Statistical analysis of multilayer perceptrons performances
The paper is based on a series of studies on the learning capabilities of multilayered perceptrons (MLP). The complexity of these nonlinear systems can be varied, acting for instance on the number of hidden units, but we will be confronted with a choice dilemma, concerning the optimal complexity of the system for a given problem. By the mean of statistical methods, we have found that the effective number of hidden units is smaller than the potential size; some units have a "binary" activation level or a time constant activation. We also prove that weight initialization to small values is recommended and reduce the effective size of the hidden layer.
Improving Karhunen-Loeve based transform coding by using square isometries
We propose, for an image compression system based on the Karhunen-Loeve transform implemented by neural networks, to take into consideration the 8 square isometries of an image block. The proper isometry applied puts the 8*8 square image block in a standard position, before applying the image block as input to the neural network architecture. The standard position is defined based on the variance of its four 4*4 sub-blocks (quadro partitioned) and brings the sub-block having the greatest variance in a specific corner and in another specific adjoining corner the sub-block having the second variance (if this is not possible the third is considered). The use of this "preprocessing" phase was e…
Cloud motion detection from infrared satellite images
The estimation of cloud motion from a sequence of satellite images can be considered a challenging task due to the complexity of phenomena implied. Being a non-rigid motion and implying non-linear events, most motion models are not suitable and new algorithms have to be developed. We propose a novel technique, combining a Block Matching Algorithm (BMA) and a best candidate block search along with a vector median regularisation.
A New Framework for the Extraction of Contour Lines in Scanned Topographic Maps
3D simulations requested in various applications had led to the development of fast and accurate terrain topography measurement techniques. In this paper, we are presenting a novel framework dedicated to the semiautomatic processing of scanned maps, extracting the contour lines vectors and building a digital elevation model on their basis, fulfilled by a number of stages discussed in detail throughout the work. Despite the good results obtained on a large amount of scanned maps, a completely automatic map processing technique is unrealistic and remains an open problem.
Improving Lossless Image Compression with Contextual Memory
With the increased use of image acquisition devices, including cameras and medical imaging instruments, the amount of information ready for long term storage is also growing. In this paper we give a detailed description of the state-of-the-art lossless compression software PAQ8PX applied to grayscale image compression. We propose a new online learning algorithm for predicting the probability of bits from a stream. We then proceed to integrate the algorithm into PAQ8PX&rsquo
Automated PCB Inspection System
TEM Journal; Vol 6, No 2, 2017. ISSN 2217-8309
Extracting cloud motion from satellite image sequences
This paper present a new technique for the estimation of cloud motion, using a sequence of infrared satellite images. It can be considered a challenging task due to the complexity of phenomena implied, as non-linear events and a non-rigid motion. In this circumstances most motion models are not suitable and new algorithms have to be developed. We propose a novel method, combining an Automatic Multilevel Thresholding for image segmentation, a Block Matching Algorithm (BMA) and a best candidate block search along with a vector median regularization.
A Comparative Study of Block Matching Optical Flow Algorithms
TEM Journal; Vol 6, No 4, 2017. ISSN 2217-8309
Seam Puckering Objective Evaluation Method for Sewing Process
The paper presents an automated method for the assessment and classification of puckering defects detected during the preproduction control stage of the sewing machine or product inspection. In this respect, we have presented the possible causes and remedies of the wrinkle nonconformities. Subjective factors related to the control environment and operators during the seams evaluation can be reduced using an automated system whose operation is based on image processing. Our implementation involves spectral image analysis using Fourier transform and an unsupervised neural network, the Kohonen Map, employed to classify material specimens, the input images, into five discrete degrees of quality…
Image Inpainting Methods Evaluation and Improvement
With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorith…
A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury
Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach b…
Optimal Filter Estimation for Lucas-Kanade Optical Flow
Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filt…
Security Requirements, Counterattacks and Projects in Healthcare Applications Using WSNs - A Review
Healthcare applications are well thought-out as interesting fields for WSN where patients can be examine using wireless medical sensor networks. Inside the hospital or extensive care surroundings there is a tempting need for steady monitoring of essential body functions and support for patient mobility. Recent research cantered on patient reliable communication, mobility, and energy-efficient routing. Yet deploying new expertise in healthcare applications presents some understandable security concerns which are the important concern in the inclusive deployment of wireless patient monitoring systems. This manuscript presents a survey of the security features, its counter attacks in healthcar…
Accelerating Causal Inference and Feature Selection Methods through G-Test Computation Reuse
This article presents a novel and remarkably efficient method of computing the statistical G-test made possible by exploiting a connection with the fundamental elements of information theory: by writing the G statistic as a sum of joint entropy terms, its computation is decomposed into easily reusable partial results with no change in the resulting value. This method greatly improves the efficiency of applications that perform a series of G-tests on permutations of the same features, such as feature selection and causal inference applications because this decomposition allows for an intensive reuse of these partial results. The efficiency of this method is demonstrated by implementing it as…
Randomized Hough Transform for Ellipse Detection with Result Clustering
Our research is focused on the development of robust machine vision algorithms for pattern recognition. We want to provide robotic systems the ability to understand more on the external real world. In this paper, we describe a method for detecting ellipses in real world images using the randomized Hough transform with result clustering. A preprocessing phase is used in which real world images are transformed - noise reduction, greyscale transform, edge detection and final binarization - in order to be processed by the actual ellipse detector. The ellipse detector filters out false ellipses that may interfere with the final results. Due to the fact that usually more "virtual" ellipses are de…