance and potential future extensions of the proposed method.
Key words: convolutional neural networks, patterned stride, medical image classification, deep learning.

Roopalakshmi R.:
Chemical ripening and contaminations detection using neural networks-based image features and spectrometric signatures
MGV vol. 30, no. 1/4, 2021, pp. 23-43.

In this pandemic-prone era, health is of utmost concern for everyone and hence eating good quality fruits is very much essential for sound health. Unfortunately, nowadays it is quite very difficult to obtain naturally ripened fruits, due to existence of chemically ripened fruits being ripened using hazardous chemicals such as calcium carbide. However, most of the state-of-the art techniques are primarily focusing on identification of chemically ripened fruits with the help of computer vision-based approaches, which are less effective towards quantification of chemical contaminations present in the sample fruits. To solve these issues, a new framework for chemical ripening and contamination detection is presented, which employs both visual and IR spectrometric signatures in two different stages. The experiments conducted on both the GUI tool as well as hardware-based setups, clearly demonstrate the efficiency of the proposed framework in terms of detection confidence levels followed by the percentage of presence of chemicals in the sample fruit.
Key words: chemical ripening, arsenic contamination, visual features, IR spectral signatures.

Doshi H., Kiran N. U.:
Nonlinear Evolutionary PDE-Based Refinement of Optical Flow
MGV vol. 30, no. 1/4, 2021, pp. 45-65.

The goal of this paper is to propose two nonlinear variational models for obtaining a refined motion estimation from an image sequence. Both the proposed models can be considered as a part of a generalized framework for an accurate estimation of physics-based flow fields such as rotational and fluid flow. The first model is novel in the sense that it is divided into two phases: the first phase obtains a crude estimate of the optical flow and then the second phase refines this estimate using additional constraints. The correctness of this model is proved using an evolutionary PDE approach. The second model achieves the same refinement as the first model, but in a standard manner, using a single functional. A special feature of our models is that they permit us to provide efficient numerical implementations through the first-order primal-dual Chambolle-Pock scheme. Both the models are compared in the context of accurate estimation of angle by performing an anisotropic regularization of the divergence and curl of the flow respectively. We observe that, although both the models obtain the same level of accuracy, the two-phase model is more efficient. In fact, we empirically demonstrate that the single-phase and the two-phase models have convergence rates of order O(1/N²) and O(1/N) respectively.
Key words: optical flow, evolutionary PDE, variational methods, primal-dual, convergence.

29 (2020)	main	forthcoming papers

Last updated: May 11, 2022 (DOI confirmed)