Image enhancement refers to processing images to make them more suitable for display or further image analysis. An enhancement procedure improves future automated image-processing steps (detection, segmentation, and recognition) for efficient system decision-making. This paper presents a new method of visual surveillance image enhancement that improves the visual quality of digital images that exhibit dark shadows due to the limited dynamic range of imaging. The proposed method base on 3-D block-rooting multi-scale transform domain technique, comprising: finding similar blocks in the image by block-matching; block-grouping for different block sizes; applying 3-D block-matching parametric image enhancement; calculating the quality measure of enhancement; optimizing parameters of image enhancement method through the quality measure of enhancement; fusing different enhanced images. Experimental results from test data set show that the proposed technique performs well and can improve the quality during the sharpening of the image details.
The article proposes an approach to improve the accuracy of restoring the boundaries of objects obtained to create 3D structures by analyzing data obtained by a machine vision system. At the first stage, the operation of reducing the number of color gradients is performed, the technique allows you to combine similar values into common enlarged structures. This operation allows you to simplify the analyzed objects, since small details are not important. In parallel with the first operation of denoising is performed. The paper proposes the application of the multicriteria processing method with the possibility of smoothing locally stationary sections and preserving the boundaries of objects. As an algorithm for strengthening the boundaries of objects, a modification of the combined multi-criteria method is used, which makes it possible to reduce the effect of salt/pepper noise and impulse failures, as well as to strengthen the detected boundaries of objects. The resulting images with enhanced boundaries are fed to the input of the block for constructing three-dimensional objects. The data obtained by both a stereo pair and a camera based on 3D construction using structured light were used in the work. On a set of synthetic data simulating the work in real conditions, the increase in the efficiency of the system using the proposed approach is shown. Based on field data under conditions of interfering factors in the form of dust/fog, the applicability of the proposed approach for solving problems of increasing the accuracy of restoring the boundaries of objects obtained to create three-dimensional structures is shown. Images of simple shapes are used as analyzed objects.
Automation of production processes using robots is a priority for the development of many industrial enterprises. Robotization is aimed at freeing a person from dangerous or routine work. At the same time, robots are able to perform tasks more efficiently than human, and the collaboration of a human and a robot allows to combine the strengths and effectiveness of robots and human cognitive ability into a single flexible system, and as a result, organize flexible methods of automation and reconfiguration of production processes. In this work, we focused on the implementation of the method of interaction between a person and a robot based on the recognition of gesture commands of a human-operator. An approach based on extraction a human skeleton and classification using a neural network is proposed as a method for recognizing actions. To test the effectiveness of the proposed algorithm, the possibility of transmitting gesture commands to the robot and organizing a contactless control method of the robot, simulation modeling was carried out in the RoboGuid environment. This environment is for industrial robots, provided by Fanuc.
The article considers the issue of using the multi-criteria smoothing method, with the possibility of adaptive parameter changes for various types of images. As an approach to implement the improvement of the group of images, the work proposes phased processing for each multi-channel image. As a first step, an algorithm for changing the color space is applied, in which multiple adaptive compression of the range occurs, based on a change in the size of the clusters. This algorithm allows adaptive absorption of adjacent pixel regions by analysis of histograms of the gradients. The application of this approach allows performing primary localization and simplification of the image. In the next step, we search for areas of significance (maximum number of transitions or complexity of an object). We check the coincidence of areas in a multi-channel image. Next, we perform image smoothing. As a filter mask, the data obtained at the previous stages of processing are used. The parameters of the multicriteria method depend on the value of a certain standard deviation coefficient and the analysis area (object boundary, detailed section, or locally stationary region). At the final stage, we perform an image enhancement operation based on the application of the α-rooting algorithm in local areas defined in the first stages of the algorithm. All operations are performed for each image in all the channels. The approach proposed in the article showed high efficiency and the possibility of applying for the processing of multichannel images. This is method can be expanded to other groups and types of sensors.
KEYWORDS: Video, Robots, Information visualization, Control systems, Detection and tracking algorithms, Manufacturing, Systems modeling, Visual process modeling, Machine vision, Image processing
A crucial technology in modern smart manufacturing is the human-robot collaboration (HRC) concept. In the HRC, operators, and robots unite and collaborate to perform complex tasks in a variety of scenarios, heterogeneous and dynamic conditions. A unique role in the implementation of the HRC model, as a means of sensation, is assigned to machine vision systems. It provides the receipt and processing of visual information about the environment, the analysis of images of the working area, the transfer of this information to the control system, and decision-making within the framework of the task. Thus, the task of recognizing the actions of a human-operator for the development of a robot control system in order to implement an effective HRC system becomes relevant. The operator commands fed to the robot can have a variety of forms: from simple and concrete to quite abstract. This introduces several difficulties when the implementation of automated recognition systems in real conditions; this is a heterogeneous background, an uncontrolled work environment, irregular lighting, etc. In the article, we present an algorithm for constructing a video descriptor and solve the problem of classifying a set of actions into predefined classes. The proposed algorithm is based on capturing three-dimensional subvolumes located inside a video sequence patch and calculating the difference in intensities between these sub-volumes. Video patches and central coordinates of sub-volumes are built on the principle of VLBP. Such a representation of three-dimensional blocks (patches) of a video sequence by capturing sub-volumes, inside each patch, in several scales and orientations, leads to an informative description of the scene and the actions taking place in it. Experimental results showed the effectiveness of the proposed algorithm on known data sets.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.