IMAGE FUSION FOR MULTIFOCUS IMAGES USING SPEEDUP ROBUST FEATURES

The multi-focus image fusion technique has emerged as major topic in image processing in order to generate all focus images with increased depth of field from multi focus photographs. Image fusion is the process of combining relevant information from two or more images into a single image. The image registration technique includes the entropy theory. Speed up Robust Features (SURF), feature detector and Binary Robust Invariant Scalable Key points (BRISK) feature descriptor is used in feature matching process. An improved RANDOM Sample Consensus (RANSAC) algorithm is adopted to reject incorrect matches. The registered images are fused using stationary wavelet transform (SWT).The experimental results prove that the proposed algorithm achieves better performance for unregistered multiple multi-focus images and it especially robust to scale and rotation translation compared with traditional direct fusion method.  Â

Multi-view coding of local features in visual sensor networks

Local visual features extracted from multiple camera views are employed nowadays in several application scenarios, such as object recognition, disparity matching, image stitching and many others. In several cases, local features need to be transmitted or stored on resource-limited devices, thus calling for efficient coding techniques. While recent works have addressed the problem of efficiently compressing local features extracted from still images or video sequences, in this paper we propose and evaluate an architecture for coding features extracted from multiple, overlapping views. The proposed Multi-View Feature Coding architecture can be applied to either real-valued or binary features, and allows to obtain bitrate reductions in the order of 10-20% with respect to simulcast coding

Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study

Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value

Corrigendum to "Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study" [Phys. Imaging Radiat. Oncol. 22 (2022) 131-136]

Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value

Methods for iris classification and macro feature detection

This work deals with two distinct aspects of iris-based biometric systems: iris classification and macro-feature detection. Iris classification will benefit identification systems where the query image has to be compared against all identities in the database. By preclassifying the query image based on its texture, this comparison is executed only against those irises that are from the same class as the query image. In the proposed classification method, the normalized iris is tessellated into overlapping rectangular blocks and textural features are extracted from each block. A clustering scheme is used to generate multiple classes of irises based on the extracted features. A minimum distance classifier is then used to assign the query iris to a particular class. The use of multiple blocks with decision level fusion in the classification process is observed to enhance the accuracy of the method.;Most iris-based systems use the global and local texture information of the iris to perform matching. In order to exploit the anatomical structures within the iris during the matching stage, two methods to detect the macro-features of the iris in multi-spectral images are proposed. These macro-features typically correspond to anomalies in pigmentation and structure within the iris. The first method uses the edge-flow technique to localize these features. The second technique uses the SIFT (Scale Invariant Feature Transform) operator to detect discontinuities in the image. Preliminary results show that detection of these macro features is a difficult problem owing to the richness and variability in iris color and texture. Thus a large number of spurious features are detected by both the methods suggesting the need for designing more sophisticated algorithms. However the ability of the SIFT operator to match partial iris images is demonstrated thereby indicating the potential of this scheme to be used for macro-feature detection

A-SATMVSNet: An attention-aware multi-view stereo matching network based on satellite imagery

Introduction: The stereo matching technology of satellite imagery is an important way to reconstruct real world. Most stereo matching technologies for satellite imagery are based on depth learning. However, the existing depth learning based methods have the problems of holes and matching errors in stereo matching tasks.Methods: In order to improve the effect of satellite image stereo matching results, we propose a satellite image stereo matching network based on attention mechanism (A-SATMVSNet). To solve the problem of insufficient extraction of surface features, a new feature extraction module based on triple dilated convolution with attention module is proposed, which solves the problem of matching holes caused by insufficient extraction of surface features. At the same time, compared with the traditional weighted average method, we design a novel cost-volume method that integrates attention mechanism to reduce the impact of matching errors to improve the accuracy of matching.Results and discussion: Experiments on public multi-view stereo matching dataset based on satellite imagery demonstrate that the proposed method significantly improves the accuracy and outperforms various previous methods. Our source code is available at https://github.com/MVSer/A-SATMVSNet

Higher-order Occurrence Pooling on Mid- and Low-level Features: Visual Concept Detection

In object recognition, the Bag-of-Words model assumes: i) extraction of local descriptors from images, ii) embedding these descriptors by a coder to a given visual vocabulary space which results in so-called mid-level features, iii) extracting statistics from mid-level features with a pooling operator that aggregates occurrences of visual words in images into so-called signatures. As the last step aggregates only occurrences of visual words, it is called as First-order Occurrence Pooling. This paper investigates higher-order approaches. We propose to aggregate over co-occurrences of visual words, derive Bag-of-Words with Second- and Higher-order Occurrence Pooling based on linearisation of so-called Minor Polynomial Kernel, and extend this model to work with adequate pooling operators. For bi- and multi-modal coding, a novel higher-order fusion is derived. We show that the well-known Spatial Pyramid Matching and related methods constitute its special cases. Moreover, we propose Third-order Occurrence Pooling directly on local image descriptors and a novel pooling operator that removes undesired correlation from the image signatures. Finally, Uni- and Bi-modal First-, Second-, and Third-order Occurrence Pooling are evaluated given various coders and pooling operators. The proposed methods are compared to other approaches (e.g. Fisher Vector Encoding) in the same testbed and attain state-of-the-art results