Group Of Pixels Research Articles

Time-of-flight measurement of fast neutrons with Timepix detectors

Timepix pixel detectors have been used to study theresponse of silicon hybrid pixel detectors to fast neutrons from a pulsedneutron beam at WNR FP30R, a 14 m long flight path, in the Los AlamosNeutron Science Center. Neutrons with kinetic energies up to 600 MeV wereavailable. In order to enhance the conversion of neutrons to energeticcharged particles, several converter foils and filters were attached to the300 μm thick silicon sensor, i.e. polyethylene, polyethylene withaluminum, 6LiF, 6LiF with aluminum, aluminum. The Time-of-Arrivalmode of the Timepix detectors has permitted the application of theTime-of-Flight (TOF) technique for the assignment of the detectedinteractions in the form of clusters (groups of adjacent pixels) in thepixel matrix, to the kinetic energies of the incident neutrons.It was found that, for lower neutron energies ( ∼ MeV range) the clusterrates below the polyethylene and the polyethylene and aluminum region,produced by recoil protons, are a good measure for the mean kinetic energiesof neutrons. For energies above 50 MeV nuclear reactions in the silicondominate the detector response. In this energy range the shape of theclusters indicates the neutron kinetic energy.

A modified adaptive differential evolution algorithm for color image segmentation

Image segmentation is an important low-level vision task. It is a perceptual grouping of pixels based on some similarity criteria. In this paper, a new differential evolution (DE) algorithm, modified adaptive differential evolution, is proposed for color image segmentation. The DE/current-to-pbest mutation strategy with optional external archive and opposition-based learning are used to diversify the search space and expedite the convergence process. Control parameters are automatically updated to appropriate values in order to avoid user intervention of parameters setting. To find an optimal number of clusters (the number of regions or segments), the average ratio of fuzzy overlap and fuzzy separation is used as a cluster validity index. The results demonstrate that the proposed technique outperforms state-of-the-art methods.

Pixel chip architecture optimization based on a simplified statistical and analytical model

The technical challenges related to increased collision rates of the LHC will significantly affect detector electronics design. Efficient hit processing is achieved in pixel detectors by grouping pixels in regions, which share buffering logic. We present an approach to determine an optimized sharing strategy between pixels, depending on the shape of clustered hits in the detector. Simple statistical models of such shapes have been developed with respect to the position in the detector. The buffering performance of different pixel region configurations has been compared, showing significant improvement from architectures that do not feature pixel grouping.

One‐third probability embedding: a new ±1 histogram compensating image least significant bit steganography scheme

A new method is introduced for the least significant bit (LSB) image steganography in spatial domain providing the capacity of one bit per pixel. Compared to the recently proposed image steganography techniques, the new method called one-third LSB embedding reduces the probability of change per pixel to one-third without sacrificing the embedding capacity. This improvement results in a better imperceptibility and also higher robustness against well-known LSB detectors. Bits of the message are carried using a function of three adjacent cover pixels. It is shown that no significant improvement is achieved by increasing the length of the pixel sequence employed. A closed-form expression for the probability of change per pixel in terms of the number of pixels used in the pixel groups has been derived. Another advantage of the proposed algorithm is to compensate, as much as possible, for any changes in the image histogram. It has been demonstrated that one-third probability embedding outperforms histogram compensating version of the LSB matching in terms of keeping the image histogram unchanged.

Land transitions from multivariate time series: using seasonal trend analysis and segmentation to detect land-cover changes

The detection of land-cover change over large areas using short time series is a challenging and important task in global change studies. This paper introduces a novel method, called the Segment-based Detection of Trends and Change (SDTC), to determine areas that are undergoing land-cover changes. The method is illustrated in the State of Alaska over the 2001–2009 time period using three time series derived from Moderate Resolution Spectroradiometer (MODIS) imagery: the normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST). SDTC extends seasonal trend analysis (STA) by using segmentation in the detection process to combine multiple variables and to incorporate the spatial context. Segments labelled as ‘change’ correspond to groups of adjacent pixels with a majority of pixels undergoing significant temporal trends as defined by the Mann–Kendall (MK) procedure and STA. Segments correspond to landscape units in a less arbitrary manner than pixels because they represent groupings of adjacent areas undergoing similar temporal behaviour. Findings indicate that the use of MK in conjunction with segmentation exploits more fully the richness of the spatial context in the process of detection. Results suggest that SDTC is a useful method for detecting and characterizing land-cover change. The technique is conservative and eliminates the ‘salt and pepper’ effect by filtering noise and identifying areas of change. Using SDTC, we found that most areas of change in Alaska undergo between one and three significant changes and that increasing LST seasonality constitutes the most widespread type of change.

Imaging Fluorescence Correlation Spectroscopy Measures Dynamics and Structure in Live Samples

Fluorescence Correlation Spectroscopy (FCS) has been developed more than 40 years ago and has evolved into a major tool in the biophysical sciences for the quantitative measurement of biomolecular dynamics and interactions. In the past FCS was mainly used as a confocal, single spot measurement technique. Imaging FCS, i.e. the recording of FCS measurements at all pixels in an image, became feasible only recently with the advent of fast, sensitive array detectors and the development of new illumination modes in microscopy, in particular light sheet microscopy. Despite the fact that the time resolution of imaging FCS is lower than for single spot measurements, imaging FCS has several advantages. The most important being i) that it contains much more information since spatial and temporal correlations can be evaluated between any pixels or group of pixels in the image, and ii) the illumination modes (TIRFM or SPIM) expose the sample to much lower light doses and allow more measurements per sample.We demonstrate the capabilities of imaging FCS using total internal reflection (TIR) and single plane illumination microscopy (SPIM) as illumination modes. We investigate the membrane active human Islet Amyloid Polypeptide (hIAPP) on cell membranes using imaging TIR-FCS, FCS diffusion laws, and time-lapse FCS videos, to elucidate the membrane action of monomeric hIAPP. With the aid of the FCS diffusion laws, Imaging FCS can access information about sample structure even below the diffraction limit. And light sheet illumination allows single cell observations over up to one hour (total effective illumination time ∼10 minutes) without visible cell damage. Second, we use SPIM-FCCS to demonstrate the measurement of interactions of biomolecules in vitro and in live cells. These experiments show that Imaging FCS provides quantitative images of biological samples under physiological conditions.

Rate Control For People detection in video

People Detection in video is generally used in high level multimedia applications like intelligent surveillance systems, augmented reality ,etc. People detection is based on background subtraction. Generally videos are available in compressed form due to which noise will be added in it. Many algorithms are used to detect people and control their rate. There are three key steps in video analysis detection of interesting moving objects, tracking of such objects from frame to frame, and analysis of object tracks to recognize their behavior. The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. In this paper we put our attention into background subtraction and Gaussian grouping of pixels for detection of people in low quality video i.e. improve accuracy. Video surveillance system is based on the ability to detect moving objects or people in video. Detection of moving objects is first step in surveillance systems. Each image is then segmented i.e. each image is split into number of frames. Video is converted into sequence of frames by image analysis techniques. Background subtraction is widely used technique for detecting moving objects in videos from static camera. The background subtraction method is to use the method of difference between current image and background image. This method is effective to improve the effect of moving object detection. Background subtraction is nothing but foreground detection. After detecting foreground image, grouping of pixels as per Gaussian distribution will be done. Kalman filter is used to track the people in video. Background subtraction technique find the foreground object from video and then classify it into categories like human, animal, vehicle etc., based on shape , color , motion or other features. Most of the multimedia videos are available in compressed format. Usually higher the compression rate, lowers the correct hits and quality of video due to noise added in it. A modern object detection algorithm can be divided into five parts: pre-processing and normalization, local rectification and compensation of small shape variations, computation of descriptor set, machine learning classification, and post-processing to fuse multiple detections. In this paper, we focus on detection schemes based on background subtraction because of their widespread use and the possibilities they offer in implementing real-time object detection systems. background subtraction is nothing but foreground detection. Unfortunately, images and video are usually available in compressed format which makes object detection more difficult because of the additional distortion noise. In this paper we propose a saliency map algorithm and compare it with background method to improve accuracy. Object detection has been studied for about four decades producing a wide range of object detection techniques. This research effort has been fostered by the many application scenarios where object detection can be employed (e.g., active video surveillance, assisted or autonomous drive, database search, data classification) and by the need of increasing the robustness of existing algorithms to different lighting conditions, poses, scales, locations, and geometries . Object detection has been studied for about four decades producing a wide range of object detection techniques. This research effort has been fostered by the many application scenarios where object detection can be employed (e.g., active video surveillance, assisted or autonomous drive, database search, data classification) and by the need of increasing the robustness of existing algorithms to different lighting conditions, poses, scales, locations, and geometries (deformable objects). As a matter of fact, a modern object detection algorithm can be divided into five parts: pre-processing and normalization, local rectification and compensation of small shape variations, computation of descriptor set, machine learning classification, and post-processing to fuse multiple detections.

Efficient ROI Selection for Multi-Emitter Fitting Approaches in Single-Molecule Super-Resolution Microscopy

Single molecule super-resolution techniques such as PALM and STORM circumvent the diffraction limit by using fluorophore switching to isolate single emitters in space and time. The requirement that individual fluorophore signals are well separated, however, limits the speed of these methods. Recent advances have relaxed this requirement through the use of model functions which allow multiple overlapping emitters within one image region. Building on the methods used for sparse localisation, multi-emitter fitting methods typically extract fixed size rectangular regions of interest (ROIs) around candidate molecules and perform fitting within these regions. These ROIs will often intersect and it is common for molecules to be present in multiple ROIs and/or to only be partially enclosed within a ROI. This requires a number of empirical solutions to collate molecules and ensure that they are not over-counted, and to ensure convergence when attempting to fit partial molecules. Fitting molecules repeatedly within multiple ROIs also incurs significant computational overhead.Rather than using fixed size rectangular ROIs at candidate molecule positions, an alternative approach can be constructed based on the selection of contiguous regions of pixels which have a non-zero information content. In short, the algorithm determines which pixels in the image contain useful information by performing a statistical test to determine if pixels (or groups of neighbouring pixels) are significantly higher than the background, dilates this mask to introduce a ‘safety factor’, and uses a labelling algorithm to identify contiguous groups of pixels within this mask. These contiguous, non-rectangular, regions become the new ROIs. The approach has the dual advantages of fitting each emitter only once, and incorporating the full information about every emitter within each ROI so as to avoid any convergence problems.

Gray Scale Image Denoising using PCA-SPT

This paper proposed a spatially adaptive image denoising scheme, which is comprised of two stages. In the first stage, image is denoised by using Principal Component Analysis (PCA) with Local Pixel Grouping (LPG). LPG-PCA can effectively preserve the image fine structures while denoising. In the second stage, we use Steerable Pyramid Transform (SPT) to decompose images into frequency sub-bands. The noise level is updated adaptively before second stage denoising. Steerable Pyramid Transform in the second stage further improves the denoising performance. This paper also reviews on the present denoising processes and performs their comparative study. Experimental results demonstrate that the proposed PCA-SPT algorithm achieve competitive outcomes. PCA-SPT works well in image fine structure preservation, compared with state-of-the-art denoising algorithms. Keywords: AWGN; Wavelet; SPT; LPG-PCA; BM3D; Edge preservation.

Noise Removal in Image Using LPG-PCA (Local Pixel GroupingPrinciple Component Analysis) Algorithm

In this paper an effective algorithm for noise removal in an image is obtained by using PCA (principal component analysis) with LPG (Local Pixel Grouping). This technique ensures the preservation of image local structure. Here the pixels and its neighbors are treated as vector variables whose training samples are selected from local windows using block matching based LPG. This ensures only the similar samples are selected for the PCA transformation so that the desired local characteristics are only preserved with considerable noise reduction. The LPG -PCA algorithm is performed twice to enhance the quality of an image. The first iteration would remove the noise considerably and the second iteration would preserve the image features like edges etc. The LPG-PCA algorithm will adaptively adjust the noise level of an image unlike WT (Wavelet Transformation). Several experimental results show the effectiveness of the proposed algorithm.

Fuzzy Free Path Detection from Disparity Maps by Using Least-Squares Fitting to a Plane

A method to detect obstacle-free paths in real-time which works as part of a cognitive navigation aid system for visually impaired people is proposed. It is based on the analysis of disparity maps obtained from a stereo vision system which is carried by the blind user. The presented detection method consists of a fuzzy logic system that assigns a certainty to be part of a free path to each group of pixels, depending on the parameters of a planar-model fitting. We also present experimental results on different real outdoor scenarios showing that our method is the most reliable in the sense that it minimizes the false positives rate.

Pixel-QUAD based Reversible Data Hiding for High Embedding Capacity

Embedding capacity and Perceptibility are prime issues in relation to data hiding techniques. There had been techniques for hiding data in reversible fashion. A new term ‘Pixel-QUAD’ is coined in this paper, instead of commonly used ‘Pixel-Pair’ for embedding the watermark data. This paper presents a high embedding capacity technique for reversible data hiding based on a PixelQuad approach. The proposed methodology for the technique is quite general and is applicable to any reversible watermarking scheme; Demonstration is done for a class of reversible watermarking schemes which operate on a disjoint group of pixels. An attempt is also made to propose algorithm to estimate the embedding capacity iteratively for a multi-pass scheme.

Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery

To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-based method that uses the additional characteristics of features in the process of classification. In this paper, three types of classification were used to classify SPOT 5 satellite image in mapping land cover; Support vector machine (SVM) pixel-based, SVM object-based and Decision Tree (DT) pixel-based classification. Normalised Difference Vegetation Index and the brightness value of two infrared bands (NIR and SWIR) were used in manually developed DT classification. The classification of the SVM (pixel based) was generated using the selected groups of pixels that represent the selected features. In addition, the SVM (object based) was implemented by using radial-based function kernel. The classified features were oil palm, rubber, urban area, soil, water and other vegetation. The study found that the overall classification of the DT was the lowest at 69.87% while those of SVM (pixel based) and SVM (object based) were 76.67 and 81.25%, respectively.

Range image segmentation for tree detection in forest scans

Abstract. To make a tree-wise analysis inside a forest stand, the trees have to be identified. An interactive segmentation is often labourintensive and time-consuming. Therefore, an automatic detection process will aspired using a range image. This paper presents a method for the segmentation of range images extracted from terrestrial laser scanner point clouds of forest stands. After range image generation the segmentation is carried out with a connectivity analysis using the differences of the range values as homogeneity criterion. Subsequently, the tree detection is performed interactively by analysing one horizontal image line. When passing objects with a specific width, the object indicates a potential tree. By using the edge points of a segmented pixel group the tree position and diameter is calculated. Results from one test site are presented to show the performance of the method.

시각 셀 OOK 변조 : MIMO CamCom 연구 사례

Multiplexing information over parallel data channels based on RF MIMO concept is possible to achieve considerable data rates over large transmission ranges with just a single transmitting element. Visual multiplexing MIMO techniques will send independent streams of bits using the multiple elements of the light transmitter array and recording over a group of camera pixels can further enhance the data rates. The proposed system is a combination of the reliance on computer vision algorithms for tracking and OOK cell frame modulation. LED array are controlled to transmit message in the form of digital information using ON-OFF signaling with ON-OFF pulses (ON = bit 1, OFF = bit 0). A camera captures image frames of the array which are then individually processed and sequentially decoded to retrieve data. To demodulated data transmission, a motion tracking algorithm is implemented in OpenCV (Open source Computer Vision library) to classify the transmission pattern. One of the most advantages of proposed architecture is Computer Vision (CV) based image analysis techniques which can be used to spatially separate signals and remove interferences from ambient light. It will be the future challenges and opportunities for mobile communication networking research.

Solving Speed and Memory Issues in Multiple-Point Statistics Simulation Program SNESIM

In the last 10 years, Multiple-Point Statistics (MPS) modeling has emerged in Geostatistics as a valuable alternative to traditional variogram-based and object-based modeling. In contrast to variogram-based simulation, which is limited to two-point correlation reproduction, MPS simulation extracts and reproduces multiple-point statistics moments from training images; this allows modeling geologically realistic features, such as channels that control reservoir connectivity and flow behavior. In addition, MPS simulation works on individual pixels or small groups of pixels (patterns), thus does not suffer from the same data conditioning limitations as object-based simulation. The Single Normal Equation Simulation program SNESIM was the first implementation of MPS simulation to propose, through the introduction of search trees, an efficient solution to the extraction and storage of multiple-point statistics moments from training images. SNESIM is able to simulate three-dimensional models; however, memory and speed issues can occur when applying it to multimillion cell grids. Several other implementations of MPS simulation were proposed after SNESIM, but most of them manage to reduce memory demand or simulation time only at the expense of data conditioning exactitude and/or training pattern reproduction quality. In this paper, the original SNESIM program is revisited, and solutions are presented to eliminate both memory demand and simulation time limitations. First, we demonstrate that the time needed to simulate a grid node is a direct function of the number of uninformed locations in the conditioning data search neighborhood. Thus, two improvements are proposed to maximize the ratio of informed to uniformed locations in search neighborhoods: a new multiple-grid approach introducing additional intermediary subgrids; and a new search neighborhood designing process to preferentially include previously simulated node locations. Finally, because SNESIM memory demand and simulation time increase with the size of the data template used to extract multiple-point statistics moments from the training image and build the search tree, a simple method is described to minimize data template sizes while preserving training pattern reproduction quality.

Texture Inpainting Using Covariance in Wavelet Domain

AbstractIn this article, the covariance of wavelet transform coefficients is used to obtain a texture inpainted image. The covariance is obtained by the maximum likelihood estimate. The integration of wavelet decomposition and maximum likelihood estimate of a group of pixels (texture) captures the best-fitting texture used to fill in the inpainting area. The image is decomposed into four wavelet coefficient images by using wavelet transform. These wavelet coefficient images are divided into small square patches. The damaged region in each coefficient image is filled by searching a similar square patch around the damaged region in that particular wavelet coefficient image by using covariance.

Saliency for Spectral Image Analysis

We introduce a new feature extraction model for purposes of image comparison, visualization and interpretation. We define the notion of spectral saliency, as the extent to which a certain group of pixels stands out in an image and in terms of reflectance, rather than in terms of colorimetric attributes as it is the case in traditional saliency studies. The model takes as an input a multi- or hyper-spectral image with any dimensionality, any range of wavelengths, and it uses a series of dedicated feature extractions to output a single saliency map. We also present a local analysis of the image spectrum allowing to produce such maps in color, thus depicting not only which objects are salients, but also in which range of wavelengths. A variety of applications can be derived from the resulting maps, particularly under the scope of visualization, such as the saliency-driven evaluation of dimensionality reduction techniques. Results show that spectral saliency provides valuable information, which do not correlate neither with visual saliency, second-order statistics nor with naturalness, but serve however well for visualization-related applications.

Speckle pattern quality assessment for digital image correlation

To perform digital image correlation (DIC), each image is divided into groups of pixels known as subsets or interrogation cells. Larger interrogation cells allow greater strain precision but reduce the spatial resolution of the data field. As such the spatial resolution and measurement precision of DIC are limited by the resolution of the image. In the paper the relationship between the size and density of speckles within a pattern is presented, identifying that the physical properties of a pattern have a large influence on the measurement precision which can be obtained. These physical properties are often overlooked by pattern assessment criteria which focus on the global image information content. To address this, a robust morphological methodology using edge detection is devised to evaluate the physical properties of different speckle patterns with image resolutions from 23 to 705pixels/mm. Trends predicted from the pattern property analysis are assessed against simulated deformations identifying how small changes to the application method can result in large changes in measurement precision. An example of the methodology is included to demonstrate that the pattern properties derived from the analysis can be used to indicate pattern quality and hence minimise DIC measurement errors. Experiments are described that were conducted to validate the findings of morphological assessment and the error analysis.

Incorporating local image structure in normalized cut based graph partitioning for grouping of pixels

Graph partitioning for grouping of image pixels has been explored a lot, with normalized cut based graph partitioning being one of the popular ones. In order to have a credible allegiance to the perceptual grouping taking place in early human vision, we propose and study in this paper the incorporation of local image structure/context in normalized cut based graph partitioning for grouping of image pixels. Similarity and proximity, which have been studied earlier for grouping of image pixels, are only two among many perceptual cues that act during grouping in early human vision. In addition to the said two cues, we study three other such cues, namely, common fate, common region and continuity, and find indications of local image structure utilization during grouping of image pixels. Appropriate incorporation of local image structure/context is achieved by representing it using neighborhood in the form of histogram and fuzzy set. We demonstrate both qualitatively and quantitatively through experimental results that the incorporation of local image structure improves performance of grouping of image pixels.