Multi-image Processing Research Articles

Methods and research for deformation monitoring of earth and rock dams based on close-range photogrammetry

Abstract Traditional means of monitoring deformation in earth and rock dams encounter challenges such as low monitoring efficiency and limited coverage. Despite the potential of emerging technologies such as GPS and three-dimensional laser scanning, their adoption is expensive and hard to promote. This paper presents a deformation monitoring method for earth and rock dams based on the close-range photogrammetry technique. The proposed approach focuses on analytical algorithm the design and deployment of monitoring points, photographic schemes, camera checking and calibration, as well as deformation analysis methods. Initially, based on the analysis of the parsing algorithms’ applicability, they are fused to address the shortcomings of common image parsing methods in meeting the requirements of high precision and multi-image processing for deformation monitoring of earth and rock dams. Subsequently, the fused algorithm is introduced to analyze the acquired image data for 3D reconstruction, and the deformation in earth and rock dams is assessed based on the generated dense point cloud model. The proposed deformation monitoring method is applied to Pine Bridge Reservoir Dam, and the results demonstrated its capacity to comprehensively analyze the deformation. Furthermore, the required equipment is simple and easy to operate, aligning with the requirements for deformation monitoring accuracy of earth and rock dams.

Multi-image encryption scheme with quaternion discrete fractional Tchebyshev moment transform and cross-coupling operation

With quaternion theory, the traditional discrete fractional Tchebyshev transform is extended to the quaternion algebra domain for multi-image processing. A new multi-image encryption scheme based on quaternion discrete fractional Tchebyshev moment transform (QDFrTMT) and the cross-coupling chaotic system is suggested. The original images are first confused by fractal sorting square matrix and sine-logistic exponential chaotic map, and then the resulting image is divided into four matrices. With the quaternion symplectic form representation, a quaternion signal can be formed by the divided matrices, which can reduce the number of the discrete fractional Tchebyshev transforms used and enhance the computational efficiency. Subsequently, the quaternion array is encrypted with the proposed QDFrTMT. To overcome the weakness of the permutation-diffusion operation based on a single chaotic map, a Logistic-sine exponential chaotic map and a piece-wise linear chaotic map are cross-coupled. The final ciphertext images can be acquired by carrying out the dual-layer encryption processes in horizontal and vertical directions. Numerical simulations and security analyses verify the effectiveness of the multi-image encryption algorithm and its strong ability to counteract common attacks.

Multi support vector machine and image processing for diagnosis of coronary artery disease

Multi support vector machine and image processing for diagnosis of coronary artery disease

Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network.

Femtosecond X-ray pulse lasers are promising probes for the elucidation of the multiconformational states of biomolecules because they enable snapshots of single biomolecules to be observed as coherent diffraction images. Multi-image processing using an X-ray free-electron laser has proven to be a successful structural analysis method for viruses. However, the performance of single-particle analysis (SPA) for flexible biomolecules with sizes ≤100 nm remains difficult. Owing to the multiconformational states of biomolecules and noisy character of diffraction images, diffraction image improvement by multi-image processing is often ineffective for such molecules. Herein, a single-image super-resolution (SR) model was constructed using an SR convolutional neural network (SRCNN). Data preparation was performed in silico to consider the actual observation situation with unknown molecular orientations and the fluctuation of molecular structure and incident X-ray intensity. It was demonstrated that the trained SRCNN model improved the single-particle diffraction image quality, corresponding to an observed image with an incident X-ray intensity (approximately three to seven times higher than the original X-ray intensity), while retaining the individuality of the diffraction images. The feasibility of SPA for flexible biomolecules with sizes ≤100 nm was dramatically increased by introducing the SRCNN improvement at the beginning of the various structural analysis schemes.

A proposed brain tumor detection algorithm using Multi wavelet Transform (MWT)

Magnetic resonance imaging)MRI) is a technological development in the medical field. It is used to give images of the human body with high accuracy and good quality, facilitating the process of identifying and classifying diseases in the human body. One of the diseases that are diagnosed using MRI is a brain tumor, MRI which helps in the early diagnosis of the tumor, and helps the doctor to diagnose and identify the tumor and thus make the fastest medical decision. This paper used to identification and extraction of brain tumors from magnetic resonance images based on Multi Wavelet Transform (MWT) and image processing techniques. This project Multi Wavelet Transform (MWT) used in preprocessing stage to enhance the input image and denoising. Thresholding based is used for segmentation methods, classification of brain tumor is done using Statistical Classification Methods to classify the input MRI of brain into normal or abnormal. Firstly the location of tumor was presented & next its area computed.

Semantic Place Prediction With User Attribute in Social Media

In our daily life, we prefer to describe the location of a person with semantic words such as “travel,” “cinema” rather than geographic terminology such as GPS coordinates ($32^{\circ }24^{^{\prime }}12^{^{\prime \prime }}$32∘24'12''N, $2^{\circ }90^{^{\prime }}26.5^{^{\prime \prime }}$2∘90'26.5''E). This is because semantic places are easier to understand compared with precise geographic location. Existing methods only take text-image pairs into consideration, neglecting the user attribute, which is essential in semantic place prediction. In this work, we explore the impact of user attribute and propose user attribute included multimodal fusion models for semantic place prediction. Specifically, due to the complementarity of different modalities, multimodal fusion networks can adaptively learn the relationship between different modalities to fully use representational information from each modality. Besides, we propose a multi-image processing module to solve the scenario that a piece of Microblog text is attached to multiple images. Extensive experiments on our collected dataset verified the effectiveness of the proposed approach.

MULTIMODAL DATA PROCESSING BASED ON ALGEBRAIC SYSTEM OF AGGREGATES RELATIONS

Context. In many engineering tasks where the monitoring of changes in the characteristics of an observation object, subject, or process is required, it is necessary to process multimodal data recorded with respect to time moments when these characteristics are registered. In this paper, the author presents a new approach to solving the problem of multimodal data structures timewise processing, which allows to simplify the processing of such data by using the mathematical apparatus of an algebraic system of aggregates and thereby reduce the requirements to computing resources. The algebraic system of aggregates operates with such specific data structures as aggregates and multi-images. These complex data structures can be obtained as a result of data measuring, generating, recording, etc. The processing of such multimodal data can also require discrete intervals processing. Objective. The goal of the work is to formalise the relations between basic mathematical objects defined in the algebraic system of aggregates, such as elements, tuples and aggregates, as well as the data structures based on these mathematical objects, namely, discreet intervals and multi-images. Method. The research presented in this paper is based on both the algebraic system of aggregates and the concept of multi-image which enable multimodal data timewise processing. A carrier of the algebraic system of aggregates is an arbitrary set of specific structures – aggregates. An aggregate is a tuple of arbitrary tuples, elements of which belong to predefined sets. Aggregates can be processed by using logical, ordering, and arithmetical operations defined in the algebraic system of aggregates. A multi-image is a non-empty aggregate, the first tuple of which is a tuple of time values. Such tuple of time values represents a certain discrete interval. To process discrete intervals and multi-images, a set of relations is defined in the algebraic system of aggregates. This set includes relations between tuple elements, relations between tuples, and relations between aggregates. The relations between tuples enable arithmetical comparison, frequency comparison, and interval comparison. This mathematical apparatus can be used for both complex representation of object (process) multimodal characteristics and further timewise processing of data represented as multi-images. Results. The approach to discrete intervals and multi-images processing based on relations, which are defined in the algebraic system of aggregates, has been developed and presented in the paper. The author provides examples of the developed approach practical implementation. Conclusions. The results obtained in the research presented in this paper has shown that the relations defined in algebraic system of aggregates enable processing of complex data structures named multi-images in data modelling, prediction and other tasks. To allow data processing with respect to time scale, discrete intervals can be employed. A discrete interval is a tuple of time values. In the paper, the author shows how relations for discrete intervals comparison can be used for solving practical tasks. Besides, the author presents the software tools which can be used for practical implementation of the given theoretical approach by employing the domain-specific language ASAMPL.

Abstract 2337: Changes in GDF15 (growth/differentiation factor 15) expression and M2 macrophages during prostate carcinogenesis

Abstract GDF15 (growth/differentiation factor 15), also known as MIC-1 (Macrophage inhibitory cytokine 1), is a divergent member of the TGFβ superfamily of cytokines and is highly expressed in prostate tumors, but its role in prostate carcinogenesis and utility as a prognostic biomarker is unclear. We studied 91 prostate cancer cases that underwent surgery as their primary treatment and were then subsequently followed for biochemical recurrence (BCR). These cases also had a benign prostate biopsy at least one year before their prostate cancer diagnosis. In both the benign biopsy and tumor specimens, we quantified the intensity of GDF15 expression and characterized the presence of tumor associated macrophages by measuring the density of CD68-positive stained cells, and the M2 macrophage marker CD204 by immunohistochemical analysis. Marker expression was measured in prostate a) benign biopsy (BS), b) tumor-adjacent benign (BGS) and c) tumor tissue (MGS) using an automated multi-image processing macro developed in the ImageJ software. A Cox proportional hazards model was used to test the association of time to BCR of the low expression group compared with medium or high biomarker expression. The risk of BCR differed with the extent of GDF15 expression in the prostatic epithelial tissue of benign and tumor regions of the prostate. For instance, in BGS, high expression of GDF15 was associated with increased risk of BCR (hazard ratio (HR)=2.58, p=0.07). After controlling for PSA at diagnosis, Gleason grade and pathologic stage, high expression of GDF15 in BGS had a stronger association with BCR (HR=3.30, p=0.04). The risk of BCR increased with increasing level of infiltration of CD204/CD68 positive macrophages in BGS (HR=4.19, p=0.02) and MGS (HR=4.16, p=0.01) even after controlling for PSA at diagnosis, Gleason grade and pathologic stage. The combined expression levels of GDF15 and CD204/CD68 showed that prostate cancer cases with small GDF15 expression changes between BGS and MGS and high CD204/CD68 expression in BGS were at 3-fold higher risk of BCR (HR=3.67, p=0.05). In summary, expression levels of GDF15 and CD204 M2 macrophages in different regions of the prostate can change as the disease progresses from benign to a tumorigenic state and serve as markers for aggressive disease. Further evaluation of these dynamic differences in the prostate immune cellular profile in the pre-malignant and malignant state may offer additional insight into inflammatory-mediated prostate carcinogenesis. Citation Format: Sudha M. Sadasivan, Yalei Chen, Nilesh Gupta, Sean Williamson, Dhananjay Chitale, Xiaoxia Han, Kevin Bobbitt, Andrew Rundle, Deliang Tang, Benjamin A. Rybicki. Changes in GDF15 (growth/differentiation factor 15) expression and M2 macrophages during prostate carcinogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2337.

Principle of an innovative visual sensor based on combined laser structured lights and its experimental verification

To promote development of sensing technology applied in the welding process and improvement of production automation in the welding manufacturing industry, an innovative multi-functional monocular visual sensor based on combined laser structured lights (single-line & cross-lines) is proposed and devised. It can realize the detection of cross-sectional size parameters of welding groove, space position and posture of welding torch relative to the weldment or welding groove by using monocular vision. Its calibration makes full use of the intrinsic parameters, such as its internal structure parameters and the focal length of industrial CCD camera. Therefore, it is unnecessary to re-calibrate when the position and posture of welding torch have some variations during the welding process or the visual sensor reassembles with the welding torch, which availably improves the adaptability of this sensor in engineering application, so that it can apply to the welding process with complex space welding trajectory. Furthermore, with the use of combined laser structured lights, it effectively eliminates the problem of information loss in depth commonly existing in usual monocular visual sensor. Meanwhile, all the detection functions of this sensor are derived from the processing results of a single image captured by industrial CCD camera, which avoids the problems of time-consuming and large amount of calculation resulting from multi-images processing (multi-vision method or multi-angle time-sharing imaging in monocular vision), and thus significantly improves its real-time performance in engineering application. Its functional detection algorithms for the cross-sectional size parameters of welding groove and the height of welding torch derived from the principle of optical triangulation are given. After establishing a test platform, we carried out a series of detecting experiments for different types or sizes of V-type butt groove. By image processing, recognition and extraction of the feature points, the cross-sectional size parameters of welding groove and the height of welding torch calculate out through the derived detection algorithms. The acquired detection results have enough accuracy and their maximum relative errors are not exceeding 3.9%, which demonstrate the rationality of the physical design for the proposed visual sensor and the validity of the derived detection algorithms.

Abstract 4211: The role of GDF15 (growth/differentiation factor 15) during prostate carcinogenesis

Abstract GDF15 (growth/differentiation factor 15), also known as MIC-1 (Macrophage inhibitory cytokine 1), is a divergent member of the TGFβ superfamily of cytokines and is highly expressed in prostate tumors, but its role in prostate carcinogenesis and utility as a prognostic biomarker is unclear. We studied 91 prostate cancer cases that underwent surgery as their primary treatment and were then subsequently followed for biochemical recurrence (BCR). These cases also had a benign prostate biopsy at least one year before their prostate cancer diagnosis. In both the benign biopsy and tumor specimens, we quantified the intensity of GDF15 expression and characterized the presence of tumor associated macrophages by measuring the density of CD68-positive stained cells and the M2 macrophage marker CD204 by immunohistochemical analysis. Marker expression was measured in a) benign biopsy, b) tumor-adjacent benign and c) tumor tissue using an automated multi-image processing macro developed in the ImageJ software. Expression measurements were log2 transformed and high-low cut-off points were selected that optimized the association of biomarker expression with BCR-free survival. A Cox proportional hazards model was used to test the association of time to BCR with low vs high biomarker expression. During follow-up, 23 cases (25.2%) experienced BCR (96% of men without BCR had at least one year of follow-up). An increased hazard ratio (HR) for BCR was found in men with a higher ratio of GDF15 expression in their tumor vs. tumor-adjacent benign tissue (HR: 3.74; 95% confidence interval (CI) = 1.27-10.99) Adjusting for tumor grade, pathological tumor stage and PSA at diagnosis did not alter risk estimates significantly. In these same prostate tumor specimens, increased hazard ratios for BCR were found among men who had elevated CD204 expression in tumor (HR = 5.24; 95% CI = 2.02-13.62) and in tumor-adjacent benign tissue (HR = 3.29; 95% CI = 1.32, 8.23). We found no association of BCR-free survival with either GDF15 or CD204 expression in pre-diagnostic benign biopsies. Our results suggest that men who have a larger difference in GDF15 expression levels between prostate tumor and tumor-adjacent benign tissue, and with increased levels of M2 macrophages in both tumor and tumor-adjacent benign tissue, are at greater risk of disease recurrence. Further evaluation of the differences in the prostate immune cellular profile in the pre-malignant and malignant state may offer additional insight into inflammatory-mediated prostate carcinogenesis. Citation Format: Sudha M. Sadasivan, Yalei Chen, Nilesh S. Gupta, Kanika Taneja, Scott A. Maresh, Alysa Gonzalez, Xiaoxia Han, Kuan-Han Hank Wu, Dhananjay Chitale, Andrew Rundle, Deliang Tang, Benjamin A. Rybicki. The role of GDF15 (growth/differentiation factor 15) during prostate carcinogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4211.

BSFCoS: Block and Sparse Principal Component Analysis-Based Fast Co-Saliency Detection Method

Co-saliency detection, an emerging research area in saliency detection, aims to extract the common saliency from the multi images. The extracted co-saliency map has been utilized in various applications, such as in co-segmentation, co-recognition and so on. With the rapid development of image acquisition technology, the original digital images are becoming more and more clearly. The existing co-saliency detection methods processing these images need enormous computer memory along with high computational complexity. These limitations made it hard to satisfy the demand of real-time user interaction. This paper proposes a fast co-saliency detection method based on the image block partition and sparse feature extraction method (BSFCoS). Firstly, the images are divided into several uniform blocks, and the low-level features are extracted from Lab and RGB color spaces. In order to maintain the characteristics of the original images and reduce the number of feature points as well as possible, Truncated Power for sparse principal components method are employed to extract sparse features. Furthermore, K-Means method is adopted to cluster the extracted sparse features, and calculate the three salient feature weights. Finally, the co-saliency map was acquired from the feature fusion of the saliency map for single image and multi images. The proposed method has been tested and simulated on two benchmark datasets: Co-saliency Pairs and CMU Cornell iCoseg datasets. Compared with the existing co-saliency methods, BSFCoS has a significant running time improvement in multi images processing while ensuring detection results. Lastly, the co-segmentation method based on BSFCoS is also given and has a better co-segmentation performance.

Robust Multi-image Processing with Optimal Sparse Regularization

Sparse modeling can be used to characterize outlier type noise. Thanks to sparse recovery theory, it was shown that 1-norm super-resolution is robust to outliers if enough images are captured. Moreover, sparse modeling of signals is a way to overcome ill-posedness of under-determined problems. This naturally leads to this question: does an added sparsity assumption on the signal improve the robustness to outliers of the 1-norm super-resolution, and if yes, how strong should this assumption be? In this article, we review and extend results of the literature to the robustness to outliers of overdetermined signal recovery problems under sparse regularization, with a convex variational formulation. We then apply them to general random matrices, and show how the regularization parameter acts on the robustness to outliers. Finally, we show that in the case of multi-image processing, the structure of the support of signal and noise must be studied precisely. We show that the sparsity assumption improves robustness if outliers do not overlap with signal jumps, and determine how the regularization parameter can be chosen.

근접수치사진측량을 위한 스마트폰 카메라 검보정

Recently studies on application development and utilization using sensors and devices embedded in smartphones have flourished at home and abroad. This study aimed to analyze the accuracy of the images of smartphone to determine three-dimension position of close objects prior to the development of photogrammetric system applying smartphone and evaluate the feasibility to use. First of all, camera calibration was conducted on autofocus and infinite focus. Regarding camera calibration distortion model with balance system and unbalance system was used for the decision of lens distortion coefficient, the results of calibration on 16 types of projects showed that all cases were in RMS error by less than 1 mm from bundle adjustment. Also in terms of autofocus and infinite focus on S and S2 model, the pattern of distorted curve was almost the same, so it could be judged that change in distortion pattern according to focus mode is very little. The result comparison according to autofocus and infinite focus and the result comparison according to a software used for multi-image processing showed that all cases were in standard deviation less than ±3 mm. It is judged that there is little result difference between focus mode and determination of three-dimension position by distortion model. Lastly the checkpoint performance by total station was fixed as most probable value and the checkpoint performance determined by each project was fixed as observed value to calculate statistics on residual of individual methods. The result showed that all projects had relatively large errors in the direction of Y, the direction of object distance compared to the direction of X and Z. Like above, in terms of accuracy for determination of three-dimension position for a close object, the feasibility to use smartphone camera would be enough.

PROCESSING OF UAV BASED RANGE IMAGING DATA TO GENERATE DETAILED ELEVATION MODELS OF COMPLEX NATURAL STRUCTURES

Abstract. Unmanned Aerial Vehicles (UAVs) are more and more used in civil areas like geomatics. Autonomous navigated platforms have a great flexibility in flying and manoeuvring in complex environments to collect remote sensing data. In contrast to standard technologies such as aerial manned platforms (airplanes and helicopters) UAVs are able to fly closer to the object and in small-scale areas of high-risk situations such as landslides, volcano and earthquake areas and floodplains. Thus, UAVs are sometimes the only practical alternative in areas where access is difficult and where no manned aircraft is available or even no flight permission is given. Furthermore, compared to terrestrial platforms, UAVs are not limited to specific view directions and could overcome occlusions from trees, houses and terrain structures. Equipped with image sensors and/or laser scanners they are able to provide elevation models, rectified images, textured 3D-models and maps. In this paper we will describe a UAV platform, which can carry a range imaging (RIM) camera including power supply and data storage for the detailed mapping and monitoring of complex structures, such as alpine riverbed areas. The UAV platform NEO from Swiss UAV was equipped with the RIM camera CamCube 2.0 by PMD Technologies GmbH to capture the surface structures. Its navigation system includes an autopilot. To validate the UAV-trajectory a 360° prism was installed and tracked by a total station. Within the paper a workflow for the processing of UAV-RIM data is proposed, which is based on the processing of differential GNSS data in combination with the acquired range images. Subsequently, the obtained results for the trajectory are compared and verified with a track of a UAV (Falcon 8, Ascending Technologies) carried out with a total station simultaneously to the GNSS data acquisition. The results showed that the UAV's position using differential GNSS could be determined in the centimetre to the decimetre level. The RIM data indicated a high noise level in the measured distance image, due to the vibrations caused by the flight system. Multi-image processing reduced the noise level of the distance image. The produced elevation models from a test area show the high potential of the proposed method for complex structures such as riverbeds.

ISP: An Optimal Out-of-Core Image-Set Processing Streaming Architecture for Parallel Heterogeneous Systems

Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.

Short range tracking of rainy clouds by multi-image flow processing of X-band radar data

Two innovative algorithms for motion tracking and monitoring of rainy clouds from radar images are proposed. The methods are generalizations of classical optical flow techniques, including a production term (modelling formation, growth or depletion of clouds) in the model to be fit to the data. Multiple images are processed and different smoothness constraints are introduced. When applied to simulated maps (including additive noise up to 10 dB of SNR) showing formation and propagation of objects with different directions and velocities, the algorithms identified correctly the production and the flow, and were stable to noise when the number of images was sufficiently high (about 10). The average error was about 0.06 pixels (px) per sampling interval (ΔT) in identifying the modulus of the flow (velocities between 0.25 and 2 px/ΔT were simulated) and about 1° in detecting its direction (varying between 0° and 90°). An example of application to X-band radar rainfall rate images detected during a stratiform rainfall is shown. Different directions of the flow were detected when investigating short (10 min) or long time ranges (8 h), in line with the chaotic behaviour of the weather condition. The algorithms can be applied to investigate the local stability of meteorological conditions with potential future applications in nowcasting.

Fast image mapping of endoscopic image mosaics with three-dimensional ultrasound image for intrauterine fetal surgery

This paper describes a fast image mapping system that integrates endoscopic image mosaics with three-dimensional (3-D) ultrasound images for assisting intrauterine laser photocoagulation treatment. Endoscopic laser photocoagulation treatment has a good survival rate and a low complication rate for twins. However, the small field of view and lack of surrounding information makes the identification of vessel anastomosis difficult. We have developed an extended visualization system with the fusion of endoscopic image mosaics with a 3-D ultrasound-image model. Fully automatic and fast calibration is used for endoscope calibration in fluid. The 3-D spatial position of the endoscopic images and the ultrasound image are tracked by a 3-D position tracking device. The mosaiced endoscope images are registered to the surface of the 3-D ultrasound placenta model by using a fast GPU-based image rendering method and a seamless multi-images processing. Experimental results show that the system may provide an improved and efficient way of planning and guidance in laser photocoagulation treatment.

PC control and data processing in relation with a standalone laser beam analyzer

In this note we present a PC-based remote control and data processing setup and the associated software, developed in LabVIEW, working in conjunction with a laser beam analyzer. The setup expands the imaging processing techniques for the laser beam profile, especially in relation to the three-dimensional data representation and manipulation, by using the strong graphical processing and presentation libraries of the HIQ and IMAQ Vision software packages. One novelty of the proposed program consists in its multi-image processing ability with regard to images acquired under different conditions, and which have to be processed aligned to their centroids and their major axes.

Cloud detection from a sequence of SST images

A cloud detection algorithm was designed as an adjunct to a companion edge-detection algorithm. The cloud detection integrates two distinct algorithms: one based on multiimage processing, the other on single-image analysis. The multiimage portion of the cloud detection algorithm operates on a time sequence of sea surface temperature (SST) images. It is designed to detect clouds associated with regions of apparently lower temperatures than the underlying SST field. A pixel in the current image is initially considered to be corrupted by clouds if it is significantly cooler than the corresponding pixel in a neighbor image. To refine the initial classification, the algorithm checks the current image and the neighbor image for the presence of water masses, which through displacement could explain the change in temperature. The single-image cloud detection algorithm is designed to detect clouds associated with regions of the SST image where gradient vectors have a large magnitude. These regions are flagged in the map of potential clouds. multiimage processing is integrated with the single-image algorithm by adding pixels classified as cloudy at the multiimage level to the map of potential clouds. Further analysis of the gradient vector field and of the shapes of potentially cloudy areas allows one to determine whether these regions correspond to clouds or SST fronts. A previous study has shown that the clouds identified by the single-image algorithm were in close agreement with those detected by a human expert. To validate the additional multiimage processing, the effect of the integrated cloud detection on the performance of a companion edge detection algorithm is examined. These results and a direct comparison with the cloud masks produced by a human expert indicate that, compared to the single-image algorithm, the multiimage algorithm successfully identify additional cloud-corrupted regions while keeping a low rate for the detection of false clouds.

Three-dimensional measurement of foot shape using the method of multi-images processing

Three-dimensional measurement of foot shape using the method of multi-images processing