A robust approach to detect gas bubbles through images analysis

In this research, we attempted to elucidate brain mechanism relates to processing of emotion of happy by using an fMRI scanner to detect brain activation induced by images that include not only a face image of happy facial expression but also background images. In previous study, the number of reports about brain activation relates to emotion of happy is relatively small when compared with it of fear and disgust. We assume the one of the reasons of this fact is that brain activation induced by emotion of happy is relatively smaller than that of fear or disgust. Then showing image of happy facial expression combined with proper background image may induce significant brain activation related to emotion of happy because proper background image is able to enhance the impression of image of happy facial expression. In this research, the images of face expressing happy and neutral with two background images (a flower garden and cherry blossoms) and no background were prepared. As a result, the analysis of happy face images with the cherry blossoms background minus neutral face with the cherry blossoms background showed significant brain activation in the hippocampus of the right hemisphere of the brain. From the result of questionnaire, the difference of impression of happy between happy face and neutral face showed largest when background image of cherry blossoms was used compared with when background of a flower garden or no background image was used. Questionnaire showed that impression of face was enhanced by background image of cherry blossoms. This effect of enhancement of background image may cause the significant brain activation in the hippocampus.Keywordshappyfacebackground image and fMRI

The main steps of image analysis are image capturing, image storage (compression), correcting imaging defects (e.g. non-uniform illumination, electronic-noise, glare effect), image enhancement, segmentation of objects in the image and image measurements. Digitisation is made by a camera. The most modern types include a frame-grabber, converting the analog-to-digital signal into digital (numerical) information. The numerical information consists of the grey values describing the brightness of every point within the image, named a pixel. The information is stored in bits. Eight bits are summarised in one byte. Therefore, grey values can have a value between 0 and 256 (2(8)). The human eye seems to be quite content with a display of 5-bit images (corresponding to 64 different grey values). In a digitised image, the pixel grey values can vary within regions that are uniform in the original scene: the image is noisy. The noise is mainly manifested in the background of the image. For an optimal discrimination between different objects or features in an image, uniformity of illumination in the whole image is required. These defects can be minimised by shading correction [subtraction of a background (white) image from the original image, pixel per pixel, or division of the original image by the background image]. The brightness of an image represented by its grey values can be analysed for every single pixel or for a group of pixels. The most frequently used pixel-based image descriptors are optical density, integrated optical density, the histogram of the grey values, mean grey value and entropy. The distribution of the grey values existing within an image is one of the most important characteristics of the image. However, the histogram gives no information about the texture of the image. The simplest way to improve the contrast of an image is to expand the brightness scale by spreading the histogram out to the full available range. Rules for transforming the grey value histogram of an existing image (input image) into a new grey value histogram (output image) are most quickly handled by a look-up table (LUT). The histogram of an image can be influenced by gain, offset and gamma of the camera. Gain defines the voltage range, offset defines the reference voltage and gamma the slope of the regression line between the light intensity and the voltage of the camera. A very important descriptor of neighbourhood relations in an image is the co-occurrence matrix. The distance between the pixels (original pixel and its neighbouring pixel) can influence the various parameters calculated from the co-occurrence matrix. The main goals of image enhancement are elimination of surface roughness in an image (smoothing), correction of defects (e.g. noise), extraction of edges, identification of points, strengthening texture elements and improving contrast. In enhancement, two types of operations can be distinguished: pixel-based (point operations) and neighbourhood-based (matrix operations). The most important pixel-based operations are linear stretching of grey values, application of pre-stored LUTs and histogram equalisation. The neighbourhood-based operations work with so-called filters. These are organising elements with an original or initial point in their centre. Filters can be used to accentuate or to suppress specific structures within the image. Filters can work either in the spatial or in the frequency domain. The method used for analysing alterations of grey value intensities in the frequency domain is the Hartley transform. Filter operations in the spatial domain can be based on averaging or ranking the grey values occurring in the organising element. The most important filters, which are usually applied, are the Gaussian filter and the Laplace filter (both averaging filters), and the median filter, the top hat filter and the range operator (all ranking filters). Segmentation of objects is traditionally based on threshold grey values. (AB

In this paper the authors report on a new algorithm used to separate an object from its background using a background image. In the past, simple background subtraction has been used because of its low processing costs and ease of implementation. However, because this method depends solely on brightness patterns in the object and shadows, it has problems such as an inability to deal with poor lighting conditions and an inability to detect regions in which the brightness levels of the object and shadows are similar. In order to resolve these problems, in this paper the authors propose a new filter process called a Radial Reach Filter (RRF). The authors define a new statistic called a Radial Reach Correlation (RRC) used to determine on a pixel-by-pixel basis the similar and dissimilar areas between a background image and a current scene. They then evaluate the local texture at pixel-level resolution while reducing the effects of variations in lighting. In addition, by introducing a mechanism to adjust the defined region adaptively based on local characteristics of the background image, the authors are able to work with various shadows and objects in the scene. The authors perform a theoretical evaluation and experiments using real images to demonstrate the validity of their proposed method. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(10): 63–73, 2004; Published online in Wiley InterScience (). DOI 10.1002sscj.10590

A framework is suggested to segment the foreground in image sequences using background subtraction based on the reconstructed background image for each frame. First, the consecutive frames are taken as inputs for a motion estimation algorithm to calculate the motion vector between frames. Second, an incomplete background image can be achieved by cutting moving parts from origin images. Then, all the incomplete background images from the same sequence can be used to model the background by probabilistic principle component analysis with missing data. The background image can be estimated for each frame. Finally, a simple background subtraction can segment the foreground. The effectiveness of our method is demonstrated in different illumination conditions and compared to the commonly used Gaussian mixture models method.

Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization

Moving Object Detection systems have long been in use to look at security sensitive areas. The manufacture of video investigating systems sensible desires fast, reliable and durable algorithms for moving object detection classification pursuit and activity analysis. Moving object detection is that the essential step for extra analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes like human, vehicle, animal, clutter, etc. it's a necessity to inform apart objects from each other therefore on trace and analyse their actions reliably. In previous system we have got performed background subtraction by victimization clever Edge Detection. In clever Edge Detection technique we tend to tend to square measure taking 2 photos for comparison those square measure background image and foreground image. Previous strategies for object detection square measure methodology along side object detectors image segmentation and Background subtraction. Our methodology aims to section objects supported motion info and it contains a part of background modelling. In the previous strategies we tend to tend to square measure conducting background subtraction only for photos. For this we tend to tend to projected an image component wise background modelling and subtraction technique mistreatment multiple choices. Hence, throughout this color, gradient and Hear-Like choices square measure integrated to handle the variation element. Thus, motion segmentation and background subtraction square measure the foremost connected topics to the current paper.

Dynamic backgrounds and sudden illumination changes are two of the major problems associated with background subtraction techniques. In this paper, we present a novel approach to background subtraction that addresses both of these challenges. In particular, we present an improved codebook background modelling and subtraction technique. We utilise image segmentation on the background image and model the background with a codebook for each pixel along with a pseudo background layer. We perceive background motion as an occlusion of one background layer by a nearby background layer. In other words, sliding of one background layer over a neighbouring layer causes background motion and will hence result in false segmentation. We present our approach of codeword spreading across layer boundaries to handle background motion. Furthermore, we present a two-step update of the background codebook to handle both sudden and gradual illumination changes.

Flotation froth image segmentation using Mask R-CNN

This paper proposes a method to detect moving objects by the background subtraction using the normalized correlation matching. The normalized correlation matching is known as one of general-purposed template matching methods. And the method is robust against change of brightness. Therefore, it is expected that the stable detection of moving objects will be performed by using the normalized correlation matching against changing brightness of background. The proposed method regards the background image as the template image and evaluates correlation rates between the background image and the scene image in order to extract moving objects. We also adopt the integration technique of the correlation rate to realize more stable detection.

When detecting moving objects using visible light image sequence, the shadows of moving objects always appear together with moving objects. Shadows usually change with light condition, which brings difficulty for recognizing objects after segmenting image. This text proposes an algorithm to detect shadows in foreground image according to the Phong illumination model. The algorithm combines the brightness information between foreground image and background image to calculate an array of brightness relative variation ratio, detecting shadows using the statistical feature of shadows in the array. The algorithm uses the statistical histogram to describe statistical feature of brightness relative variation ratio, and the Gaussian model to do curve fitting as a description for the shape of statistical histogram. And finally with the property of the fitted function could we find the threshold value of shadows adaptively. Introduction When using popular algorithms to detect moving objects, among those algorithms such as optical flow [1], frame difference [2], background subtraction, usually a frame of image is segmented as foreground image and background image. In foreground image, moving objects and shadows of moving objects always appear together. Among the popular algorithms used for object recognition, shadows are obstacle for achieving accuracy of the feature extracted from objects of the algorithm. How to remove shadows accurately and completely become an important work before further recognition and tracking of moving objects. At present, there are three categories of algorithm to detect shadows, based on color, texture, and statistical information [3]. This text detects shadows based on the illumination features in foreground image according to Phong illumination model. Experiments prove that the algorithm has a good effect and less calculation. Analysis of Shadow Area Illumination Feature According to Phong illumination model [4], brightness reflected from objects mainly comes from ambient light, diffuse reflection light, specular reflection light. Assume I the brightness of object, e I the light intensity of ambient light, s I the light intensity of direct light from light source, a K , b K , c K are the reflection coefficients, θ is the included angle between incident light and the surface normal, α is the included angle between reflected light and gazing direction, s n is the coefficient of specular reflection, so the brightness of object could be defined as Eq. 1. s n a e b s c s I K I K I cos cosθ K I cos α. = + + (1) In real world scenarios, most objects surface are not smooth, hence assume the contribution of specular reflection light for brightness could be ignored, means s n c s K I cos α to be 0. a K and b K could be considered approximately as the same. Assume ( ) F I x, y to be the brightness value of pixel point ( ) x, y in foreground image, ( ) B I x, y to be the brightness value of pixel point ( ) x, y in background image, define the brightness relative variation ratio to be as Eq. 2. International Conference on Information Sciences, Machinery, Materials and Energy (ICISMME 2015) © 2015. The authors Published by Atlantis Press 2019

Image contour detection is fundamental to many image analysis applications, including image segmentation, object recognition and classification. However, highly accurate image contour detection algorithms are also very computationally intensive, which limits their applicability, even for offline batch processing. In this work, we examine efficient parallel algorithms for performing image contour detection, with particular attention paid to local image analysis as well as the generalized eigensolver used in Normalized Cuts. Combining these algorithms into a contour detector, along with careful implementation on highly parallel, commodity processors from Nvidia, our contour detector provides uncompromised contour accuracy, with an F-metric of 0.70 on the Berkeley Segmentation Dataset. Runtime is reduced from 4 minutes to 1.8 seconds. The efficiency gains we realize enable high-quality image contour detection on much larger images than previously practical, and the algorithms we propose are applicable to several image segmentation approaches. Efficient, scalable, yet highly accurate image contour detection will facilitate increased performance in many computer vision applications.

Image background assessment as a novel technique for insect microhabitat identification

We introduce a descriptor for shape feature extraction and matching using keypoints that are extracted from both the foreground and the background of binary images. First, distance transform (DT) is applied on the image after contour detection. Then, connected components (CCs) of pixels having the same intensity are extracted. Keypoints correspond to centers of mass of CCs. A keypoint filtering mechanism is applied by estimating the spatial stability of keypoints when successive iterations of image blurring and binarization are applied. Finally, features are extracted for each keypoint using a round layout which radius is set depending on the keypoint's location. We evaluate our descriptor using datasets of silhouette images, handwritten math expressions, and logos. Experimental results show that our descriptor is competitive compared with state-of-the-art methods, and that keypoint filtering is effective in reducing the number of keypoints without compromising matching performances.

Two challenge problems in tracking objects in video are object segmentation and matching. Most previous methods can work with some user-specific control. In this paper, we will propose an adaptive algorithm for colour object tracking in video sequences based on background subtraction and image matching by using multiresolution critical point filter (CPFs). Which the background subtraction segment the blob and identify all the objects in the image no matter what they are moving or not. Using results from segmentation phase, object tracking is performed using the multiresolution critical point filter algorithm. This is based on research from prof. Shinagawa's image interpolation. For now, our approach is suitable for tracking of the object through a sequence of 64*64 images.

Smart CCTV (Closed-Circuit Television) technology has increasingly been developed in the last few years to judge the situation and notify the administrator or take immediate action for security and surveillance reasons. Currently the methods to detect object motion typically include the Frame Difference Method (FDM) which can detect moving objects and the Background Subtraction Method (BSM) which is able to detect motionless objects. Those results can be obtained only if there were some background images ready in advance. The Adaptive Background Subtraction Method (ABSM) also could not recognize an object very well if there are rapid scene changes or an object does not move relatively for a long time. To resolve such a problem, in this research, a filmed image has been divided into the regular sized blocks and then, only the necessary parts of the previous frame image are updated in real-time and a background image was generated so that it is insensible to surrounding environment changes such as object motion, noise or light variations. We proposed a novel moving object detection method which showed high performance with regard to the MSE (Mean Squared Error) and the accuracy of detecting the moving object contours compared to other existing methods. We also evaluated quantitatively the detectability for a moving object region by quickly creating a background image even if it is difficult to shoot a background image or we do not have the baseline image prepared in advance. The proposed method could be used for cases that any background image does not exist or hard to be generated. It is also good for observation of many places at the same time with only a single CCTV system since it is especially robust to abrupt scene changes.