KEYWORDS: Pathology, Mammography, Feature extraction, Image segmentation, Deep learning, Education and training, Breast, Classification systems, Cancer detection, Breast cancer
In this study, the main goal is to improve the performance of existing computer diagnostic systems by proposing new processing methods. We use the public CBIS-DDSM dataset for training and validation. The dataset consists of normal screenings with benign tumors and malignant tumors, with all pathologies carefully selected and checked by a radiologist. The data set also includes ROI masks and pathology bounding boxes, as well as labels corresponding to the class of each pathology diagnosis. To achieve better results on the dataset, we transform the data for their more efficient representation using autoencoders in order to obtain features with low intraclass and high interclass variance, and apply LDA to the encoded features to classify pathologies. Methods for automated pathology detection are not considered in this article, since it is mainly focused on the classification task itself. The entire pipeline of the system consists of the following steps: first, feature extraction using pathology segmentation; dividing the data into two clusters; feature transformation using linear discriminant analysis to minimize intra-class variance; finally, the classification of pathologies. The results of this study for the classification of pathologies using various deep learning methods are presented and discussed.
The paper deals with the design of a fast algorithm for computing the hopping discrete cosine transform in equidistant signal windows using a recursive relationship between transform spectra. Discrete cosine transform is widely used in digital signal processing such as image coding, spectral analysis, feature extraction, and filtering. Short-time transform is suitable for adaptive processing and time-frequency analysis of quasi-stationary data. Hopping transform refers to a transform computed on the signal of a fixed-size window that slides over the signal with an integer hop step. Hopping discrete transform can be employed for time-frequency analysis and adaptive processing quasi-stationary data such as speech, biomedical, radar and communication signals. The performance of the algorithm with respect to computational costs and execution time is compared with that of conventional sliding and fast algorithms.
In this paper, we first estimate the accuracy of 3D facial surface reconstruction from real RGB-D depth maps using various depth filtering algorithms. Next, a new 3D face recognition algorithm using deep convolutional neural network is proposed. With the help of 3D face augmentation techniques different facial expressions from a single 3D face scan are synthesized and used for network learning. The performance of the proposed algorithm is compared in terms of 3D face recognition metrics and processing time with that of common 3D face recognition algorithms.
This presentation deals with restoration of the image corrupted by impulsive noise using a novel cascade switching algorithm. The algorithm processes iteratively the observed degraded image changing adaptively parameters of the switching filter. With the help of computer simulation, we show that the proposed algorithm is able to effectively remove impulse noise from a highly contaminated image. The performance of the proposed algorithm is compared with that of common successful algorithms in terms of image restoration metrics.
In this paper, we reconstruct 3D object shape using multiple Kinect sensors. First, we capture RGB-D data from Kinect sensors and estimate intrinsic parameters of each Kinect sensor. Second, calibration procedure is utilized to provide an initial rough estimation of the sensor poses. Next, extrinsic parameters are estimated using an initial rigid transformation matrix in the Iterative Closest Point (ICP) algorithm. Finally, a fusion of calibrated data from Kinect sensors is performed. Experimental reconstruction results using Kinect V2 sensors are presented and analyzed in terms of the reconstruction accuracy.
In the present work new methods and algorithms for selecting features using deep learning techniques based on autoencoders will be proposed to provide high informativeness with low within-class and high between-class variance. The performance of the proposed methods in real indoor environments is presented and discussed.
The problem at solving which the project is aimed consists in the development of methods of constructing a threedimensional combined dense map are of the accessible environment and determining a position of a robot in a relative coordinate system based on a history of camera positions and the robot's motions, symbolic (semantic) tags.
In this paper, we propose an algorithm for the detection of local features in depth maps. The local features can be utilized for determination of special points for Iterative Closest Point (ICP) algorithms. The proposed algorithm employs a novel approach based a cascade mechanism, which can be applied for several 3D keypoint detection algorithms. Computer simulation and experimental results obtained with the proposed algorithm in real-life scenes are presented and compared with those obtained with state-of-the-art algorithms in terms of detection efficiency, accuracy, and speed of processing. The results show an improvement in the accuracy of 3D object reconstruction using the proposed algorithm followed by ICP algorithms.
KEYWORDS: Denoising, Reconstruction algorithms, Digital filtering, Magnetorheological finishing, 3D modeling, Image filtering, Data modeling, Nonlinear filtering, RGB color model, Clouds
In this paper, we estimate the accuracy of 3D object reconstruction using depth filtering and data from a RGB-D sensor. Depth filtering algorithms carry out inpainting and upsampling for defective depth maps from a RGB-D sensor. In order to improve the accuracy of 3D object reconstruction, an efficient and fast method of depth filtering is designed. Various methods of depth filtering are tested and compared with respect to the reconstruction accuracy using real data. The presented results show an improvement in the accuracy of 3D object reconstruction using depth filtering from a RGB-D sensor.
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