Summary It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well-characterized markers. However, imageanalysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under Image J, named JACoP, that integrates current global statistic methods and a novel object-based approach.

A guided tour into subcellular colocalization analysis in light microscopy

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

Image processing and computer vision are currently hot topics with undergraduates and professionals alike. "Feature Extraction and Image Processing" provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques and fundamentals in a clear and concise manner. Readers can develop working techniques, with usable code provided throughout and working Matlab and Mathcad files on the web. Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals.The new edition includes: a new coverage of curvature in low-level feature extraction (SIFT and saliency) and features (phase congruency); geometric active contours; morphology; and camera models and an updated coverage of image smoothing (anistropic diffusion); skeletonization; edge detection; curvature; and shape descriptions (moments). It is an essential reading for engineers and students working in this cutting edge field. It is an ideal module text and background reference for courses in image processing and computer vision. It features a companion website that includes worksheets, links to free software, Matlab files, solutions and new demonstrations.

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Feature Extraction and Image Processing

Edge detection is a fundamental problem in computer vision. Recently, convolutional neural networks (CNNs) have pushed forward this field significantly. Existing methods which adopt specific layers of deep CNNs may fail to capture complex data structures caused by variations of scales and aspect ratios. In this paper, we propose an accurate edge detector using richer convolutional features (RCF). RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation. RCF fully exploits multiscale and multilevel information of objects to perform the image-to-image prediction holistically. Using VGG16 network, we achieve state-of-the-art performance on several available datasets. When evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of 0.811 while retaining a fast speed (8 FPS). Besides, our fast version of RCF achieves ODS F-measure of 0.806 with 30 FPS. We also demonstrate the versatility of the proposed method by applying RCF edges for classical image segmentation.

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Richer Convolutional Features for Edge Detection

Recently, digital-image-correlation techniques have been used to accurately determine two-dimensional in-plane displacements and strains. An extension of the two-dimensional method to the acquisition of accurate, three-dimensional surfacedisplacement data from a stereo pair of CCD cameras is presented in this paper. A pin-hole camera model is used to express the transformation relating three-dimensional world coordinates to two-dimensional computer-image coordinates by the use of camera extrinsic and intrinsic parameters. Accurate camera model parameters are obtained for each camera independently by (a) using several points which have three-dimensional world coordinates that are accurate within 0.001 mm and (b) using two-dimensional image-correlation methods that are accurate to within 0.05 pixels to obtain the computer-image coordinates of various object positions. A nonlinear, least-squares method is used to select the optimal camera parameters such that the deviations between the measured and estimated image positions are minimized. Using multiple orientations of the cameras, the accuracy of the methodology is tested by performing translation tests. Using theoretical error estimates, error analyses are presented. To verify the methodology for actual tests both the displacement field for a cantilever beam and also the surface, three-dimensional displacement and strain fields for a 304L stainless-steel compact-tension specimen were experimentally obtained using stereo vision. Results indicate that the three-dimensional measurement methodology, when combined with two-dimensional digital correlation for subpixel accuracy, is a viable tool for the accurate measurement of surface displacements and strains.

Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision

: The report developes a parametric model for a computer-controlled moveable camera on a pan-tilt head. The model expresses the transform relating object space to image space as a function of the control variables of the camera. We constructed a calibration system for measuring the model parameters which has a demonstrated accuracy more than adequate for our present needs. We have also identified the major source of error in model measurement to be undesired image motion and have developed means of measuring and compensating for some of it and eliminating other parts of it. The system can measure systematic image distortions if they become the major accuracy limitation. It has been shown how to generalize the model to handle small systematic errors due to aspects of pan-tilt head geometry not presently accounted for. The report demonstrates the model's application in stereo vision and have shown how it can be applied as a predictive device in locating objects of interest and centering them in an image. (Author)

Irwin Edward Sobel

Papers

Camera Models and Machine Perception