314 research outputs found
Intensity-based image registration using multiple distributed agents
Image registration is the process of geometrically aligning images taken from different sensors, viewpoints or instances in time. It plays a key role in the detection of defects or anomalies for automated visual inspection. A multiagent distributed blackboard system has been developed for intensity-based image registration. The images are divided into segments and allocated to agents on separate processors, allowing parallel computation of a similarity metric that measures the degree of likeness between reference and sensed images after the application of a transform. The need for a dedicated control module is removed by coordination of agents via the blackboard. Tests show that additional agents increase speed, provided the communication capacity of the blackboard is not saturated. The success of the approach in achieving registration, despite significant misalignment of the original images, is demonstrated in the detection of manufacturing defects on screen-printed plastic bottles and printed circuit boards
Real-Time Wheel Detection and Rim Classification in Automotive Production
This paper proposes a novel approach to real-time automatic rim detection,
classification, and inspection by combining traditional computer vision and
deep learning techniques. At the end of every automotive assembly line, a
quality control process is carried out to identify any potential defects in the
produced cars. Common yet hazardous defects are related, for example, to
incorrectly mounted rims. Routine inspections are mostly conducted by human
workers that are negatively affected by factors such as fatigue or distraction.
We have designed a new prototype to validate whether all four wheels on a
single car match in size and type. Additionally, we present three comprehensive
open-source databases, CWD1500, WHEEL22, and RB600, for wheel, rim, and bolt
detection, as well as rim classification, which are free-to-use for scientific
purposes.Comment: 5 pages, 7 figures, 3 table
3D functional models of monkey brain through elastic registration of histological sections
In this paper we describe a method for the reconstruction and visualization of functional models of monkey brains. Models are built through the registration of high resolution images obtained from the scanning of histological sections with reference photos taken during the brain slicing. From the histological sections it is also possible to acquire specifically activated neuron coordinates introducing functional information in the model. Due to the specific nature of the images (texture information is useless and the sections could be deformed when they were cut and placed on glass) we solved the registration problem by extracting corresponding cerebral cortex borders (extracted with a snake algorithm), and computing from their deformation an image transform modeled as an affine deformation plus a non-linear field evaluated as an elastically constrained deformation minimizing contour distances. Registered images and contours are used then to build 3D models of specific brains by a software tool allowing the interactive visualization of cortical volumes together with the spatially referenced neurons classified and differently colored according to their functionalities
Robust Kernel-Based Tracking with Multiple Subtemplates in Vision Guidance System
The mean shift algorithm has achieved considerable success in target tracking due to its simplicity and robustness. However, the lack of spatial information may result in its failure to get high tracking precision. This might be even worse when the target is scale variant and the sequences are gray-levels. This paper presents a novel multiple subtemplates based tracking algorithm for the terminal guidance application. By applying a separate tracker to each subtemplate, it can handle more complicated situations such as rotation, scaling, and partial coverage of the target. The innovations include: (1) an optimal subtemplates selection algorithm is designed, which ensures that the selected subtemplates maximally represent the information of the entire template while having the least mutual redundancy; (2) based on the serial tracking results and the spatial constraint prior to those subtemplates, a Gaussian weighted voting method is proposed to locate the target center; (3) the optimal scale factor is determined by maximizing the voting results among the scale searching layers, which avoids the complicated threshold setting problem. Experiments on some videos with static scenes show that the proposed method greatly improves the tracking accuracy compared to the original mean shift algorithm
Fast Mesh-Based Medical Image Registration
In this paper a fast triangular mesh based registration method is proposed.
Having Template and Reference images as inputs, the template image is
triangulated using a content adaptive mesh generation algorithm. Considering
the pixel values at mesh nodes, interpolated using spline interpolation method
for both of the images, the energy functional needed for image registration is
minimized. The minimization process was achieved using a mesh based
discretization of the distance measure and regularization term which resulted
in a sparse system of linear equations, which due to the smaller size in
comparison to the pixel-wise registration method, can be solved directly. Mean
Squared Difference (MSD) is used as a metric for evaluating the results. Using
the mesh based technique, higher speed was achieved compared to pixel-based
curvature registration technique with fast DCT solver. The implementation was
done in MATLAB without any specific optimization. Higher speeds can be achieved
using C/C++ implementations.Comment: Accepted manuscript for ISVC'201
Visible and Infrared Image Registration Employing Line-Based Geometric Analysis
Abstract. We present a new method to register a pair of visible (ViS) and infrared (IR) images. Unlike most of existing systems that align interest points of two images, we align lines derived from edge pixels, because the interest points extracted from both images are not always identical, but most major edges detected from one image do appear in another image. To solve feature matching problem, we emphasize the geometric structure alignment of features (lines), instead of descriptor-based individual feature matching. This is due to the fact that image properties and patch statistics of corresponding features might be quite different, especially when one compares ViS image with long wave IR images (thermal information). However, the spatial layout of features for both images always preserves consistency. The last step of our algorithm is to compute the image transform matrix, given minimum 4 pairs of line correspondence. The comparative evaluation for algorithms demon-strates higher accuracy attained by our method when compared to the state-of-the-art approaches.
The application of big data and AI in the upstream supply chain
The use of Big Data has grown in popularity in organisations to exploit the purpose of their primary data to enhance their competitiveness. In conjunction with the increased use of Big Data, there has also been a growth in the use of Artificial Intelligence (AI) to analyse the vast amounts of data generated and provide a mechanism for locating and constructing useable patterns that organisations can incorporate in their supply chain strategy programme. As these organisations embrace the use of technology and embed this in their supply chain strategy, there are questions as to how this may affect their upstream supply chains especially with regards to how SME’s may be able to cope with the potential changes. There exists the opportunity to conduct further research into this area, mainly focusing on three key industry sectors of aerospace, rail and automotive supply chains.N/
Relation of chlorophyll fluorescence sensitive reflectance ratios to carbon flux measurements of Montanne grassland and Norway spruce forest ecosystems in the temperate zone
We explored ability of reflectance vegetation indexes (VIs) related to chlorophyll fluorescence emission (R686/R630, R 740/R800) and de-epoxidation state of xanthophyll cycle pigments (PRI, calculated as (R531 - R570) (R 531 - R570)) to track changes in the CO2 assimilation rate and Light Use Efficiency (LUE) in montane grassland and Norway spruce forest ecosystems, both at leaf and also canopy level. VIs were measured at two research plots using a ground-based high spatial/spectral resolution imaging spectroscopy technique. No significant relationship between VIs and leaf light-saturated CO2 assimilation (AMAX) was detected in instantaneous measurements of grassland under steady-state irradiance conditions. Once the temporal dimension and daily irradiance variation were included into the experimental setup, statistically significant changes in VIs related to tested physiological parameters were revealed. ΔPRI and Δ(R686 R630) of grassland plant leaves under dark-to-full sunlight transition in the scale of minutes were significantly related to AMAX (R2 = 0.51). In the daily course, the variation of VIs measured in one-hour intervals correlated well with the variation of Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and LUE estimated via the eddy-covariance flux tower. Statistical results were weaker in the case of the grassland ecosystem, with the strongest statistical relation of the index R686 R630 with NEE and GPP
Color image registration under illumination changes
The estimation of parametric global motion has had a significant attention
during the last two decades, but despite the great efforts invested, there
are still open issues. One of the most important ones is related to the ability to recover
large deformation between images in the presence of illumination changes
while kipping accurate estimates. Illumination changes in color images are another
important open issue. In this paper, a Generalized least squared-based motion
estimator is used in combination with color image model to allow accurate
estimates of global motion between two color images under the presence of large
geometric transformation and illumination changes. Experiments using challenging
images have been performed showing that the presented technique is feasible
and provides accurate estimates of the motion and illumination parameter
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