Image Selection Method Research Articles

Image Selection Method from Image Sequence to Improve Computational Efficiency of 3D Reconstruction: Application of Robust Threshold Based on Multimodal Test to Images

This paper describes the image selection method by multimodal detection for improving the computational efficiency of three-dimensional (3D) reconstruction based on images in a time-series. To decrease the calculation time of the 3D reconstruction, an adequate selection from the images is required. For this reason, we introduced multimodal detection by a statistical test on the image selection process, and then applied it to soundness evaluation of the displacements based on the optical flow between images acquired by a camera. The results suggest that suitable images can be extracted from the images in a time-series for decreasing the calculation time of the 3D reconstruction. Therefore, the suitable images selected by the proposed method contributed to efficiently performing the 3D reconstruction.

Optimal selection from extremely redundant satellite images for efficient large-scale mapping

Nowadays, the substantially increasing optical remote sensing satellites are constantly generating tremendous amount of images. However, superabundant images would easily lead to unnecessary computation and time costs for photogrammetric mapping product generation; thus, data redundancy should be properly reduced to improve production efficiency. In this study, we proposed an optimal selection method for extracting a minimal subset from extremely redundant satellite images, aiming at providing full coverage of the area of interest (AOI) while maintaining the minimally required overlap between adjacent scenes for efficient large-scale mapping applications. We first constructed a novel optimization model by rasterizing the target AOI into regular grids and converting the image selection problem into a grid voting problem. Then, we applied the constraints on image quality, which we efficiently quantified using metadata information, and the distribution reasonability, which we designed by penalizing the adjacent grids voting for different images, to the model to achieve an optimal selection result. We modeled the optimization problem as a Markov random field. The experimental results on four datasets, which all cover large-scale areas with 165,456, 38,252, 729, and 25,922 scenes of optical satellite images, respectively, demonstrated that the proposed approach can substantially reduce the amount of raw data whiling maintaining high image quality and sufficient overlap. The quantitative evaluation indicated that the proposed method considerably outperformed the state-of-the-art methods in most of eight evaluation metrics describing data quality, simplicity, and feasibility. Furthermore, the additional mapping production experiments on the provincial-level dataset, revealed that the proposed image selection method can significantly improve the production efficiency with only a marginal decrease in product accuracy.

A method of brain computer cooperative navigation combined with simultaneous localization and mapping

Introducing human brain intelligence into robot system is an effective means to improve robot's cognition and decision-making ability. Aiming at the problems of human brain fatigue and the need of multi lead information in brain robot control, a brain computer cooperative navigation method combining synchronous localization and mapping (SLAM) is proposed in this paper. Through the steady-state visual evoked potential based on three leads, the image of the target area of interest of human brain is selected, and the brain computer cooperative navigation task is completed by combining SLAM and artificial potential field. The test results show that the average accuracy of the target area image selection method based on steady-state visual evoked potential is 94.17%, which proves that the three leads are effective. On this basis, the brain computer cooperative navigation method combined with SLAM is tested. The results show that the completion rate of navigation task is as high as 92.5%. This method alleviates the fatigue of human brain and reduces the hardware requirements of EEG acquisition.

Computer simulation of adapting the object image selection method to video surveillance conditions

The article deals with the issues of computer modeling of methods for selecting images of objects against a non-uniform background. A test video sequence with given background and object parameters is considered, which provides imitation of one of the special cases of video surveillance conditions, namely, the convergence of the video surveillance point and the object. The issues of adaptation of the compensation selection method to the specified conditions of video surveillance are discussed. Examples of test images and graphs of dependences of the probability of correct determination of coordinates depending on the value of the local contrast of the object in relation to the background, obtained by computer simulation are given.

A multi-scale patch-wise algorithm for multi-exposure image fusion

Multi-exposure fusion is a high dynamic range image generation method. The challenge it faces is to reconstruct artifact-free high dynamic range images for dynamic scenes containing moving objects. This paper proposes a structural patch decomposition algorithm based on the Laplacian pyramid to obtain high dynamic range images without artifacts. This paper discusses the relationship between the total exposure quality score and the number of poorly exposed pixels and proposes a robust reference image selection method. It avoided the local exposure distortion in the fused image caused by the improper selection of reference images and improved the algorithm’s fusion effect in dynamic scenes. More importantly, this paper inject a new fusion mechanism into the conventional Laplacian pyramid fusion framework, namely the non-standardized structural patch decomposition algorithm. While retaining its original advantages, it reduces the computational complexity of the algorithm. Experimental results on dynamic and static scenes show that the proposed algorithm can generate fused images with a clear structure and almost no halo artifacts.

A cover image selection method providing stego-object robustness to various image processing attacks

A cover image selection method providing stego-object robustness to various image processing attacks

An Automatic Centroid Image Selection Method Based on Fuzzy Logic Reasoning in Image Deduplication

Centroid selection plays a key role in image deduplication. It means selecting an optimal solution as a centroid image in a duplicate image set. Meanwhile, it will delete other image copies and establish pointers to point to the centroid image in the original position. At present, there is not a mature centroid selection scheme. Centroid selection mainly relies on users to manually complete according to experience. In a massive data environment, it will consume a lot of human resources, and it is easy to make mistakes by subjective judgment. Therefore, in order to solve this problem, this article proposes an automatic centroid image selection method based on fuzzy logic reasoning. In a duplicate image set, the image attribute information is used to automatically infer comprehensive quantized values to represent images, and the centroid image is selected by comparing the quantized values. The experimental results showed that the scheme not only could meet the visual perception characteristics, but also meet the purpose of image deduplication.

AN OPTIMAL IMAGE SELECTION METHOD TO IMPROVE QUALITY OF RELATIVE RADIOMETRIC CALIBRATION FOR UAV MULTISPECTRAL IMAGES

Abstract. Radiometric calibration has become important pre-processing with increasing use of unmanned aerial vehicle (UAV) images in various applications. In order to convert the digital number (DN) to reflectance, vicarious radiometric calibration is widely used including relative radiometric calibration. Some UAV sensor systems can measure irradiance for precise relative radiometric calibration. However, most of UAV sensor systems cannot measure irradiance and therefore precise relative radiometric calibration is needed to produce reflectance map with vicarious calibration. In this study, an optimal image selection method is proposed to improve quality of relative radiometric calibration. The method, relative calibration by the optimal path (RCOP), uses filtered tie points acquired in geometric calibration based on selection optimal image by Dijkstra algorithm. About 100 multispectral images were acquired with a RedEdge-M camera and a fixed-wing UAV. The reflectance map was produced using RCOP and vicarious calibration using ground reference panels. A validation data was processed using irradiance for precise relative radiometric calibration. As a result, the RCOP method showed root mean square error (RMSE) of 0.03–0.10 reflectance to validation data. Therefore, the proposed method can be used to produce precise reflectance map by vicarious calibration.

A privacy-preserving protocol for efficient nighttime haze removal using cloud based automatic reference image selection and color transfer as a service

The advanced Internet technologies have migrated the people to rejoice a virtual environment known as cloud computing. The user can avail the desired services on a pay-as-you-go model, without worrying about the burden of infrastructure maintenance. However, privacy is one of the major issues in cloud computing. This issue is further widened for highly confidential multimedia data like surveillance images and videos. In the context of cloud based smart multimedia systems, it has been found that due to inconsistent weather conditions, there is a usual requirement of post-processing the captured multimedia for better appearance. However, privacy related concerns are resisting users to move their data to the cloud. One such problem is addressed in this paper, specializing the task of efficient nighttime haze removal using privacy-preserving cloud based automatic reference image selection and color transfer as a service. Different from daytime conditions, nighttime haze image consists of multiple light sources, which makes an ambiguous situation for haze removal. We address this problem by first selecting an appropriate gray image as the reference and then transferring its colors to nighttime haze image. This makes the transformed image a suitable candidate for radiance recovery. The proposed protocol is designed to securely outsource this considerable burden from user end. We accomplish this by first proposing an automatic reference gray image selection method, followed by efficient handling mechanisms for technical challenges arising due to performing color transfer operations securely over cloud. Experimental results and validation demonstrates superiority of the proposed method over state-of-the-art schemes. Security analysis of the proposed protocol is established through a challenge-response game model.

Examination of Image Processing Method and Image Selection Method for Region of Interest Extraction in an Ultrasound Moving Image for Swallowing Evaluation

Examination of Image Processing Method and Image Selection Method for Region of Interest Extraction in an Ultrasound Moving Image for Swallowing Evaluation

Shape-weighted bubble size distribution based reagent predictive control for the antimony flotation process

Antimony flotation is a complicated industrial process with an unknown system structure. Manual manipulation is still widely used by observing the froth appearance, which relies on operators’ experience. Therefore, this paper proposes a reagent predictive control strategy for the antimony flotation process based on machine vision, which is an optional replacement of manual control. Froth image is a good indicator to reflect the working conditions of the process. A shape-weighted bubble size distribution (SWBSD) is developed to characterize the froth image feature. The SWBSD combines the bubble size and bubble shape, which differs from the separate use of froth features. Since lots of the froth images can be obtained, a random selection of an image from a froth video as the indicator of the working conditions is not advisable. A typical froth image selection method is proposed to choose the froth image which can best characterize the current working conditions. Due to the unknown system model, multi-output least square support vector regressor (LS-SVR) is used to predict the future image feature according to the current image feature and reagent dosages. Based on the above methods, predictive control technique is employed to calculate the optimal control inputs of the reagents. Simulations using the real-world production data verify the effectiveness of the proposed control strategy. In further, industrial experiments in the antimony flotation plant of China demonstrate that our control strategy improved the tracking performance and decreased the tailing grade, compared with the manual manipulation.

Non-rigid registration of serial section images by blending transforms for 3D reconstruction

In this research, we propose a novel registration method for three-dimensional (3D) reconstruction from serial section images. 3D reconstructed data from serial section images provides structural information with high resolution. However, there are three problems in 3D reconstruction: non-rigid deformation, tissue discontinuity, and accumulation of scale change. To solve the non-rigid deformation, we propose a novel non-rigid registration method using blending rigid transforms. To avoid the tissue discontinuity, we propose a target image selection method using the criterion based on the blending of transforms. To solve the scale change of tissue, we propose a scale adjustment method using the tissue area before and after registration. The experimental results demonstrate that our method can represent non-rigid deformation with a small number of control points, and is robust to a variation in staining. The results also demonstrate that our target selection method avoids tissue discontinuity and our scale adjustment reduces scale change.

Novel Image Set Compression Algorithm Using Rate-Distortion Optimized Multiple Reference Image Selection

Image set compression has recently become an active research topic due to the explosion of digital photographs. In order to efficiently compress the image sets of similar images with moving objects, in this paper, we propose a novel algorithm for image set compression using multiple reference images. First, for an image set, its depth-constrained minimum arborescence is generated. We then present a reference image candidate determination method to build the reference image candidates for the images of the set. Furthermore, we propose a rate-distortion optimized multiple reference image selection method. This method compares the correlation between every image and each of its reference image candidates to produce its multiple reference images. Finally, compressed image data are achieved by employing block-based motion compensation and residue coding. In addition, we also give a new way of access to images to keep the same access delay with single reference image-based schemes. Compared with the state-of-the-art image compression algorithms, experimental results show that our proposed algorithm can significantly improve the image compression performance.

Automatic Reference Image Selection for Color Balancing in Remote Sensing Imagery Mosaic

Selection of a reference image is an important step in color balancing. However, the past research and currently available methods do not focus on it, leading to the lack of an effective way to select the reference image for color balancing in remote sensing imagery mosaic. This letter proposes a novel automatic reference image selection method that aims to select the reference images by assessing multifactors according to the land surface types of the target images. The proposed method addresses the limitations caused by the use of a single assessment factor as well as the selection of a single image as the reference in traditional methods. In addition, the proposed method has a wider range of applications than those requiring no reference image. The visual experimental results indicate that the proposed method can select the suitable reference images, which benefits the color balancing result, and outperforms the other comparative methods. Moreover, the absolute mean value of skewness metric of the proposed method is 0.0831, which is lower than the values of the other comparison methods. It indicates that the result of the proposed method had the best performance in the color information. The quantitative analyses with the metric of absolute difference of mean value indicate that the proposed method has a good ability in maintaining the spectral information, and the spectral changing rates had been reduced at least 10.66% by the proposed method when compared with the other methods.

A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics

A training image (TI) can be regarded as a database of spatial structures and their low to higher order statistics used in multiple-point geostatistics (MPS) simulation. Presently, there are a number of methods to construct a series of candidate TIs (CTIs) for MPS simulation based on a modeler's subjective criteria. The spatial structures of TIs are often various, meaning that the compatibilities of different CTIs with the conditioning data are different. Therefore, evaluation and optimal selection of CTIs before MPS simulation is essential. This paper proposes a CTI evaluation and optimal selection method based on minimum data event distance (MDevD). In the proposed method, a set of MDevD properties are established through calculation of the MDevD of conditioning data events in each CTI. Then, CTIs are evaluated and ranked according to the mean value and variance of the MDevD properties. The smaller the mean value and variance of an MDevD property are, the more compatible the corresponding CTI is with the conditioning data. In addition, data events with low compatibility in the conditioning data grid can be located to help modelers select a set of complementary CTIs for MPS simulation. The MDevD property can also help to narrow the range of the distance threshold for MPS simulation. The proposed method was evaluated using three examples: a 2D categorical example, a 2D continuous example, and an actual 3D oil reservoir case study. To illustrate the method, a C++ implementation of the method is attached to the paper.

A hybrid just-in-time soft sensor for carbon efficiency of iron ore sintering process based on feature extraction of cross-sectional frames at discharge end

Iron ore sintering is the second-most energy-consuming process in steelmaking. The main source of energy for it is the combustion of carbon. In order to reduce energy consumptions and improve industrial competitiveness, it is important to improve carbon efficiency. Reliable online prediction of the carbon efficiency would be extremely beneficial for making timely adjustments to the process to improve it. In this study, the comprehensive carbon ratio (CCR) was taken to be a measure of the carbon efficiency; and a soft sensing system was built to make an online estimation of the CCR. First, the sintering process was analyzed, and the key characteristics of the process parameters were extracted. Then, the configuration of the soft sensing system was devised based on the characteristics of the process. The system consists of three parts: an image selection, an image segmentation, and a hybrid just-in-time learning soft sensor (HJITL-SS). First, an image selection method was devised to automatically select the key frames (KFs) from the video taken at the discharge end of the sintering machine. Then, a genetic-algorithm-based fuzzy c-means clustering method was devised to extract feature parameters from the KFs. Finally, an HJITL-SS, which consists of online and offline submodels, was devised to estimate the CCR using the extracted feature parameters as inputs. Actual run data were used to verify the validity of our system. Accuracy, overfitness, and error distribution of the HJITL-SS, offline, and JITL-based soft sensing methods were compared, which show the validity of the HJITL-SS. The actual run results also show the validity of the soft sensing system with 97% of the actual runs are in an acceptable range.

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that, with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the ‘eigen-patterns’ of the natural system. The eigen-pattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

DEVELOPMENT OF IMAGE SELECTION METHOD USING GRAPH CUTS

Abstract. 3D models have been widely used by spread of many available free-software. Additionally, enormous images can be easily acquired, and images are utilized for creating the 3D models recently. The creation of 3D models by using huge amount of images, however, takes a lot of time and effort, and then efficiency for 3D measurement are required. In the efficient strategy, the accuracy of the measurement is also required. This paper develops an image selection method based on network design that means surveying network construction. The proposed method uses image connectivity graph. The image connectivity graph consists of nodes and edges. The nodes correspond to images to be used. The edges connected between nodes represent image relationships with costs as accuracies of orientation elements. For the efficiency, the image connectivity graph should be constructed with smaller number of edges. Once the image connectivity graph is built, the image selection problem is regarded as combinatorial optimization problem and the graph cuts technique can be applied. In the process of 3D reconstruction, low quality images and similar images are also extracted and removed. Through the experiments, the significance of the proposed method is confirmed. It implies potential to efficient and accurate 3D measurement.

DEVELOPMENT OF IMAGE SELECTION METHOD USING GRAPH CUTS

3D models have been widely used by spread of many available free-software. Additionally, enormous images can be easily acquired, and images are utilized for creating the 3D models recently. The creation of 3D models by using huge amount of images, however, takes a lot of time and effort, and then efficiency for 3D measurement are required. In the efficient strategy, the accuracy of the measurement is also required. This paper develops an image selection method based on network design that means surveying network construction. The proposed method uses image connectivity graph. The image connectivity graph consists of nodes and edges. The nodes correspond to images to be used. The edges connected between nodes represent image relationships with costs as accuracies of orientation elements. For the efficiency, the image connectivity graph should be constructed with smaller number of edges. Once the image connectivity graph is built, the image selection problem is regarded as combinatorial optimization problem and the graph cuts technique can be applied. In the process of 3D reconstruction, low quality images and similar images are also extracted and removed. Through the experiments, the significance of the proposed method is confirmed. It implies potential to efficient and accurate 3D measurement.

Three dimension reconstruction through measure-based image selection

Three dimension (3D) reconstruction is one of the research focus of computer vision and widely applied in various fields. The main steps of 3D reconstruction include image acquisition, feature point extraction and matching, camera calibration and production of dense 3D scene models. Generally, not all the input images are useful for camera calibration because some images contain similar and redundant visual information. These images can even reduce the calibration accuracy. In this paper, we propose an effective image selection method to improve the accuracy of camera calibration. Then a new 3D reconstruction algorithm is proposed by adding the image selection step to 3D reconstruction. The image selection method uses structure-from-motion algorithm to estimate the position and attitude of each camera, first. Then the contributed value to 3D reconstruction of each image is calculated. Finally, images are selected according to the contributed value of each image and their effects on the contributed values of other images. Experimental results show that our image selection algorithm can improve the accuracy of camera calibration and the 3D reconstruction algorithm proposed in this paper can get better dense 3D models than the normal algorithm without image selection.