scholarly journals Efficiency of object identification for binary images

2019 ◽  
Vol 43 (2) ◽  
pp. 277-281 ◽  
Author(s):  
R. Magdeev ◽  
Al. Tashlinskii
Keyword(s):  
Comparative Analysis ◽  
Additive Noise ◽  
Orientation Angle ◽  
Object Identification ◽  
Binary Image ◽  
Stochastic Gradient ◽  
Contour Analysis ◽  
Binary Images ◽  
Similarity Model ◽  
Angle Shift

In this paper, a comparative analysis of the correlation-extreme method, the method of contour analysis and the method of stochastic gradient identification in the objects identification for a binary image is carried out. The results are obtained for a situation where possible deformations of an identified object with respect to a pattern can be reduced to a similarity model, that is, the pattern and the object may differ in scale, orientation angle, shift along the base axes, and additive noise. The identification of an object is understood as the recognition of its image with an estimate of the strain parameters relative to the template.

scholarly journals A Novel Traffic Tracking System Based on division of Video into Frames and Processing

2013 ◽  
pp. 255-259
Author(s):  
Prabhakar Telagarapu ◽  
B. Jagdishwar Rao ◽  
J. Venkata Suman ◽  
K. Chiranjeevi
Keyword(s):  
Tracking System ◽  
Relative Size ◽  
Morphological Processing ◽  
Binary Image ◽  
Video Frame ◽  
Gray Scale ◽  
Binary Images ◽  
Morphological Image Processing ◽  
Car Detection ◽  
Analysis Technique

The objective of this paper is to visualize and analyze video.Videos are sequence of image frames. In this work, algorithm will be developed to analyze a frame and the same will be applied to all frames in a video. It is expected see unwanted objects in video frame, which can be removed by converting colour frames into a gray scale and implement thresh holding algorithm on an image. Threshold can be set depending on the object to be detected. Gray scale image will be converted to binary during thresh holding process. To reduce noise, to improve the robustness of the system, and to reduce the error rate in detection and tracking process, morphological image processing method for binary images is used. Morphological processing will be applied on binary image to remove small unwanted objects that are presented in a frame. A developed blob analysis technique for extracted binary image facilitates pedestrian and car detection. Processing blob’s information of relative size and location leads to distinguishing between pedestrian and car. The threshold, morphological and blobs process is applied to all frames in a video and finally original video with tagged cars will be displayed.

A Hybrid Lossless-Lossy Binary Image Compression Scheme

2013 ◽  
Vol 3 (4) ◽  
pp. 37-50
Author(s):  
Saif alZahir ◽  
Syed M. Naqvi
Keyword(s):  
Image Compression ◽  
Mathematical Models ◽  
Regression Models ◽  
Input Image ◽  
Binary Image ◽  
Large Set ◽  
Image Simulation ◽  
Compression Scheme ◽  
Binary Images ◽  
Visual Distortion

In this paper, the authors present a binary image compression scheme that can be used either for lossless or lossy compression requirements. This scheme contains five new contributions. The lossless component of the scheme partitions the input image into a number of non-overlapping rectangles using a new line-by-line method. The upper-left and the lower-right vertices of each rectangle are identified and the coordinates of which are efficiently encoded using three methods of representation and compression. The lossy component, on the other hand, provides higher compression through two techniques. 1) It reduces the number of rectangles from the input image using our mathematical regression models. These mathematical models guarantees image quality so that rectangular reduction should not produce visual distortion in the image. The mathematical models have been obtained through subjective tests and regression analysis on a large set of binary images. 2) Further compression gain is achieved through discarding isolated pixels and 1-pixel rectangles from the image. Simulation results show that the proposed schemes provide significant improvements over previously published work for both the lossy and the lossless components.

Rice Kernel Separations Using Contour Analysis and Skeleton

2015 ◽  
Vol 781 ◽  
pp. 515-518
Author(s):  
Chaladchai Siriwongkul ◽  
Pattarawit Polpinit
Keyword(s):  
Quality Evaluation ◽  
Median Filter ◽  
Processing Method ◽  
Separation Process ◽  
Contour Analysis ◽  
Separation Algorithm ◽  
Rice Quality ◽  
Binary Images ◽  
Separation Line ◽  
Broken Rice

Determine the percentage of broken rice kernel is crucial for rice quality evaluation. This paper studies a digital image processing method that can effectively separate touching rice kernels in an image of rice used for quality evaluation. An alternative separation algorithm based on contour analysis and skeleton is proposed to separate touching rice kernels. The proposed algorithm can be divided into three parts, namely, pre-processing, obtaining the candidates for separation line endpoints, and analysis for separation process. In the pre-processing, the images are converted into grayscale images. Then the median filter is applied in order to remove noise. Finally the binary images are obtained using Otsu’s algorithm. The next step is to obtain the candidates for separation line endpoints from concave points on the contour of rice kernels. The final step is to draw a separation lines among the candidates using several categories based on concave analysis and skeleton. The experimental results show that the proposed algorithm can accurately separate touching rice kernels and as a result the accurate percentage of broken rice can be obtained.

GRAPH-BASED THINNING FOR BINARY IMAGES

1993 ◽  
Vol 07 (05) ◽  
pp. 1009-1030 ◽  
Author(s):  
SATOSHI SUZUKI ◽  
NAONORI UEDA ◽  
JACK SKLANSKY
Keyword(s):  
Input Image ◽  
Binary Image ◽  
Feature Points ◽  
Shape Distortion ◽  
Binary Images ◽  
Ideal Line ◽  
Geographical Maps ◽  
Line Patterns

A thinning method for binary images is proposed which converts digital binary images into line patterns. The proposed method suppresses shape distortion as well as false feature points, thereby producing more natural line patterns than existing methods. In addition, this method guarantees that the produced line patterns are one pixel in width everywhere. In this method, an input binary image is transformed into a graph in which 1-pixels correspond to nodes and neighboring nodes are connected by edges. Next, nodes unnecessary for preserving the topology of the input image and the edges connecting them are deleted symmetrically. Then, edges that do not contribute to the preservation of the topology of the input image are deleted. The advantages of this graph-based thinning method are confirmed by applying it to ideal line patterns and geographical maps.

BINARY IMAGE WATERMARKING THROUGH BLURRING AND BIASED BINARIZATION

2005 ◽  
Vol 05 (01) ◽  
pp. 67-87 ◽  
Author(s):  
HAIPING LU ◽  
YUN Q. SHI ◽  
ALEX C. KOT ◽  
LIHUI CHEN
Keyword(s):  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
Image Watermarking ◽  
Binary Image ◽  
Experimental Results ◽  
Cosine Transform ◽  
Binary Images ◽  
Blurred Image ◽  
Binarization Threshold

Digital watermarking has been proposed for the protection of digital medias. This paper presents two watermarking algorithms for binary images. Both algorithms involve a blurring preprocessing and a biased binarization. After the blurring, the first algorithm embeds a watermark by modifying the DC components of the Discrete Cosine Transform (DCT), followed by a biased binarization, and the second one embeds a watermark by directly biasing the binarization threshold of the blurred image, controlled by a loop. Experimental results show the imperceptibility and robustness aspects of both algorithms.

Character Detection and String Identification on a Signboard in Scene Image Based on Criterions of Segmented Images

2011 ◽  
Vol 103 ◽  
pp. 658-666
Author(s):  
Hideaki Kawano ◽  
Hideaki Orii ◽  
Hiroshi Maeda
Keyword(s):  
Image Segmentation ◽  
Binary Image ◽  
Color Information ◽  
Binary Images ◽  
Input Interface ◽  
Scene Image ◽  
Segmented Images

In this paper, a method which specifies the signboard region and extracts the charactersinside the signboard is proposed.We usually take notes not to forget what we should leave to memory.But it is often that the task is too troublesome. Our aim is the development of a new input-interface soas to input texts froma picture.Most of signboards are composed of almostmonochromatic region. Onthe basis of this observation, image segmentation using color information is applied, and then we getsome binary images by applying threshold for each segmented region. Each binary image is enclosedby the smallest circumscribed square. The signboard region is specified according to distribution andarea of the white pixels inside the square. As a result of experiment, we confirmed the effectivenessof the proposed method.

scholarly journals Image skeletonization based on combination of one- and two-sub-iterations models

Informatics ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 25-35
Author(s):  
J. Ma ◽  
V. Yu. Tsviatkou ◽  
V. K. Kanapelka
Keyword(s):  
Mathematical Model ◽  
Computational Complexity ◽  
Relative Position ◽  
Binary Image ◽  
Iteration Algorithm ◽  
Layer By Layer ◽  
Original Image ◽  
High Quality ◽  
Binary Images ◽  
High Computational Complexity

This paper is focused on the field of the skeletonization of the binary image. Skeletonization makes it possible to represent a binary image in the form of many thin lines, the relative position, sizes and shape of which adequately describe the size, shape and orientation in space of the corresponding image areas. Skeletonization has many variety methods. Iterative parallel algorithms provide high quality skeletons. They can be implemented using one or more sub-iterations. In each iteration, redundant pixels, the neighborhoods of which meet certain conditions, are removed layer by layer along the contour and finally they leave only the skeleton. Many one-sub-iterations algorithms are characterized by a breakdown in connectivity and the formation of excess skeleton fragments. The highest-quality skeletons are formed by the well-known single-iteration OPTA algorithm, which based on 18 binary masks, but it is sensitive to contour noise and has a high computational complexity. The Zhang and Suen two-iteration algorithm (ZS), which is based on 6 logical conditions, is widely used due to its relative simplicity. But it suffers from the problem of the blurs of the diagonal lines with a thickness of 2 pixels and the lost of the square which size is 2×2 pixels. Besides, both algorithms mentioned above do not achieve the unit pixel thickness of the skeleton lines (many non-node pixels have more than two neighbors). Mathematical model and OPCA (One-Pass Combination Algorithm) algorithm which is based on a combination and simplification of single-iterative OPTA and two-iterative ZS are proposed for constructing extremely thin bound skeletons of binary images with low computational complexity. These model and algorithm also made it possible to accelerate the speed of skeletonization, to enhance recoverability of the original image on the skeleton and to reduce the redundancy of the bonds of the skeleton elements.

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