Breast cancer is one of the principal causes of death for women in the world. Invasive breast cancer develops in about one in eight women in the United States during her lifetime. Digital mammography is a common technique for early detection of the breast cancer. However, only 84% of breast cancers are detected by interpreting radiologists. Computer Aided Detection (CAD) is a technology designed to help radiologists and to decrease observational errors. Actually, for every true-positive cancer detected by the CAD there are more false predictions, which have to be ignored by radiologists. In this work, a CAD method for detection and classification of breast abnormalities is proposed. The proposed method is based on the local energy and phase congruency approach and a supervised machine learning classifier. Experimental results are presented using digital mammography dataset and evaluated under different performance metrics.
The Robust Reading research area deals with detection and recognition of textual information in scene images. In particular, natural scene text detection and recognition techniques have gained much attention from the computer vision community due to their contribution to multiple applications. Common text detection and recognition methods are often affected by environment aspects, image acquisition problems, and the text content. In this work, a method for text detection and recognition in natural scenes is proposed. The method consists of three stages: 1) phase-based text segmentation, obtained by applying the MSER algorithm to the local phase image; 2) text localization, where segmented regions are classified and grouped as text and non-text components; and, 3) word recognition, where characters are recognized utilizing Histograms of Phase Congruency. Experimental results are presented using a known dataset and evaluated under precision and recall measures.
ICP is the most commonly used algorithm in tasks of point clouds mapping, finding the transformation between clouds, building a three-dimensional map. One of the key steps of the algorithm is the removal a part of the points and the searching a correspondence of clouds. In this article, we propose a method for removing some points from the clouds. Reducing the number of points decrease an execution time of the next steps and, as a result, increase performance. The paper describes an approach based on the analysis of the geometric shapes of the scene objects. In the developed algorithm, the points lying on the boundaries of the planes intersections, the so-called edges of objects, are selected from the clouds. Then the intersection points of the found edges are checked to belong the main vertices of the objects. After that, additional vertices are excluded from the edges and, if necessary, new ones are added. The described approach is performed for both point clouds. All further steps of the ICP algorithm are performed with new clouds. In the next step, after finding the correspondence, the vertices found in the previous step are taken from the first cloud, with all the edges connected with them. For each such group it is necessary to find the corresponding group from the second cloud. The method looks for correspondence for geometrically similar parts of point clouds. After finding the intermediate transformation, the current error is calculated. The original point clouds are used for the error calculation. This approach significantly reduces the number of points participating the deciding of the ICP variational subproblem.
In recent years, deep learning as a part of the artificial intelligence theory has formed the basis for many advanced developments, such as drone, voice and image recognition technologies, and etc. The concept of deep learning is closely related to artificial neural networks. On the other hand deep learning techniques work with unmarked data. For this reason, deep learning algorithms show their effectiveness in face recognition. But there are a number of difficulties related to implementation of deep learning algorithms. Deep learning requires a large amount of unmarked data and long training. In this presentation a new algorithm for automatic selection of face features using deep learning techniques based on autoencoders in combination with customized loss functions to provide high informativeness with low withinclass and high between-class variance is proposed. The multilayer networks of feed forward type are used. The extracted features are used for face classification. The performance of the proposed system for processing, analyzing and classifying persons from face images is compared with that of state-of-art algorithms.
The problem of face recognition is the important task in the security field, closed-circuit television (CCTV), artificial intelligence, and etc. One of the most effective approaches for pattern recognition is the use of artificial neural networks. In this presentation, an algorithm using generative adversarial networks is developed for face recognition. The proposed method consists in the interaction of two neural networks. The first neural network (generative network) generates face patterns, and the second network (discriminative network) rejects false face patterns. Neural network of feed forward type (single-layer or multilayer perceptron) is used as generative network. The convolutional neural network is used as discriminative network for the purpose of pattern selection. A big database of normalized to brightness changes and standardized in scale artificial images is created for the training of neural networks. New facial images are synthesized from existing ones. Results obtained with the proposed algorithm using generative adversarial networks are presented and compared with common algorithms in terms of recognition and classification efficiency and speed of processing.
The registration of two surfaces is finding a geometrical transformation of a template surface to a target surface. The transformation combines the positions of the semantically corresponding points. The transformation can be considered as warping the template onto the target. To choose the most suitable transformation from all possible warps, a registration algorithm must satisfies some constraints on the deformation. This is called regularization of the deformation field. Often use regularization based on minimizing the difference between transformations for different vertices of a surface. The variational functional consists of the several terms. One of them is the functional of the ICP (Iterative Closest Point algorithm) variational subproblem for the point-to-point metric for affine transformations. Other elements of the functional are stiffness and landmark terms. In proposed presentation we use variational functional based on the point-toplane metric for affine transformations. In addition, the use of orthogonal transformations is considered. The proposed algorithm is robust relative to bad initialization and incomplete surfaces. For noiseless and complete data, the registration is one-to-one. With the help of computer simulation, the proposed method is compared with known algorithms for the searching of optimal geometrical transformation.
In recent years, the importance of text detection in imagery has been increasing due to the great number of applications developed for mobile devices. Text detection becomes complicated when backgrounds are complex or capture conditions are not controlled. In this work, a method for text detection in natural scenes is proposed. The method is based on the Phase Congruency approach, obtained via Scale-Space Monogenic signal framework. The proposed method is robust to geometrical distortions, resolution, illumination, and noise degradation. Finally, experimental results are presented using a natural scene dataset.
The Histogram of Oriented Gradients (HOG) is a popular feature descriptor used in computer vision and image processing. The technique counts occurrences of gradient orientation in localized portions of an image. The descriptor is sensible to the presence in images of noise, nonuniform illumination, and low contrast. In this work, we propose a robust HOG-based descriptor using the local energy model and phase congruency approach. Computer simulation results are presented for recognition of objects in images affected by additive noise, nonuniform illumination, and geometric distortions using the proposed and conventional HOG descriptors.
Nowadays most of digital information is obtained using mobile devices specially smartphones. In particular, it brings the opportunity for optical character recognition in camera-captured images. For this reason many recognition applications have been recently developed such as recognition of license plates, business cards, receipts and street signal; document classification, augmented reality, language translator and so on. Camera-captured images are usually affected by geometric distortions, nonuniform illumination, shadow, noise, which make difficult the recognition task with existing systems. It is well known that the Fourier phase contains a lot of important information regardless of the Fourier magnitude. So, in this work we propose a phase-based recognition system exploiting phase-congruency features for illumination/scale invariance. The performance of the proposed system is tested in terms of miss classifications and false alarms with the help of computer simulation.
Proc. SPIE. 9217, Applications of Digital Image Processing XXXVII
KEYWORDS: Mobile devices, Detection and tracking algorithms, Digital filtering, Composites, Image resolution, Computer simulations, Image filtering, Optical character recognition, Nonlinear filtering, Binary data
Optical character recognition in scanned printed documents is a well-studied task, where the captured conditions like sheet position, illumination, contrast and resolution are controlled. Nowadays, it is more practical to use mobile devices for document capture than a scanner. So as a consequence, the quality of document images is often poor owing to presence of geometric distortions, nonhomogeneous illumination, low resolution, etc. In this work we propose to use multiple adaptive nonlinear composite filters for detection and classification of characters. Computer simulation results obtained with the proposed system are presented and discussed.