Image Change Detection

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Image Change Detection and Statistical Test

Image Change Detection and Statistical Test

In this study, image ratio differencing and statistical test were adopted to make decisions on change area on the bi-temporal Landsat images. From the positive sides, NDVI differencing is straightforward and practical: (1) NDVI has the advantage of maximizing the spectral difference between vegetation and man-made features; (2) Univariate image analysis does not need to concern about the redundancy among multispectral bands; (3) The distribution of dNDVI is roughly bell- shape, indicating that changes randomly took place across the research area and dNDVI should be a random variable, the value of which is a combination of true signal and random noise. This is the basis of hypothesis test, although it can be easily violated by spatial variable due to the spatial autocorrelation and spatial heterogeneity. Furthermore, hypothesis test, as inferential statistic, can handle noises and uncertainties in image change detection. The advantages of hypothesis test can be summarized as follows: (1) Instead of hard binary change decision, the hypothesis test can produce a soft probability change decision; (2) Change probability can simultaneously denotes the strength of decision and uncertainty; (3) From a frequentist perspective, probability is regarded as the long-run frequency of an outcome occurring, while from a Bayesian perspective, probability quantifies a degree of belief in our decisions. Such idea strengthens our determination to establish land use class frequencies as prior probabilities, instead of worrying about the assumption of fixed mean from samples or population.

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Image Change Detection by Means of Discrete Fractional Fourier Transform

Image Change Detection by Means of Discrete Fractional Fourier Transform

year that the other transforms could also be fractionalized [6]. McBride and Keer explored the refinement and mathematical definition in 1987 [7]. In a very short span of time, FrFT has established itself as a commanding tool for the analysis of time varying signals [8-9]. It has gained prominence in signal processing, optics and quantum mechanics during last two decadesBut when FrFT is considered in discrete domain there are many definitions of Discrete Fractional Fourier Transform (DFrFT) [10-11]. It has been recently observed that DFrFT can be used in the field of image processing .The vital feature of Discrete Fractional Fourier domain Image compression aids from its extra degree of freedom that is provided by its fractional orders. The 1D DFrFT is useful in processing signals such as speech waveforms (one dimensional signal). For analysis of 2D signals such as images, a two dimensional version of DFrFT is required. For an M x N matrix, the 2D DFrFT is computed in an unpretentious way. The 1D DFrFT is applied to each row of given matrix and then same is applied to each column of the result matrix. Thus, the generalization of the DFrFT to 2D is given by taking the DFrFT of the rows of the matrix i.e. image in a fractional domain and then taking the DFrFT of the subsequent column wise. In case of 2D DFrFT, two angles of rotation α= and β= have to be taken. If one of these angles is zero, the 2D transformation kernel reduces to the 1D transformation Kernel. Image Change Detection using DFrFT involves a simple methodology, firstly the difference image is obtained and then by applying DFrFT of flexible order (0-1), we acquire different version of the difference image. Regions are marked on the change image with some criteria to find image change detection. The paper is organized as follows:

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Hyperspectral Image Change Detection Using Hopfield Neural Network

Hyperspectral Image Change Detection Using Hopfield Neural Network

The Change detection refers to recognizing dissimilarities arising in the characteristics of an object, over a period of time. Widespread application of change detection in hyper spectral images in areas like remote sensing, machine vision, video compression, military reconnaissance, etc. has made it demanding area of research. In image processing, detecting changes is an essential and crucial component. Existing work employed change detection mechanism in multidimensional unlabeled data. Features namely lowest variances are extracted using PCA for change detection. However efficient change detection in very high-resolution data namely hyper spectral images are not done with SPLL. The proposed system detects changes in hyper spectral images using Hopfield Neural Network. Hyper spectral remote sensing images offer more detailed information on spectral changes so as to present promising change detection performance. The proposed system which models spatial correlation between neighbouring pixels of the difference image extracts the spectral and spatial correlated features using PCA for detecting changes in the images. Experimental result reveals better result when compare with the existing system.

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EFFICIENT & ANALYSIS OF PCA BASED IMAGE CHANGE DETECTION ALGORITHMS

EFFICIENT & ANALYSIS OF PCA BASED IMAGE CHANGE DETECTION ALGORITHMS

By the help of some tools of Adobe Photoshop we did some changes in the still images (captured by a camera). The size of each image should be restricted because the size should be easily divisible to produce the number of sub-blocks. Six still images and two remotely sensed images taken in different date/time were chosen as input images for our experiment. The selected area for the remotely sensed input images for this research is the Madurai city, Tamilnadu, India. Madurai is a historical city and it is the gateway of south Tamilnadu. It is one of the mini metros in India. The city is well known for its architectural marvels and its rich cultural heritage. The city is known as the Athens of East. Madurai is situated between the longitude 78° 04‟ 47” E to 78° 11‟ 23” E and the latitude 9° 50‟ 59” N to 9° 57‟ 36” N. The topography of Madurai is approximately 101 meters above the sea level. The city lies in the interior of Tamilnadu. The present area of the city is 51.9 square kilometers. Now due to economic advancement of the people of the city the rural hamlets in the district are given an urban image by people. With the view to present an analytical perspective for the socio economical issues pertaining to Madurai, it is needed to analyse the spatial pattern such as urban sprawl, to predict the future growth. The kinds of satellite data with different resolution and acquisition dates (LISS II and LISS III) were used in this research is given in table 1. The pre-processed multispectral temporal input image data is shown in figure1.

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SAR 
		image change detection using Gaussian mixture model with spatial 
		information

SAR image change detection using Gaussian mixture model with spatial information

Change Detection [4, 10] is the method of analyzing two images taken at different times over the same geographical area and identifying the changes that have taken place between the two different acquisition times. Synthetic Aperture Radar (SAR) system offers a wide coverage area and it is insensitive to the weather and illumination conditions. Hence it finds vast applications in Remote Sensing. The drawback existing in SAR image is that it they contain speckle noise. This makes the change detection of SAR images more challenging than the other optical images.

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Image Change Detection using Discrete Fractional Fourier Transform along with Intensity Normalization and Thresholding

Image Change Detection using Discrete Fractional Fourier Transform along with Intensity Normalization and Thresholding

appearances/disappearances of cars. Change Detection between these two images using with a threshold of 0.11 and with a region size of area greater than 200 pixels is carried out and the result is shown in Figure 2(d). In above figure 2(d), we classified regions as main change region (red rectangles), low certainty change regions (yellow rectangle), and no change or non-significant regions (green rectangle) if any. In this case, precision value for proposed method is increased by 60% than previous method, but recall value is same for both methods. Results showing that no false and missed regions are detected with proposed method.

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Change Detection

Change Detection

4. How might detection of dynamic change differ from detection of completed change? At a conceptual level, there is a considerable difference between these two types of detection, suggesting that separate mechanisms may be involved (see Dynamic vs. Completed Change, above). However, whether this division really exists in the human visual system remains unknown. Interestingly, a phenomenological dissociation appears to exist in the de- tection of gap-contingent change, with detection of dynamic change (see- ing the dynamic transformation) found for interstimulus intervals of about 300 ms or less, and detection of completed change (seeing that something has changed) for longer intervals (e.g., Phillips 1974, Rensink et al. 2000a; see also Bridgeman et al. 1975). To establish this difference firmly would require behavioral correlates (see Merikle & Reingold 1992). A possible candidate in this regard might be the finding of two separate mechanisms for the detection of displacement: one for intervals less than 200 ms, the other for intervals greater than 500 ms (Palmer 1986).

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Forgery Detection using Noise Inconsistency:          A Review

Forgery Detection using Noise Inconsistency: A Review

Exchangeable Image File Format (EXIF) header features for detecting manipulation. Image manipulations like brightness and contrast enhancements can alter the noise features of the image. The authors observe the numerical differences between the original EXIF features and the corresponding EXIF features from the estimated noise features. That difference can serve as a great indicator to determine if the image is the original one that is taken from a camera source or it has gone through some manipulations. Again some specific camera models were examined by this method. Ahmer Emir Dirik and Nasir Memon [14] proposed a detection method which was applicable to various operations like splicing, retouching, recompression, resizing, blurring etc. But it did not target any specific operation.

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Brain Tumor Analysis using SVM and Score function

Brain Tumor Analysis using SVM and Score function

UNWANTED growth of cells inside the skull is termed as brain tumor. They classified into two - benign (non-cancerous) and malignant (cancerous) tumors. The first one is slow growing tumors which causes potentially damaging pressure but does not spread into surrounding brain tissue. But the second one is rapid growing tumor and able to spread into surrounding brain.Magnetic resonance imaging (MRI) is the medical imaging method used for diagnosis of brain tumor. The rich information that MR images provide about the soft tissue anatomy has dramatically improved the quality of brain pathology diagnosis and treatment. It produces high quality images of the anatomical structures of the human body, especially in the brain, and provides rich information for clinical diagnosis and biomedical research. However, the amount of data is far too much for manual interpretation and hence there is a great need for automated image analysis tools.

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CRL 97 2 pdf

CRL 97 2 pdf

There are two restrictions on reading and writing channel items in the STM which ensure that garbage collection can occur. The first is the write-once nature of data items: Once an item with a particular time-stamp has been written, it cannot be modified. It can be read an arbitrary number of times and will be garbage collected when it is no longer needed. The write-once property ensures that a thread never has to read a particular time-stamped item more than once. A second implication is that time- stamps whose data has been garbage collected need not be supported by the STM. This property is consistent with the dynamic nature of data in the STM. For example, a given video frame will not change once it has been digitized, but a new frame will be produced every 33 ms.

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The research on edge detection algorithm of lane

The research on edge detection algorithm of lane

The edge of the image is the singularity and mutation point of the grayscale function of the image, that is, the area of the image where the grayscale information changes drastically. Along the pixels at the edges of the image, the gray value changes slowly, and the pixels that are perpendicular to the edge direction have drastically changed gray values. The nighttime road surface image is affected by uneven illumination, so it is not suitable to use a special threshold segmentation algorithm to obtain binary images. The edge detection algorithm can obtain binary images without using a special threshold segmen- tation algorithm. The edge of the object is the region where the local brightness of the image changes signifi- cantly. The identification and extraction of the image edge is very important for the recognition and under- standing of the entire image scene. It is also an import- ant feature that the image segmentation depends on. The commonly used edge detection operator is the Sobel operator, LOG operator, Kirsch operator, Canny operator, etc. [14]. Various algorithms have advantages and disadvantages for different occasions. The purpose of this paper is to explore an algorithm that can detect

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An Efficient Change Detection Model for Hyper Spectral Images Using Hierarchical Clustering Algorithm

An Efficient Change Detection Model for Hyper Spectral Images Using Hierarchical Clustering Algorithm

A comprehensive understanding of the global change is necessary for sustainable development of human society. As one of the interesting subtopics in global change study, detection of anthropogenic and natural impacts on land surface is essential for environmental monitoring. To enable whole monitoring and evaluation of changes occurred on the ground, both long-term and short-term observations are required. Due to the revisit property of polar Earth Observation (EO) satellites, we can acquire remote sensing images in a given area at different times. Thus, multi-temporal remote sensing images are an important data source to detect the land surface changes in wide geographical areas, which is gradually reducing the need for conventional field investigations. Change detection (CD) is the process that identifies changes occurred between two (or more) images based on the image properties [2].

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VAISHALI SHARMA et al , International Journal of Computer Science and Mobile Applications,

VAISHALI SHARMA et al , International Journal of Computer Science and Mobile Applications,

Hard Clustering: Hard Clustering is a simple clustering technique that divides the image into a set of clusters so that the pixel can belong to only one group. In other words, it can be said that each pixel can belong exactly to a cluster. These methods use membership functions with values of 1 or 0, that is, one of the pixels may belong to a certain cluster or not. An example of a technique based on hard clustering is a technique based on the k-means clustering known as HCM. In this technique, the centers are first calculated and then each pixel is assigned to the nearest center. It stands out by maximizing intra-cluster similarity and also minimizing equality between clusters.

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Research on Illegal Building Detection Based on the Change Discovery and Spatial Morphological Characteristics of Image

Research on Illegal Building Detection Based on the Change Discovery and Spatial Morphological Characteristics of Image

Abstract. In the process of traditional urban enforcement supervision, the illegal building detection are mainly based on the inspection of the naked eye. Due to factors such as fatigue and the environment, the detection efficiency is low and it is error-prone. This article focuses on the automatic detection algorithm of fixed-point monitoring video images as samples, and performs background separation, match, repair and other pre-processing on the sampled video images of different phases. For the weather blocking the surrounding environment and many other interference factors, the morphological operators are used to filter and extract clear areas of change, then the HU moments and template matching evaluation function are combined to clean the changed sub-regions, and finally the spatial characteristics are studied to detect the building. The experimental results show that this scheme not only improves the computational efficiency but also ensures the recognition rate.

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Abstract: The lane line detection is through the on-board camera to deal with the captured image, to extract the

Abstract: The lane line detection is through the on-board camera to deal with the captured image, to extract the

Abstract: The lane line detection is through the on-board camera to deal with the captured image, to extract the lane line information from the image. The lane line detection is often applied to the advanced driving assistance systems, such as adaptive cruise lane, lane departure warning, maintain, secondly it is unmanned intelligent car vision navigation. Real-time performance, robustness and accuracy is the goal of the lane line detection research, including unmanned intelligent car visual navigation of the lane line detection real-time performance, robustness and accuracy requirements of the highest. The lane line detection method according to whether using inverse perspective image (have a bird's eye view of the image) detection is divided into the lane line detection method based on the perspective of image and image the lane line detection method based on inverse perspective.

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Use Human Facial Features To Recognition Person

Use Human Facial Features To Recognition Person

detection to find the edge of the eye, and the grayscale processing before binarization preserves the edge of the eye by setting the threshold value. The critical value method will be converted from Fig 5 (b) to a grayscale image, and then the grayscale value that is added to the number of 25% grayscale values from the number of grayscale values of 0 is used as the threshold value. After the binarization results are shown in Fig 5(c), the edge breaks and the isolated black dots in the edge are connected or filled through the closing operation, as shown in Fig 5(d), but in Fig 5(d) It is found that there are still some small white spots or blocks on the periphery of the eye. At this time, the study removes these points or blocks by retaining the largest block and removes the minimum block. After removal, it is shown in Fig 5(e) [8] Finally, in order to avoid jaggies in the corners of the eyes, a four-connected method is used to fill in the jagged portions of the corners of the eyes and fill them up. However, even the part between the eyes also followed from the end of eye filled, but also to achieve the effect is more prominent appearance of the eyes, filled with complete results shown in Fig 5 (f).

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GIS-based Landsat Image Classification for Change Detection and Prediction LULC Dynamics: Case Study Kilombero District-Tanzania

GIS-based Landsat Image Classification for Change Detection and Prediction LULC Dynamics: Case Study Kilombero District-Tanzania

With the Landsat program running for over four decades now different methods for digital image analysis, GIS- based Landsat image classification, post classification, accuracy assessment, change detection and modeling LULC dynamics have been developed. In principle, Landsat image analysis entails digital image processing which involves manipulation and interpretation of the digital image data by specific computer program s to display and extract meaningful information about the surface of the earth (Paul, 2013). Pre-processing and digital image classification which is among the basic image analysis processes governs most of the LULC change detection study (Bruce & Hilbert, 2004 & Canavosio-Zuzelski, 2011). While digital Image classification involves the process of categorizing all pixels in an image or raw remotely sensed satellite data to obtain a given set of labels or land cover themes (Gelsema, 1997). Land cover classification methods using Landsat images originated from early aerial photo interpretation methods which were common in the 1950s and 1960s where land cover was classified based on visible image properties such as texture, color, shape and compactness (Amin & Fazal, 2012)

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Distance Learning Approach in Engineering – Pluses and Minuses

Distance Learning Approach in Engineering – Pluses and Minuses

This section briefly describes the proposed approach for detecting possible structural failures of temporary structures. More details are provided in later sections. Video sequences collected with high resolution video cameras are analyzed to detect early symptoms of structural failurescaused by various events, such as accumulated stress, deflection, wind, vibration, and lateral forces. These hazardous events tend to deform the materials used for shoring, and thus the ability to detect material deformation as early as possible plays a key role in efforts to prevent the disastrous collapse of a complete structure. The proposed approach consists of two steps: 1) learning and 2) failure detection. In the first step, deformation characteristics of the shoring materialsare extracted by analyzing video sequences of simulated failure situations, and then thesecharacteristics are converted into time-dependant signals to train a Hidden Markov Model (HMM). Later, video inputsare processed in the same manner and then fed into the HMM to draw inferences regarding a possible failure and its causes. The overall procedure used for training and detection is summarized in Figure 3, and details of each step are explained in the following subsections.

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Survey on Change Detection in SAR Images with Image Fusion and Image Segmentation

Survey on Change Detection in SAR Images with Image Fusion and Image Segmentation

In the case of space borne synthetic aperture radar (SAR) imagery, change detection techniques have been developed for the temporal tracking of multiyear sea-ice floes using Seasat SAR observations. Change detection techniques for SAR data can be divided into several categories, each corresponding to different image quality requirements. In a first category, changes are detected based on the temporal tracking of objects or stable image features of recognizable geometrical shape. Absolute calibration of the data is not required, but the data must be rectified from geometric distortions due to differences in imaging geometry or SAR processing parameters, and the accurate spatial registration of the multidate data is essential. Combining information acquired from multiple sensors has become very popular in many signal and image processing applications. In the case of earth observation applications, the fusion of the data produced by different types of sensors provides a

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An Application of MAP-MRF to Change Detection in Image Sequence Based on Mean Field Theory

An Application of MAP-MRF to Change Detection in Image Sequence Based on Mean Field Theory

which corresponds to a significant level of 0.005. In the lat- ter method, the parameters α , β , and z (see [20]) were set to 0 . 5, 0 . 9, and 3, respectively. The parameter k in M3 was tested from 2 to 5 and k = 4 was selected, which produced the best overall performance for the test sequences. These pa- rameter values were utilized for all the test sequences (syn- thetic and natural). The results of QPF and M3 methods are illustrated in Figures 4a-4b and 4c-4d, respectively. Com- pared with the CDMs shown in Figure 3, these two meth- ods appear to be more sensitive to the simulated noise. The error rates of the three methods are illustrated in Figure 5, which shows that the MRF method performed better than the two existing methods in terms of less false detection. It is seen that the error rate of the MRF method decreases as frames 1 through 30 are being processed, then becomes sta- ble after that. The reason is that p ( d | h = 1) adapts gradually to the test data at the initial frames, and then becomes sta- tionary. The adaptation speed is quite satisfactory for most common applications, as indicated by our results using other videos.

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