Objects In Binary Images Research Articles

Fast Object Extraction and Euler Number on Block Represented Images

The identification of objects in binary images is a fundamental task in image analysis and pattern recognition tasks. The Euler number of a binary image is an important topological measure which is used as a feature in image analysis. In this paper, a very fast algorithm for the detection and localization of the objects and the computation of the Euler number of a binary image is proposed. The proposed algorithm operates in one scan of the image and is based on the Image Block Representation (IBR) scheme. The proposed algorithm is more efficient than conventional pixel based algorithms in terms of execution speed and representation of the extracted information.

Object motion detection in home security system using the binary-image comparison method based on robot operating system 2 and Raspberry Pi

This study aims is to build a home security system based on object motion detection using the Robot Operating System 2 (ROS2) and the Raspberry Pi. ROS2 in this study was used to read and process the camera data and to control the buzzer sound. At the same time, the Raspberry Pi hardware functioned to run ROS2 using the Linux Ubuntu 18.04 operating system. The camera function to read the image and video data could be developed for the input device to control the home security systems based on object motion detection. The method used to detect an object motion was Binary-Image Comparison (BIC). This method works by comparing the value of the binary image object with the binary master and using it as a decision-making algorithm when the camera detected the object movement based on the object colors. The object colors that were detected in this study were red, yellow, green, and blue. Each object color was processed using the OpenCV library in the ROS2 node service. After that, all of the nodes communicated through topics to communicating and exchanging the message data. This study has successfully developed a prototype that can generate a buzzer sound warning to the user when the camera detected the object motion based on the object color.

Counting the Number of Active Spermatozoa Movements Using Improvement Adaptive Background Learning Algorithm

The most important early stage in sperm infertility research is the detection of sperm objects. The success rate in separating sperm objects from semen fluid has an important role for further analysis. This research performed the detection and calculation of human spermatozoa. The detected sperm was the moving sperm in the video data. An improvement of Adaptive Background Learning was applied to detect the moving sperm. The purpose of this method is to improve the performance of Adaptive Background Learning algorithm in background subtraction process to detect and calculate moving sperm on the microscopic video of sperm fluid. This paper also compared several other background subtraction algorithms to conclude the appropriate background subtraction algorithm for sperm detection and sperm counting. The process done in this research was preprocessing using the Gaussian filter. The next was background subtraction process, followed by morphology operation. To test or validate the detection results of any background subtraction algorithm used, the foreground mask results from the morphological operation were compared to the ground truth of moving sperm image. For visualization purposes, every BLOB area (white object in binary image) on the foreground were given a bounding box to the original frame and the number of BLOB objects present in the foreground mask were counted. This shows that the system had been able to detect and calculate moving sperm. Based on the test results, Adaptive Background Learning method had a value of F-measure of 0.9205 and succeeded in extracting sperm shape close to the original form compared to other methods.

An Algorithm for Calculating Objects’ Shape Features in Binary Images

The shape features of objects (connected components) in binary images are very important for image analysis, pattern (object) recognition, and computer vision. Conventional algorithms for calculating the shape features of objects in binary images can only calculate the shape features of objects without holes. This paper presents an algorithm or calculating the shape features of objects in binary images with holes. Based on a contour-tracing-based connected-component labeling algorithm, our proposed algorithm can calculate the number of objects, the number of holes, the Euler number in a binary image, extract the contours of objects, and calculate the area, perimeter, circularity, centroid of each object. Our proposed algorithm is able to make a contribution to image analysis, pattern recognition, and computer vision.

Set-based modeling and observer design for planar structure from motion

Sets that are defined either from closed contours or from a set of points are generic descriptors for several kinds of objects in binary images. In this paper, we derive a novel model in the space of sets and design an observer for the proposed model to estimate the depth and orientation of planar objects from a camera. This problem is well known as structure from motion. When the objects are only partially projected on the image plane of the camera, our model makes object depth and orientation estimation possible without feature tracking and matching between distinct image frames (i.e., the so-called correspondence problem), which is an advantage over the image moments-based model. However, the proposed model fails in some situations (the failures can be seen as brief instabilities), and it is not always continuous. To compensate for these drawbacks, we designed a fast observer based on L1 control theory with a binary signal for the proposed model. Stability analysis with respect to a certain asymptotic instability ratio is also presented in this paper. The effectiveness of the proposed model and observer is demonstrated through simulations and experimental results.

One Layer Object Separation Algorithm in Binary Image

We propose an algorithm to separate black object in binary image. The algorithm is designed to perform vertical separation first, then followed with horizontal separation. This process is done only once (one layer). Results showed that the algorithm potentially could be developed into n-layer separation in a recursive function. The algorithm also potentially could be developed in adaptive to determine when to use a vertical or horizontal separation first. It could be expected to reduce the recursive number.

A comparative analysis of the efficiency of the stochastic gradient approach to the identification of objects in binary images

A comparative analysis of the computational complexity and probability of false recognition for object identification methods in images, which are based on a comparison with a pattern, viz., correlation-extreme method, contour analysis method, and stochastic gradient identification algorithm. Possibilities of parametric optimization of the studied methods are considered.

탄성변형에너지 측도를 이용한 부분적으로 가려진 이진 객체의 인식

Process of recognizing objects in binary images consists of image segmentation and pattern matching. If binary objects in the image are assumed to be separated, global features such as area, length of perimeter, or the ratio of the two can be used to recognize the objects in the image. However, if such an assumption is not valid, the global features can not be used but local features such as points or line ∙제1저자 : 문영인 ∙교신저자 : 구자영 ∙투고일 : 2014. 8. 20, 심사일 : 2014. 9. 5, 게재확정일 : 2014. 9. 27. * 단국대학교 컴퓨터학과 졸업(Dept. of Computer Science and Engineering, Dankook University) ** 단국대학교 소프트웨어학과(Dept. of Software Science, Dankook University) 64 Journal of The Korea Society of Computer and Information October 2014 segments should be used to recognize the objects. In this paper points with large curvature along the perimeter are chosen to be the feature points, and pairs of points selected from them are used as local features. Similarity of two local features are defined using elastic deformation energy for making the lengths and angles between gradient vectors at the end points same. Neighbour support value is defined and used for robust recognition of partially occluded binary objects. An experiment on Kimia-25 data showed that the proposed algorithm runs 4.5 times faster than the maximum clique algorithm with same recognition rate. ▸

A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search

A novel nonparametric concavity point analysis-based method for splitting clumps of convex objects in binary images is presented. The method is based on finding concavity point-pairs by using a variable-size rectangular window. The concavity point-pairs can be either connected with a straight split line or with a line that follows a path of minimum or maximum intensity on an accompanying grayscale image. Using straight lines can result in non-smooth contours. Therefore, post-processing steps that remove acute angles between split lines are proposed. Results obtained with images that have clumps of biological cells show that the method gives accurate results.

Using Two-Point Set Statistics to Estimate the Diameter Distribution in Boolean Models with Circular Grains

This paper deals with the counting and measuring of overlapping circular objects in binary images, a problem that arises in the mineral processing industry when estimating the distribution of bubble diameters in flotation cells in order to predict the geometallurgical performance of the flotation process. By viewing the images as realizations of a stationary planar Boolean model with circular grains and by using two-point set statistics, it is possible to jointly estimate the average number of objects per unit area and their diameter distribution. When applied to real and simulated images, this method is shown to give accurate estimates, to be robust to the presence of noise contaminating the images and of moderate drifts in the number of objects per unit area, and to speed up the processing time with respect to currently used methods. Combined, these properties serve to make the method suitable for an on-line monitoring of the flotation process.

TOUCHING GRAIN KERNELS SEPARATION BY GAP-FILLING

Separation of touching grain kernels is a recurring problem in image analysis. Morphological methods to separatemerged objects in binary images are generally based on the watershed transformapplied to the inverse of the distance function. This method is efficient with roughly circular objects, but cannot separate objects beyond a certain elliptic shape nor when the contact zones are too numerous or too large. This paper presents a gap-filling method applied to the skeleton of the image background as an alternative technique to go further in the fused objects separation process. Open lines resulting from skeletonization are prolonged according to their direction from corresponding end points. If the distance between two lines is smaller than a certain value, their respective end points are connected. Results of combined use of watershed and gap-filling based methods are presented on sample binary images. An example of its use on an particularly complex image containing rice grains shows that it allows to segment up to 90% of the grains when classical watershed methods allow only to segment 25% of the grains. An application to breakage and cracks assessing of parboiled rice kernels is presented.

A Fast Convex Hull Algorithm for Binary Image

Convex hull is widely used in computer graphic, image processing, CAD/CAM and pattern recognition. In this work, we derive some new convex hull properties and then propose a fast algorithm based on these new properties to extract convex hull of the object in binary image. It is achieved by computing the extreme points, dividing the binary image into several regions, scanning the regions existing vertices dynamically, calculating the monotone segments, and merging these calculated segments. Theoretical analyses show that the proposed algorithm has low complexities of time and space. Povzetek: Predstavljen je nov algoritem za obdelavo binarnih slik.

Realising emergent image preprocessing tasks in cellular-automaton-alike massively parallel hardware

In this paper, we present an emergent computing architecture scheme, denoted as marching pixels (MPs), which is well-suited for massively-parallel embedded systems based realising smart optical sensors. Smart in this context refers to sensors in which signal capturing and signal processing is combined either in one chip or in a compact 3D chip stack. MPs are life-like agents, which are crawling within a pixel field in such a chip in order to find centre points and other attributes of objects in binary images. MPs fulfill these tasks only by utilising local interaction and stigmertic communication. Principal ideas, the architecture and first results are described in this paper along with a comparison to both conventional and other unconventional computing approaches.

Topology preserving alternating sequential filter for smoothing two-dimensional and three-dimensional objects

We introduce the homotopic alternating sequential filter as a new method for smoothing two-dimensional (2D) and three-dimensional (3D) objects in binary images. Unlike existing methods, our method offers a strict guarantee of topology preservation. This property is ensured by the exclusive use of homotopic transformations defined in the framework of digital topology. Smoothness is obtained by the use of morphological openings and closings by metric disks or balls of increasing radius, in the manner of an alternating sequential filter. The homotopic alternating sequential filter operates both on the object and on the background, in an equilibrated way. It takes an original image X and a control image C as input, and smooths X "as much as possible" while respecting the topology of X and geometrical constraints implicitly represented by C. Based on this filter, we introduce a general smoothing procedure with a single parameter which allows to control the degree of smoothing. Furthermore, the result of this procedure presents small variations in response to small variations of the parameter value. We also propose a method with no parameter for smoothing zoomed binary images in 2D or 3D while preserving topology.

A parallel algorithm for generating chain code of objects in binary images

This paper addresses parallel execution of chain code generation on a linear array architecture. The contours in the proposed algorithm are viewed as a set of edges (or contour segments) that can be traced by a top-down contour tracing method to generate the chain codes for the outer and inner object contours. A parallel algorithm that contains the chain code generating rules and operations needed is also described, and the algorithm is mapped onto a one-dimensional systolic array containing ⌈ 1 2 (N+1)⌉ processing elements (PEs) to devise this architecture. The architecture extracts the contours of objects and quickly generates the corresponding chain codes after the image data in all rows are inputted in a linear fashion. The total processing time for generating the chain codes in an N× N image is O(3 N). By doing so, the real-time requirement is fulfilled and its execution time is independent of the image content. In addition, a partition method is developed to process an image when the parallel architecture has a fixed number of PEs; say two or more. The total execution time for an N× N image by employing a fixed number of PEs is N( N+1)/ M+2( M−1), when M is the fixed number of PEs.

Comparison of the maximum likelihood ratio test algorithm and linear filters for target location in binary images

We consider the problem of estimating the position of objects in binary images corrupted with nonoverlapping and spatially nonhomogeneous noise. For this application, we compare classical linear filters with the recently proposed maximum likelihood ratio test (MLRT) algorithm, which is optimal in the maximum likelihood sense for object detection. We first demonstrate that the MLRT algorithm can be approximated with a good precision by the square of a correlation product. We then compare the MLRT with the Classical Matched, Phase-only and Optimal Tradeoff filters in terms of probability of correct location. We conclude that if they are properly regularized, linear filters can achieve a performance level comparable to that of the MLRT. This result is important in the design of optical correlators, which often implement linear filters with binary input spatial light modulators.

Image formation from severely undersampled scalar infrared data sets

The author describes the formation of an image from no more than ten (and usually fewer) scalar sensor readings. An infrared (IR) signal source is assumed, placing unusual demands on signal estimation and image construction algorithms. An onion-growth image construction algorithm that generates a single binary image object consistent with sensor measurements is presented. It is so called because it identifies an approximate center of mass, or seed pixel, and then grows a series of concentric circles around the seed pixel. Synthetic object studies illustrate the effectiveness of the image construction algorithm and demonstrate that, image artifacts are suppressed or eliminated by using the largest possible sensor field of view. >

Parallel syntactic thinning by recoding of binary pictures

In recoding with three bits the pixels of objects in binary images according to some measure of distance to the background, parallel thinning is achieved using syntactic rules. The codes in a 3 × 2 window are searched for interrelations which determine the new code of the central pixel. The local structure of the codes is determined through a binary cascade of tables. To each structure corresponds a rewriting rule. The new code is the output of the cascade. A two-scan postprocessing is needed to obtain a skeleton with linear structure.