A Simple Parallel Convex Hulls Algorithm for Sorted Points and the Performance Evaluation on the Multicore Processors

Salient object detection is a fundamental problem in both pattern recognition and image processing tasks. Previous salient object detection algorithms usually involve various features based on priors/assumptions about the properties of the objects. Inspired by the effectiveness of recently developed feature learning, we propose a novel deep salient object detection (DSOD) model using the deep residual network (ResNet 152-layers) for saliency computation. In particular, we model the image saliency from both local and global perspectives. In the local feature estimation stage, we detect local saliency by using a deep residual network (ResNet-L) which learns local region features to determine the saliency value of each pixel. In the global feature extraction stage, another deep residual network (ResNet-G) is trained to predict the saliency score of each image based on the global features. The final saliency map is generated by a conditional random field (CRF) to combining the local and global-level saliency map. Our DSOD model is capable of uniformly highlighting the objects-of-interest from complex background while well preserving object details. Quantitative and qualitative experiments on three benchmark datasets demonstrate that our DSOD method outperforms state-of-the-art methods in the salient object detection.

Edge detection is one of the most important tasks in image processing and pattern recognition. Edge detector with multiple color channels can provide more edge information. However, the uncertainty occurring with the edge detection in each single channel and the discordance existing in the fusion of multiple channels edge detectors make the detection difficult. In this paper, we propose a new edge detection method in color images based on information fusion. We show that obtaining final edge through fusing the edge information in each channel is a challenging problem to make decision in the framework of Multi-Criteria decision making (MCDM). In this work, we propose to detect edges in color images using Cautious OWA with evidential reasoning (COWA-ER) and Fuzzy-Cautious OWA with evidential reasoning (FCOWA-ER) to handle the uncertainty and discordance. Experimental results show that the proposed approaches achieve better edge detection performance compared with the original edge detector.

An efficiency model for general purpose instruction level parallel architectures in image processing

Automatically determine the membership function based on the maximum entropy principle

ABSTRACTComplex processing algorithms associated with requirements for real‐time target detection, acquisition and recognition have far outdistanced our ability to package the necessary processing power into real‐time weapon‐sized hardware. Weapon developers need new, radically different, approaches to solve these difficult weapon signal processing problems. Artificial neural networks (ANN) is one of the new, radically different, signal processing approaches that is currently receiving much attention for weapon applications. The human brain is a powerful image and pattern recognition processor whose basic processing element is the neuron. Synapses are the weighted interconnections between neurons that permit learning and communication between the neurons. Artificial neural network technology attempts to mathematically and/or electrically model neurons and synapses and then interconnect these models in architectures suitable for signal processing tasks.ANN technology is particularly applicable to pattern recognition, speech recognition, machine vision, robotics, and optimization signal processing tasks. Specific military applications include missile seekers, missile fuzing, sonar target discrimination, automatic target recognition, and autopilots. Two unique characteristics of ANN processors are that they are non‐linear processors and that they are trained, not programmed, to accomplish processing tasks in a manner analogous to the way the human brain learns. Learning is achieved by modifying the synaptic weights of each artificial neuron until the final desired system processing response is achieved.Several years ago the Naval Weapons Center (NavWpnCen) identified ANN technology as a high risk‐high payoff approach to missile signal processing requirements and initiated several research and development efforts in this area. One result of this work is the new 80170NW ANN analog VLSI chip produced by Intel Inc. Another is the current Missileborne Integrated Neural Network Demonstration (MINND) Advanced Technology Demonstration (ATD) project to develop a hybrid analog/digital artificial neural computer for real‐time processing of data from imaging missile seekers. The neural computer will use several Intel chips interconnected in a flexible architecture, which is loosely based on the operation of the human retina, to demonstrate desired target/background discrimination and target detection and tracking processing capabilities. This paper provides an introduction to artificial neural network technology and discusses some of the results that have been achieved by applying ANN technology to weapon system problems at the Naval Weapons Center.

According to the World Health Organization (WHO), depression is one of the largest contributors to the burden of mental and psychological diseases with more than 300 million people being affected; however a huge portion of this does not receive effective diagnosis. Traditional techniques to diagnose depression were based on clinical interviews. These techniques had several limitations based on duration and variety of symptoms, due to which these methods lacked subjectivity and accuracy. Speech is tested to be an important tool in diagnosis as they carry the impression of one’s thoughts and emotions. Speech signals not only carry the linguistic feature but they also contain several other features (paralinguistic features) which can reflect the emotional state of the speaker. The analysis of these features can be used for the diagnosis of depression. With the advancement of artificial techniques and algorithms, they have become popular and are widely used in tasks of pattern recognition and signal processing. These algorithms can easily extract the features from the data and learn to recognize patterns from them. Although these algorithms can successfully recognize emotions, their efficiency is often argued. The main objective of this paper is to propose a strategy to efficiently diagnose depression from the analysis of speech signals. The analysis is performed in the following two ways: First, by considering the male and female emotions combined (gender-neutral) where they are classified into two classes, and second, separately for the male and female emotions (gender-based) for a total of four classes. Experiments conducted show the advantages and shortcomings of paralinguistic features for diagnosis of depression. During experimentation we tested several architectures by efficiently tuning the hyperparameters. For K-nearest neighbors (KNN), best attained accuracy was 86%, whereas for Multi-Layer Perceptron (MLP) architecture the accuracy attained was 87.8%. Best results were obtained from hybrid 1D-Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) architecture with the accuracy of 88.33% and 90.07% for gender-neutral and gender-based respectively.

Summary. Pattern recognition has as its objective to classify objects into different categories and classes. It is a fundamental component of artificial intelligence and computer vision. This chapter investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing. First a clustering method that is based on PSO is discussed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. Then PSObased approaches that tackle the color image quantization and spectral unmixing problems are discussed.

Occipital Activation by Pattern Recognition in the Early Blind Using Auditory Substitution for Vision

A foveated vision of a human visual processing system is known to reduce the amount of information passed to subsequent processing layers significantly and lends itself to fast image processing and pattern recognition tasks. In order to realize such a vision chip based on the foveated vision processing system, we propose two concepts: the receptive field and the refractory period. Our vision chip can focus the fovea on a target electrically using these concepts.

Objects or events in the universe are perceived by biological systems as patterns. Pattern recognition is a process which assigns these sensory stimuli into perceptually meaningful categories. For the last four decades a considerable effort has been made to simulate the human pattern recognition capabilities by a machine. This quest for automation of pattern recognition processes is primarily driven by applications in computer vision for flexible manufacturing, speech recognition, text recognition, remote sensing, medicine and others. In computer vision for robots, for instance, the pattern recognition task may involve identification of object shape. In speech recognition the object categories may be words, phonemes or diphones and the sensory data on which classification is based could be vector quantised speech signal. In text recognition the objects of interest are characters and their groups forming words. Object categories in remote sensing relate to land cover and the sensory data are reflected energies in several spectral channels of the electromagnetic spectrum.

In this paper Neural Networks are presented in the context of Statistical Pattern Recognition, focusing the attention on all the steps needed to classify and interpolate input data. Standard multi-layer models are briefly illustrated, and then proved to be good instruments for data interpolation and Bayesian classification. Furthermore, Neural Networks are presented in the pre-processing stage, both for input reduction and clustering. Finally, two applications to signal and image processing are summarized to show the potentiality of Neural Network based systems in real world Statistical Pattern Recognition problems.KeywordsNeural NetworkWeight VectorInput PatternPrincipal EigenvectorStatistical Pattern RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The ionosphere is an important component of the near Earth space environment. The three common methods for detecting the ionosphere with high frequency (HF) radio signals are vertical detection, oblique detection, and oblique backscatter detection. The ionograms obtained by these detection methods can effectively reflect a large amount of effective information in the ionosphere. The focus of this article is on the oblique backscatter ionogram obtained by oblique backscatter detection. By extracting the leading edge of the oblique backscatter ionogram, effective information in the ionosphere can be inverted. The key issue is how to accurately obtain the leading edge of the oblique backscatter ionogram. In recent years, the application of pattern recognition has become increasingly widespread, and the YOLO model is one of the best fast object detection algorithms in one-stage. Therefore, the core idea of this article is to use the newer YOLOX object detection algorithm in the YOLO family to perform pattern recognition on the F and Es layers echoes in the oblique backscatter ionogram. After image processing, a single-layer oblique backscatter echoes are obtained. It can be found that the leading edge extraction of the oblique backscatter ionogram obtained after pattern recognition and image processing by the YOLOX model is more fitting to the actual oblique backscatter leading edge.

Image segmentation is the first step in image analysis and pattern recognition, and it is one of the most difficult tasks in image processing, and determines the quality of the final result of analysis. Image segmentation requires the improvement because most image segmentation solution is problem based. Biomedical image segmentation is different than normal image segmentation. Because normal image is taken from camera which is free of noise. While biomedical image is taken from MRI, CT, Microscopic Instrument or X-ray which is it noisy and fuzzy. Medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested. While extracting region of interest in biomedical image, region of interest can avoid the processing of irrelevant image point. Biomedical Image segmentation is necessary when we want the computer to make decision, surgical planning and robotically assisted surgical invention. Biomedical image segmentation is nothing but the classification of similar pixel and their following extraction into separate segment. There are different methods to do the Biomedical image segmentation but they are slow. he method which is described in this paper is fast and efficient. This paper gives comparative research of different method of each of category. The method described in the paper is for fast biomedical image segmentation

Image segmentation is necessary but significant element in less intensity image investigation, pattern recognition, and in robotic systems. It is one of the most complex and demanding tasks in image processing. Image segmentation is the process of separating an image into various regions such that each region is identical. This paper proposes a new medical image segmentation method that integrates multi-resolution wavelet packet decomposition with the watershed transform for MRI image. The wavelet packet transform (WPT) is applied to the input image, creating detail and approximation coefficients. If watershed technique alone is used for segmentation, then over cluster is present. To overcome this, the proposed technique which combines wavelet packet and watershed algorithm is developed. First, the wavelet packet transform is applied to produce multi-resolution images, followed by applying watershed for segmentation to the approximation sub-bands. Finally, Inverse WPT is implemented to obtain the segmented image. Due to wavelet packet decomposition, the quantity of the disturbance can be decreased and leads to a tough segmentation. This proposed work concludes that wavelet packet and watershed transform facilitate to get the elevated precision even in strident images.

Scene analysis for robotic watercraft