Detection, segmentation and classification of heart sounds
detectionmodel详细解析
detectionmodel详细解析Detection Model详细解析概述Detection Model，即检测模型，是计算机视觉中一种重要的算法模型。
它的主要任务是在图像或视频中识别和定位感兴趣的对象，如行人、车辆、动物等。
检测模型是计算机视觉领域中的基础模型之一，广泛应用于目标检测、人脸识别、自动驾驶等领域。
一、目标检测的基本原理目标检测的基本原理是通过对图像或视频中的每个像素进行分析和处理，识别出感兴趣的对象。
检测模型通常由两个主要部分组成：特征提取和目标分类。
1. 特征提取：特征提取是目标检测的前置工作，它通过对输入图像进行预处理和特征提取，将图像转化为一组有助于区分不同对象的特征向量。
常用的特征提取方法包括传统的手工设计特征和基于深度学习的卷积神经网络（Convolutional Neural Network，CNN）。
2. 目标分类：目标分类是目标检测的核心任务，它通过对提取的特征向量进行分类，判断图像中的每个区域是否含有目标对象。
常用的目标分类方法包括支持向量机（Support Vector Machine，SVM）、卷积神经网络（CNN）等。
二、常见的检测模型常见的检测模型主要包括传统的基于特征工程的方法和基于深度学习的方法。
1. 传统方法：（1）Haar特征检测：Haar特征检测是一种使用基于Haar小波的特征模板来检测对象的方法。
它通过计算图像中不同区域的灰度差异来判断对象的存在。
（2）HOG特征检测：HOG（Histogram of Oriented Gradients）特征检测是一种通过计算图像中像素梯度的方向和大小来判断对象的存在的方法。
它通过统计图像中不同区域的梯度直方图来提取特征。
2. 深度学习方法：（1）Faster R-CNN：Faster R-CNN是一种基于深度学习的目标检测算法，它通过引入区域提取网络（Region Proposal Network，RPN）和特征金字塔网络（Feature Pyramid Network，FPN）来提高检测的准确性和速度。
使用计算机视觉技术进行人脸检测的方法
使用计算机视觉技术进行人脸检测的方法近年来，计算机视觉技术的发展和应用日益成熟，人脸检测已经成为其中一个重要的研究领域。
人脸检测是指在图像或视频中准确地定位和识别人脸的过程。
在实际应用中，人脸检测被广泛地应用于人脸识别、视频监控、虚拟现实等领域。
本文将介绍几种常用的人脸检测方法，包括基于特征和机器学习的方法、基于深度学习的方法以及基于卷积神经网络的方法。
一、基于特征和机器学习的方法传统的人脸检测方法主要是基于特征和机器学习的方法。
这些方法主要通过提取图像中的特征，如颜色、纹理、边缘等，然后使用机器学习算法进行分类和识别。
其中，Haar特征是比较经典的人脸检测方法之一。
Haar特征是一种基于图像亮度差异的特征描述子，可以描述图像中不同区域的亮度变化情况。
通过计算和比较不同区域的Haar特征，可以判断该区域是否含有人脸。
通过训练和优化，可以得到一个检测器，可以在图像中快速准确地检测出人脸。
二、基于深度学习的方法近年来，随着深度学习的发展，基于深度学习的人脸检测方法取得了很大的突破。
深度学习通过构建多层的神经网络模型，可以学习到更加复杂和抽象的特征表示，从而提高人脸检测的准确率。
基于深度学习的人脸检测方法主要使用卷积神经网络（CNN）进行特征提取和分类。
CNN通过多层的卷积和池化操作，可以在图像中学习到不同层次的特征表示。
通过训练大规模数据集，CNN可以学习到辨别人脸和非人脸的特征，从而实现准确的人脸检测。
三、基于卷积神经网络的方法基于卷积神经网络的人脸检测方法是深度学习方法的一种变体。
这种方法的主要思想是通过训练一个多层的卷积神经网络模型，使其在图像中能够准确地检测出人脸。
基于卷积神经网络的人脸检测方法主要由两个阶段组成：候选框生成和候选框分类。
首先，使用滑动窗口的方式在图像中生成大量的候选框，然后使用卷积神经网络对这些候选框进行分类，判断是否为人脸。
通过训练和优化，可以得到一个准确的人脸检测器。
总结起来，人脸检测是计算机视觉领域的一个重要问题，在实际应用中有着广泛的应用前景。
基于人眼定位的快速人脸检测及归一化算法
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计算机视觉目标检测与像分割算法
计算机视觉目标检测与像分割算法计算机视觉目标检测与图像分割算法计算机视觉目标检测与图像分割是计算机视觉领域中的重要研究方向。
它们在图像处理、机器学习和人工智能等领域有着广泛的应用。
本文将介绍计算机视觉目标检测与图像分割的基本概念、算法原理以及应用实例。
一、计算机视觉目标检测算法计算机视觉目标检测算法旨在从图像中准确地检测和定位出感兴趣的目标。
它通常包括以下步骤：1. 图像预处理：对输入的图像进行预处理，例如降噪、图像增强等，以便提高后续处理的准确性和鲁棒性。
2. 特征提取：从预处理后的图像中提取特征，常用的特征提取方法包括边缘检测、色彩直方图、纹理特征等。
3. 目标检测：利用机器学习或深度学习算法对提取的特征进行目标检测。
常用的目标检测算法有基于特征匹配的方法、基于统计学习的方法以及基于深度学习的方法。
4. 目标定位：根据检测到的目标位置，将目标在图像中进行定位和标注。
计算机视觉目标检测算法在真实世界中有着广泛的应用，例如人脸识别、车辆检测等。
二、图像分割算法图像分割算法是将图像分为若干个具有语义或内容上下文相关的区域的过程。
它通常包括以下步骤：1. 图像预处理：与目标检测中的预处理步骤相似，对输入图像进行预处理以提高算法的准确性和鲁棒性。
2. 特征提取：从预处理后的图像中提取特征，常用的特征提取方法有颜色特征、纹理特征、边缘特征等。
3. 分割算法：根据提取的特征将图像进行分割，常用的分割算法有基于区域的方法、基于阈值的方法、基于图论的方法等。
4. 区域合并与过滤：对分割结果进行区域合并和过滤，以消除不需要的细节或噪声。
图像分割算法在图像处理、医学影像分析、智能交通等领域具有重要的应用价值。
三、计算机视觉目标检测与图像分割的应用实例计算机视觉目标检测与图像分割算法在众多领域有着广泛的应用。
以下是其中的两个实例：1. 人脸识别：在人脸识别领域，计算机视觉目标检测算法用于检测人脸的位置和姿态，并标注出重要的人脸特征点。
边缘检测中英文翻译
Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.An image may be defined as a two-dimensional function, f(x,y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultrasound, electron microscopy, and computer generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition,even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence(AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low, mid, and highlevel processes. Low-level processes involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higherlevel processing involves “making sense” of an ensemble of recognize d objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.” As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some formof organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnetic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other.Fig1Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image.A familiar example of enhancement is when we increase the contrast of an imagebecause “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” en hancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.F ig2Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmi it.Although storagetechnology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the pro cess that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital imageprocessing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge dectection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal b lur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problemof detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking outirrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholding parameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem insegmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.数字图像处理与边缘检测数字图像处理数字图像处理方法的研究源于两个主要应用领域：其一是改进图像信息以便于人们分析；其二是为使机器自动理解而对图像数据进行存储、传输及显示。
计算机视觉的关键技术和方法
计算机视觉的关键技术和方法
计算机视觉是一门涉及图像处理、模式识别和机器学习等多个
领域的交叉学科，它致力于让计算机具备类似甚至超越人类视觉的
能力。
在计算机视觉领域，有许多关键的技术和方法，以下是其中
一些重要的：
1. 特征提取与描述，特征提取是计算机视觉中的关键技术，它
指的是从图像或视频中提取出具有代表性的特征，比如边缘、角点、纹理等。
常用的特征描述方法包括SIFT、SURF和HOG等。
2. 目标检测与识别，目标检测与识别是计算机视觉中的重要任务，它指的是从图像或视频中识别出特定的目标，比如人脸、车辆、动物等。
常用的方法包括Haar特征级联、卷积神经网络（CNN）和
区域卷积神经网络（R-CNN）等。
3. 图像分割，图像分割是将图像分成若干个具有独立语义的区
域的过程，常用的方法包括阈值分割、边缘检测、区域生长和基于
图论的分割方法等。
4. 三维重建，三维重建是利用多幅图像或视频恢复出场景的三
维结构，常用的方法包括立体视觉、结构光和激光扫描等。
5. 运动估计，运动估计是计算机视觉中的重要问题，它指的是从图像序列中估计出物体的运动状态，常用的方法包括光流法、稠密光流法和结构光法等。
除了上述技术和方法外，计算机视觉还涉及到深度学习、神经网络、图像生成、图像增强、图像分类、图像检索等多个方面。
随着人工智能和计算机视觉的不断发展，这些关键技术和方法也在不断演进和完善，为计算机视觉的应用提供了更广阔的发展空间。
物体语义学
物体语义学
物体语义学是计算机视觉领域的一个重要研究方向，旨在通过对图像或视频中的物体进行语义理解和分类。
它涉及到识别、检测、分割和跟踪等任务，以实现对物体的智能认知和理解。
在物体语义学中，主要包括以下几个方面的内容：
1. 物体识别（Object Recognition）：通过对图像或视频中的物体进行分类，识别出不同类别的物体。
常见的方法包括使用深度学习模型，如卷积神经网络（CNN），对物体进行特征提取和分类。
2. 物体检测（Object Detection）：在图像或视频中定位和识别多个物体的位置。
物体检测旨在确定物体的边界框，并将其与相应的类别关联起来。
常用的物体检测方法包括基于区域的卷积神经网络（R-CNN）、YOLO（You Only Look Once）和SSD（Single Shot MultiBox Detector）等。
3. 物体分割（Object Segmentation）：将图像或视频中的每个像素分配给相应的物体类别，从而实现对物体的精确
分割。
物体分割可以是像素级别的（语义分割）或实例级别的（实例分割）。
常见的物体分割方法包括基于全卷积网络（FCN）和语义分割网络（如Mask R-CNN）。
4. 物体跟踪（Object Tracking）：在视频序列中实时追踪物体的位置和运动。
物体跟踪通常涉及目标初始化、目标检测和目标状态更新等步骤。
常见的物体跟踪方法包括基于相关滤波器、深度学习和多目标跟踪等。
物体语义学的研究对于计算机视觉、自动驾驶、智能监控等领域具有重要意义，它可以帮助计算机系统更好地理解和解释图像或视频中的物体信息，从而实现更高级别的视觉理解和应用。
检测分割分类算法
检测分割分类算法检测分割分类算法（Detection Segmentation Classification Algorithm）是一种用于图像处理和计算机视觉领域的重要算法。
它能够对图像进行分割，并对分割后的区域进行分类和检测。
本文将介绍检测分割分类算法的原理、应用领域以及一些常见的算法模型。
一、算法原理检测分割分类算法的基本原理是通过对图像进行分割，将图像中的不同区域分割出来，并对分割后的区域进行分类和检测。
其主要步骤包括图像分割、特征提取、分类和检测。
图像分割是指将图像划分为不同的区域，使得每个区域内的像素具有相似的属性。
常用的图像分割方法包括基于阈值的方法、基于边缘的方法和基于区域的方法。
特征提取是指从分割后的图像区域中提取出与分类和检测相关的特征。
常用的特征提取方法包括颜色直方图、纹理特征和形状特征等。
分类是指将提取到的特征输入到分类器中，通过训练分类器来对图像进行分类。
常用的分类方法包括支持向量机（SVM）、人工神经网络（ANN）和决策树等。
检测是指在分类的基础上，进一步对图像中的目标进行检测。
常用的检测方法包括滑动窗口和区域提议等。
二、应用领域检测分割分类算法在图像处理和计算机视觉领域有着广泛的应用。
以下是一些常见的应用领域：1. 目标检测：将图像中的目标进行检测，如人脸检测、车辆检测等。
通过检测分割分类算法，可以准确地定位和识别图像中的目标。
2. 图像分割：将图像分割为不同的区域，可以用于图像编辑、图像增强和图像压缩等应用。
通过检测分割分类算法，可以自动地对图像进行分割，并提取出与目标相关的区域。
3. 医学图像处理：在医学图像处理中，检测分割分类算法可以用于识别疾病区域、辅助诊断和手术导航等。
通过对医学图像进行分割和分类，可以提高医学图像的分析和处理效率。
4. 视频监控：在视频监控领域，检测分割分类算法可以用于目标跟踪、行为识别和异常检测等。
通过对视频图像进行分割和分类，可以实现对目标的准确跟踪和识别。
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•ห้องสมุดไป่ตู้When an interval exceeds the high-level limit, it is assumed that a peak has been lost and the threshold is decreased by a certain amount. It is repeated until the lost peaks are found or a certain limit is reached.
• Extra peaks were rejected by the following rules: if t2 −t1 ≤ 50ms (splitted peak) if a(t1) > 0.6a(t2 ) choose t1 else choose t2 else choose t2 (not splitted)
• Used narrow sliding windows (25ms) to compute 8th order AR model. • Features used : dominant poles (below 80Hz) and bandwidth. • Detected S1, S2 and murmurs.
Segmentation and Event Detection Cons
Suggested Methods
• Waveshape analysis – Homomorphic Filtering. • Temporal Model – (Semi) Hidden Markov Models.
Waveshape analysis - Homomorphic filtering
• Express the PCG signal x(t) by
where a(t) is the amplitude modulation (AM) component (envelope) and f(t) is the Frequency modulation (FM) component. • Define
Segmentation Using Wavelet Decomposition and Reconstruction
• Use Shannon energy to pick up the peaks above certain threshold. • Identify S1 and S2 according to set of rules similar to those used in segmentation with envelograms. • Compare the segmentation results of d4, d5 and a4. • The choosing criterion : more identified S1s and S2s and less discarded peaks.
Homomorphic Filtering Pros
• Provides smooth envelope with physical meaning. • The envelope resolution (smoothness) can be controlled. • Enables parametric modeling of the amplitude modulation for event classification (polynomial fitting ?). • Enables parametric modeling of the FM component (pitch estimation, chirp estimation ?)
Heart beat, why do you miss when my baby kisses me ?
B. Holly (1957)
PCG Analysis
• We will concentrate mainly on S1 and S2. • We will discuss only methods which do not use external references (ECG, CP or other channels). • Most of the methods are non-parametric or semiparametric (parametric models for the waveshape but non-parametric in the temporal behavior). • Suggestion for parametric modeling.
ˆ x(t) = ln a(t) + ln f (t)
ˆ ˆ xl (t) = L[x(t)]
• L is linear so :
ˆ xl (t) = L[ln a(t)] + L[ln f (t)] ≈ ln a(t)
ˆ exp[ xl (t)] ≈ exp[ln a(t)] = a(t)
• By exponentiation :
• Most of the methods are based on rules of thumb – no physical basis. • In most cases there is no parametric model of the waveshape and\or timing mechanism. • Not suitable for abnormal\irregular cardiac activity. • In case of AR model, there is still question of optimality : window size, order etc. In addition, there is no model for the timing mechanism of the events. • Heart sounds are highly non-stationary – AR model is very much inaccurate.
• The Shannon energy eliminates the effect of noise. • Use threshold to pick up the peaks.
Segmentation Using Envelogram
• Reject extra peaks and recover weak peaks according to the intervals statistics. • Recover lost peaks by lowering the threshold
Features of PCG
• The envelope of PCG signals might convey useful information. • In order to detect\segment\classify cardiac events we might need temporal information.
Labeling
• The longest interval between two adjacent peaks is the diastolic period (from the end of S2 to the beginning of S1). • The duration of the systolic period (from the end of S1 to the beginning of S2) is relatively constant
Segmentation Using Envelogram
• Identify S1 and S2 according the intervals between adjacent peaks.
Segmentation Using Wavelet Decomposition and Reconstruction (Liang et al. 1997)
Labeling
Thus • Find the longest time interval. • Set S2 as the start point and S1 as the end point. • Label the intervals forward and backward.
Normal heart beat with the labels found
Detection, segmentation and classification of heart sounds
Daniel Gill Advanced Research Seminar May 2004
Automatic Cardiac Signals Analysis
Problems : 1. Pre-processing and noise treatment. 2. Detection\segmentation problem. 3. Classification problem:
Segmentation Using Wavelet Decomposition and Reconstruction
AR modeling of PCG (Iwata et al. 1977, 1980) AR model :
y(n) = ∑ak y(n − k) + ε (n)
k= k=1
p
a. Feature extraction – waveshape & temporal information. b. The classifier.
Outline
• Methods based on waveshape : - Envelogram - Wavelet decomposition and reconstruction - AR modeling - Envelogram estimation using Hilbert transform • Suggested method : Homomorphic analysis • Suggested temporal modeling : Hidden Markov Models