A New Fast Similarity Metric Algorithm Based on Contour

2012 ◽  
Vol 562-564 ◽  
pp. 2034-2037
Author(s):  
Jing Jing Wang ◽  
Hong Jun Wang ◽  
Yong Yin
Keyword(s):  
Image Features ◽  
Similarity Metrics ◽  
Higher Dimension ◽  
Laplacian Pyramid ◽  
Illumination Change ◽  
Snake Model ◽  
Similarity Metric ◽  
Image Contour ◽  
Scan Line ◽  
Registration Result

The similarity metric is a key on image registration. This paper divides similarity metric algorithms into two classes: similarity metrics based on pixels (or voxels) and similarity metrics based on image features. For those images that acquired contours easily, this paper proposes a new fast similarity metric arithmetic based on scan line. This algorithm is insensitive to illumination change and is robust without considering gray level of pixels (or voxels). In addition, this arithmetic does not consider all pixels (or voxels) in image, but consider pixels (or voxels) in the range of contour. So it is very simple and fast. It is not only suitable for 2D images but also suitable for higher dimension images. In experiment we use Laplacian pyramid to decompose image and use snake model to detect image contour. Lastly we give a novel registration result.

scholarly journals Sketch-based 3D modeling by aligning outlines of an image

2016 ◽  
Vol 3 (3) ◽  
pp. 286-294 ◽  
Author(s):  
Chunxiao Li ◽  
Hyowon Lee ◽  
Dongliang Zhang ◽  
Hao Jiang
Keyword(s):  
3D Modeling ◽  
Image Features ◽  
Efficient Technique ◽  
Irregular Shapes ◽  
Contour Lines ◽  
Drawing Task ◽  
Starting Point ◽  
Outline Drawing ◽  
Image Contour ◽  
Careful Manipulation

Abstract In this paper we present an efficient technique for sketch-based 3D modeling using automatically extracted image features. Creating a 3D model often requires a drawing of irregular shapes composed of curved lines as a starting point but it is difficult to hand-draw such lines without introducing awkward bumps and edges along the lines. We propose an automatic alignment of a user's hand-drawn sketch lines to the contour lines of an image, facilitating a considerable level of ease with which the user can carelessly continue sketching while the system intelligently snaps the sketch lines to a background image contour, no longer requiring the strenuous effort and stress of trying to make a perfect line during the modeling task. This interactive technique seamlessly combines the efficiency and perception of the human user with the accuracy of computational power, applied to the domain of 3D modeling where the utmost precision of on-screen drawing has been one of the hurdles of the task hitherto considered a job requiring a highly skilled and careful manipulation by the user. We provide several examples to demonstrate the accuracy and efficiency of the method with which complex shapes were achieved easily and quickly in the interactive outline drawing task. Highlights We present an efficient technique for sketch-based 3D modeling using automatically extracted image features. An automatic and real-time method is proposed to align a user's hand-drawn sketch line to the contour lines of an image, facilitating a considerable level of ease for 3D modeling. We use a geometric method to align a sketch line to the outlines of an image using the features of the sketch line and contour lines of an image, and some operations are proposed to refine the result of alignment. In the sketch-based 3D modeling method, the sketch line is represented by a editable spline, therefore, the aligned sketch line can be further adjusted interactively.

scholarly journals The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ning Feng ◽  
Ping Gao
Keyword(s):  
Motion Tracking ◽  
Moving Objects ◽  
Rapid Development ◽  
Human Motion ◽  
Motion Recognition ◽  
Snake Model ◽  
Running Time ◽  
Sports Science ◽  
Human Motion Recognition ◽  
Image Contour

With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.

A SIMILARITY METRIC IN IMAGE SEARCHING

2007 ◽  
Vol 07 (02) ◽  
pp. 211-225
Author(s):  
XUELONG LI ◽  
JING LI ◽  
DACHENG TAO ◽  
YUAN YUAN
Keyword(s):  
Signal Processing ◽  
System Performance ◽  
Ground Truth ◽  
Point Of View ◽  
Visual Features ◽  
Similarity Metrics ◽  
Graphical Interface ◽  
Similarity Metric ◽  
Processing Point ◽  
Small Image

Similarity metric is a key component in query-by-example image searching with visual features. After extraction of image visual features, the scheme of computing their similarities can affect the system performance dramatically — the image searching results are normally displayed in decreasing order of similarity (alternatively, increasing order of distance) on the graphical interface for end users. Unfortunately, conventional similarity metrics, in image searching with visual features, usually encounter several difficulties, namely, lighting, background, and viewpoint problems. From the signal processing point of view, this paper introduces a novel similarity metric and therefore reduces the above three problems to some extent. The effectiveness of this newly developed similarity metric is demonstrated by a set of experiments upon a small image ground truth.

scholarly journals Re-Ranking Images Retrieval: Combined Multiple Features Method

2019 ◽  
Vol 8 (3) ◽  
pp. 1099-1105
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Critical Role ◽  
Image Features ◽  
Experimental Result ◽  
Content Based Image Retrieval ◽  
Similarity Metrics ◽  
Multiple Features ◽  
Combined Features ◽  
Relevance Measures

Content-Based Image Retrieval (CBIR) grown rapidly in multimedia field, image retrieval, pattern recognition, etc. CBIR provides an effective way of image search and retrieval from the pool image databases. Learning effective relevance measures plays a critical role in improving the performance of image retrieval systems. In this paper present a Combined multiple features method which is two key parameters (i) Feature extraction, (ii) Similarity metrics for content-based image retrieval method. Feature extraction and similarity metrics important role in Content-Based Image Retrieval. We define hybrid feature extraction and similarity method for finding the most similar images retrieved. Combined features extraction using the various image features. These papers explain some important distance metrics such as Euclidean distance and City block distance. The experiments are performed using the various kinds of databases such as WANG Database, Corel Dataset. The experimental result shows that the proposed method is proved more effective than existing methods.

scholarly journals Recommender system for surplus stock clearance

2019 ◽  
Vol 9 (5) ◽  
pp. 3813
Author(s):  
Vipul Agarwal ◽  
Vijayalakshmi A
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cosine Similarity ◽  
Similarity Metrics ◽  
Manual Work ◽  
Similarity Metric ◽  
Filtering Technique ◽  
Retail Shop ◽  
Item Item

Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend the stock which requires clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit

scholarly journals Visual Cortex Inspired Intelligent Contour Detection

2006 ◽  
Vol 10 (5) ◽  
pp. 761-768 ◽  
Author(s):  
Barna Reskó ◽  
◽  
Zoltán Petres ◽  
András Róka ◽  
Péter Baranyi
Keyword(s):  
Visual Cortex ◽  
Complex Network ◽  
Input Image ◽  
Image Features ◽  
Contour Detection ◽  
Constant Time ◽  
Feature Transformation ◽  
Image Contour

The present paper proposes a model for intelligent image contour detection. The model is strongly based on the architecture and functionality of the mammalian visual cortex. A pixel-to-feature transformation is performed on the input image, the result of which is a set of abstract image features, instead of another set of pixels. The contouring task is performed by a vast and complex network of simple units of computation that work together in a parallel way. The use of a large number of such simple units allows a clear structure that can be implemented on a special hardware to allow constant time computation.

scholarly journals Effective Image Segmentation using Composite Energy Metric in Levelset Based Curve Evolution

2019 ◽  
Vol 38 (1) ◽  
pp. 175-184
Author(s):  
Muhammad Moazzam Jawaid ◽  
Bushra Naz Soomro ◽  
Sajjad Ali Memon ◽  
Noor-ur-Zaman Leghari
Keyword(s):  
Image Segmentation ◽  
Active Contour Model ◽  
Image Features ◽  
Individual Characteristics ◽  
Curve Evolution ◽  
Local Optima ◽  
Snake Model ◽  
Low Contrast ◽  
Proposed Model ◽  
Composite Energy

Accurate segmentation of anatomical organs in medical images is a complex task due to wide inter-patient variability and several acquisition dependent artefacts. Moreover, image noise, low contrast and intensity inhomogeneity in medical data further amplifies the challenge. In this work, we propose an effective yet simple algorithm based on composite energy metric for precise detection of object boundaries. A number of methods have been proposed in literature for image segmentation; however, these methods employ individual characteristics of image including gradient, regional intensity or texture map. Segmentation based on individual featres often fail for complex images, especially for medical imagery. Accordingly, we propose that the segmentation quality can be improved by integrating local and global image features in the curve evolution. This work employs the classic snake model aka active contour model; however, the curve evolution force has been updated. In contast to the conventional image-based regional intensity statistics, the proposed snake model evolves using composite image energy. Hence, the proposed method offers a greater resistance to the local optima problem as well as initialization perturbations. Experimental results for both synthetic and 2D (Two Dimensional) real clinal images are presented in this work to validate the performance of the proposed method. The performance of the proposed model is evaluated with respect to expert-based manual ground truth. Accordingly, the proposed model achieves higher accuracy in comparison to the state-of-the-art region based segmentation methods of Lankton and Yin as reported in results section.

7-beam lattice images of (110) oriented Ge

1978 ◽  
Vol 36 (1) ◽  
pp. 290-291
Author(s):  
J.R. Parsons ◽  
C.W. Hoelke
Keyword(s):  
Image Features ◽  
Objective Lens ◽  
Lattice Plane ◽  
Lattice Image ◽  
Crystalline Defects ◽  
Transmission Electron ◽  
Lattice Images ◽  
Electron Microscopes ◽  
Structural Aspects ◽  
Beam Lattice

The direct imaging of a crystal lattice has intrigued electron microscopists for many years. What is of interest, of course, is the way in which defects perturb their atomic regularity. There are problems, however, when one wishes to relate aperiodic image features to structural aspects of crystalline defects. If the defect is inclined to the foil plane and if, as is the case with present 100 kV transmission electron microscopes, the objective lens is not perfect, then terminating fringes and fringe bending seen in the image cannot be related in a simple way to lattice plane geometry in the specimen (1).The purpose of the present work was to devise an experimental test which could be used to confirm, or not, the existence of a one-to-one correspondence between lattice image and specimen structure over the desired range of specimen spacings. Through a study of computed images the following test emerged.

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

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