Vol. 29 (2020):
Abstracts and Contents of Papers
Machine GRAPHICS & VISION, Vol. 29 (2020), No. 1/4
This number is not closed yet. The papers are published within the Accepted papers online policy.
- Bertolini M., Magri L.:
Critical hypersurfaces and instability for reconstruction of scenes in high dimensional projective spaces
MGV vol. 29, no. 1/4, 2020, pp. 3-20.
Accepted paper online
In the context of multiple view geometry, images of static scenes are modeled as linear projections from a projective space to a projective plane and, similarly, videos or images of suitable dynamic or segmented scenes can be modeled as linear projections from to , with . In those settings, the projective reconstruction of a scene consists in recovering the position of the projected objects and the projections themselves from their images, after identifying many enough correspondences between the images. A critical locus for the reconstruction problem is a configuration of points and of centers of projections, in the ambient space, where the reconstruction of a scene fails. Critical loci turn out to be suitable algebraic varieties. In this paper we investigate those critical loci which are hypersurfaces in high dimension complex projective spaces, and we determine their equations. Moreover, to give evidence of some practical implications of the existence of these critical loci, we perform a simulated experiment to test the instability phenomena for the reconstruction of a scene, near a critical hypersurface.
critical loci, projective reconstruction, computer vision, multiview geometry.
- Pach J. L., Krupa A., Antoniuk I.:
Text area detection in handwritten documents scanned for further processing
MGV vol. 29, no. 1/4, 2020, pp. 21-31.
Accepted paper online
In this paper we present an approach to text area detection using binary images, Constrained Run Length Algorithm and other noise reduction methods of removing the artefacts. Text processing includes various activities, most of which are related to preparing input data for further operations in the best possible way, that will not hinder the OCR algorithms. This is especially the case when handwritten manuscripts are considered, and even more so with very old documents. We present our methodology for text area detection problem, which is capable of removing most of irrelevant objects, including elements such as page edges, stains, folds etc. At the same time the presented method can handle multi-column texts or varying line thickness. The generated mask can accurately mark the actual text area, so that the output image can be easily used in further text processing steps.
text area detection, handwritten text, machine learning, optical character recognition, text recognition.
- Azam H., Tariq H.:
Skull stripping using traditional and soft-computing approaches for magnetic resonance images: A semi-systematic meta-analysis
MGV vol. 29, no. 1/4, 2020, pp. 33-53.
Accepted paper online
MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.
skull stripping, brain MR Images, soft computing, meta-analysis.
- Iftikhar H., Shahid A. R., Raza B., Khan H. N.:
Multi-View Attention-based Late Fusion (MVALF) CADx system for breast cancer using deep learning
MGV vol. 29, no. 1/4, 2020, pp. 55-78.
Accepted paper online
Breast cancer is a leading cause of death among women. Early detection can significantly reduce the mortality rate among women and improve their prognosis. Mammography is the first line procedure for early diagnosis. In the early era, conventional Computer-Aided Diagnosis (CADx) systems for breast lesion diagnosis were based on just single view information. The last decade evidence the use of two views mammogram: Medio-Lateral Oblique (MLO) and Cranio-Caudal (CC) view for the CADx systems. Most recent studies show the effectiveness of four views of mammogram to train CADx system with feature fusion strategy for classification task. In this paper, we proposed an end-to-end Multi-View Attention-based Late Fusion (MVALF) CADx system that fused the obtained predictions of four view models, which is trained for each view separately. These separate models have different predictive ability for each class. The appropriate fusion of multi-view models can achieve better diagnosis performance. So, it is necessary to assign the proper weights to the multi-view classification models. To resolve this issue, attention-based weighting mechanism is adopted to assign the proper weights to trained models for fusion strategy. The proposed methodology is used for the classification of mammogram into normal, mass, calcification, malignant masses and benign masses. The publicly available datasets CBIS-DDSM and mini-MIAS are used for the experimentation. The results show that our proposed system achieved 0.996 AUC for normal vs. abnormal, 0.922 for mass vs. calcification and 0.896 for malignant vs. benign masses. Superior results are seen for the classification of malignant vs benign masses with our proposed approach, which is higher than the results using single view, two views and four views early fusion-based systems. The overall results of each level show the potential of multi-view late fusion with transfer learning in the diagnosis of breast cancer.
breast cancer, mammogram, four-view mammogram, information fusion, late fusion, transfer learning.
Last updated February 15, 2021