Fast, non-destructive and on-site quality control tools, mainly high sensitive imaging techniques, are important to assess the reliability of photovoltaic plants. To minimize the risk of further damages and electrical yield losses, electroluminescence (EL) imaging is used to detect local defects in an early stage, which might cause future electric losses. For an automated defect recognition on EL measurements, a robust detection and rectification of modules, as well as an optional segmentation into cells is required. This paper introduces a method to detect solar modules and crossing points between solar cells in EL images. We only require 1-D image statistics for the detection, resulting in an approach that is computationally efficient. In addition, the method is able to detect the modules under perspective distortion and in scenarios, where multiple modules are visible in the image. We compare our method to the state of the art and show that it is superior in presence of perspective distortion while the performance on images, where the module is roughly coplanar to the detector, is similar to the reference method. Finally, we show that we greatly improve in terms of computational time in comparison to the reference method.
There are not many preliminary works on the automated detection of solar modules. al Vetter @cite_3 proposed an object detection pipeline that consists of several stacked filters followed by a Hough transform to detect solar modules in noisy infrared thermography measurements. Recently, al Deitsch @cite_1 proposed a processing pipeline for solar modules that jointly detects the modules in an EL image, estimates the configuration ( the number of rows and columns of cells), estimates the lens distortion and performs segmentation into rectified cell images. Their approach consists of a preprocessing step, where a multiscale vesselness filter @cite_8 is used to extract ridges (separating lines between cells) and bus bars. Then, parabolic curves are fitted onto the result to obtain a parametric model of the module. Finally, the distortion is estimated and module corners are extracted. Since this is, to the best of our knowledge, the only method that automatically detects solar modules and cell crossing points in EL images, we use this as a reference method to assess the performance of our approach.
@cite_1 @cite_3 @cite_8
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High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kind of defects, which is time-consuming and expensive. In this work, we make an important step towards improving the current state-of-the-art in solar module inspection. We propose a robust automated segmentation method to extract individual solar cells from EL images of PV modules. Automated segmentation of cells is a key step in automating the visual inspection workflow. It also enables controlled studies on large amounts of data to understanding the effects of module degradation over time - a process not yet fully understood. The proposed method infers in several steps a high level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 96.09 and an @math score of 97.23 . Abstract Local electric defects may result in considerable performance losses in solar cells. Infrared thermography is an essential tool to detect these defects on photovoltaic modules. Accordingly, IR-thermography is frequently used in R&D labs of PV manufactures and, furthermore, outdoors in order to identify faulty modules in PV-power plants. Massive amount of data is acquired which needs to be analyzed. An automatized method for detecting solar modules in IR-images would enable a faster and automatized analysis of the data. However, IR-images tend to suffer from rather large noise, which makes an automatized segmentation challenging. The aim of this study was to establish a reliable segmentation algorithm for R&D labs. We propose an algorithm, which detects a solar cell or module within an IR-image with large noise. We tested the algorithm on images of 10 PV-samples characterized by highly sensitive dark lock-in thermography (DLIT). The algorithm proved to be very reliable in detecting correctly the solar module. In our study, we focused on thin film solar cells, however, a transfer of the algorithm to other cell types is straight forward. Abstract We present a novel convolutional neural network (CNN) based pipeline which can effectively fuse multi-level information extracted from different intermediate layers generating hybrid convolutional features (HCF) for edge detection. Different from previous methods, the proposed method fuses multi-level information in a feature-map based manner. The produced hybrid convolutional features can be used to perform high-quality edge detection. The edge detector is also computationally efficient, because it detects edges in an image-to-image way without any post-processing. We evaluate the proposed method on three widely used datasets for edge detection including BSDS500, NYUD and Multicue, and also test the method on Pascal VOC’12 dataset for object contour detection. The results show that HCF achieves an improvement in performance over the state-of-the-art methods on all four datasets. On BSDS500 dataset, the efficient version of the proposed approach achieves ODS F-score of 0.804 with a speed of 22 fps and the high-accuracy version achieves ODS F-score of 0.814 with 11 fps . Abstract This paper pertains to the detection of objects located in complex backgrounds. A feature-based segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: a given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy (“Gabor features”) and their spatial locations are inputted to a squared-error clustering algorithm. This clustering algorithm yields a segmentation of the original image—it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across different spatial orientations and frequencies. The method is applied to a number of visual and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of “Gabor features” is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in IVHS, military reconnaissance, fingerprint analysis, and image database query. A robust and efficient anomaly detection technique is proposed, capable of dealing with crowded scenes where traditional tracking based approaches tend to fail. Initial foreground segmentation of the input frames confines the analysis to foreground objects and effectively ignores irrelevant background dynamics. Input frames are split into non-overlapping cells, followed by extracting features based on motion, size and texture from each cell. Each feature type is independently analysed for the presence of an anomaly. Unlike most methods, a refined estimate of object motion is achieved by computing the optical flow of only the foreground pixels. The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets. Texture features are modelled by an adaptively grown code-book, with the number of entries in the codebook selected in an online fashion. Experiments on the recently published UCSD Anomaly Detection dataset show that the proposed method obtains considerably better results than three recent approaches: MPPCA, social force, and mixture of dynamic textures (MDT). The proposed method is also several orders of magnitude faster than MDT, the next best performing method. Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of “green” apples within scenes of “green leaves”, shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74 recognition accuracy) over the high-level visual feature approach (53.16 recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased. We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets. This paper addresses the task of efficient object class detection by means of the Hough transform. This approach has been made popular by the Implicit Shape Model (ISM) and has been adopted many times. Although ISM exhibits robust detection performance, its probabilistic formulation is unsatisfactory. The PRincipled Implicit Shape Model (PRISM) overcomes these problems by interpreting Hough voting as a dual implementation of linear sliding-window detection. It thereby gives a sound justification to the voting procedure and imposes minimal constraints. We demonstrate PRISM's flexibility by two complementary implementations: a generatively trained Gaussian Mixture Model as well as a discriminatively trained histogram approach. Both systems achieve state-of-the-art performance. Detections are found by gradient-based or branch and bound search, respectively. The latter greatly benefits from PRISM's feature-centric view. It thereby avoids the unfavourable memory trade-off and any on-line pre-processing of the original Efficient Subwindow Search (ESS). Moreover, our approach takes account of the features' scale value while ESS does not. Finally, we show how to avoid soft-matching and spatial pyramid descriptors during detection without losing their positive effect. This makes algorithms simpler and faster. Both are possible if the object model is properly regularised and we discuss a modification of SVMs which allows for doing so. An efficient optic disk localization and segmentation are important tasks in an automated retinal image analysis system. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents a method to automatically locate and boundary detect of the optic disk. The detection procedure comprises two independent methodologies. On one hand, a location methodology obtains a pixel that belongs to the OD using iterative thresholding method followed by Principal Component Analysis techniques (PCA) and, on the other hand, a boundary segmentation methodology estimates the OD boundary by applying region-based active contour model in a variational level set formulation (RSF). The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity The results from the RSF method were compared with conventional optic disk detection using a geometric active contour models (ACM) and later verified with hand-drawn ground truth. Results indicate 89 accuracy for identification and 95.05 average accuracy in localizing the optic disc boundary. We present a method for the detection of instances of an object class, such as cars or pedestrians, in natural images. Similarly to some previous works, this is accomplished via generalized Hough transform, where the detections of individual object parts cast probabilistic votes for possible locations of the centroid of the whole object; the detection hypotheses then correspond to the maxima of the Hough image that accumulates the votes from all parts. However, whereas the previous methods detect object parts using generative codebooks of part appearances, we take a more discriminative approach to object part detection. Towards this end, we train a class-specific Hough forest, which is a random forest that directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. We demonstrate that Hough forests improve the results of the Hough-transform object detection significantly and achieve state-of-the-art performance for several classes and datasets. This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems. With the deployment of large CCD mosaic cameras and their use in large-scale surveys to discover Solar system objects, there is a need for fast detection algorithms that can handle large data loads in a nearly automatic way. We present here an algorithm that we have developed. Our approach, by using two independent detection algorithms and combining the results, maintains high efficiency while producing low false-detection rates. These properties are crucial in order to reduce the operator time associated with searching these huge data sets. We have used this algorithm on two different mosaic data sets obtained using the CFH12K camera at the Canada–France–Hawaii Telescope (CFHT). Comparing the detection efficiency and false-detection rate of each individual algorithm with the combination of both, we show that our approach decreases the false detection rate by a factor of a few hundred to a thousand, while decreasing the ‘limiting magnitude’ (where the detection rate drops to 50 per cent) by only 0.1–0.3 mag. The limiting magnitude is similar to that of a human operator blinking the images. Our full pipeline also characterizes the magnitude efficiency of the entire system by implanting artificial objects in the data set. The detection portion of the package is publicly available. In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a "parsing graph", in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches--generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns--generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. IEEE Trans. PAMI, 24(5):657--673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions. The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segment’s length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segment’s normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segment’s midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances. This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu's method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load. The continuous development of automatic retinal diseases diagnosis systems based on image processing has shown their potential for clinical practice. However, the accuracy of these systems is often compromised, mainly due to the intrinsic difficulty in detecting the abnormal structures and also due to deficiencies in the image acquisition which affects image quality. Light flares are one of such deficiencies that usually don't compromise the overall image quality, but can be misclassified by an automatic diagnosis system. In this article a method is proposed for detecting light artifacts (flares) on retinal images. The output from the light artifact detection is a binary image mask that is useful to reject those pixels from being further processed. The proposed method uses a template matching algorithm to detect artifacts similar to the predefined template artifact images. Two main types of light artifacts were identified: light flares and the central artifact. To reduce over-segmentation the light artifact candidates are characterized by their shape and color and are classified by a decision tree. The method was developed using a dataset of 61 images from which 20 were used for the classifier training and the remaining 41 for independent testing. With the test dataset the method obtained an average sensitivity false detection per image pairs of 0.97 0.12 for the central artifact and 0.73 0.36 for the light flares, what were considered good results regarding the heterogeneity of the dataset which contain low and high quality images. We optimize automultiscopic displays built by stacking a pair of modified LCD panels. To date, such dual-stacked LCDs have used heuristic parallax barriers for view-dependent imagery: the front LCD shows a fixed array of slits or pinholes, independent of the multi-view content. While prior works adapt the spacing between slits or pinholes, depending on viewer position, we show both layers can also be adapted to the multi-view content, increasing brightness and refresh rate. Unlike conventional barriers, both masks are allowed to exhibit non-binary opacities. It is shown that any 4D light field emitted by a dual-stacked LCD is the tensor product of two 2D masks. Thus, any pair of 1D masks only achieves a rank-1 approximation of a 2D light field. Temporal multiplexing of masks is shown to achieve higher-rank approximations. Non-negative matrix factorization (NMF) minimizes the weighted Euclidean distance between a target light field and that emitted by the display. Simulations and experiments characterize the resulting content-adaptive parallax barriers for low-rank light field approximation. Using the variational approaches to estimate optical flow between two frames, the flow discontinuities between different motion fields are usually not distinguished even when an anisotropic diffusion operator is applied. In this paper, we propose a multi-cue driven adaptive bilateral filter to regularize the flow computation, which is able to achieve the smoothly varied optical flow field with highly desirable motion discontinuities. First, we separate the traditional one-step variational updating model into a two-step filtering-based updating model. Then, employing our occlusion detector, we reformulate the energy functional of optical flow estimation by explicitly introducing an occlusion term to balance the energy loss due to the occlusion or mismatches. Furthermore, based on the two-step updating framework, a novel multi-cue driven bilateral filter is proposed to substitute the original anisotropic diffusion process, and it is able to adaptively control the diffusion process according to the occlusion detection, image intensity dissimilarity, and motion dissimilarity. After applying our approach on various video sources (movie and TV) in the presence of occlusion, motion blurring, non-rigid deformation, and weak textureness, we generate a spatial-coherent flow field between each pair of input frames and detect more accurate flow discontinuities along the motion boundaries. article i nfo The remote detection of water stress in a citrus orchard was investigated using leaf-level measurements of chlorophyll fluorescence and Photochemical Reflectance Index (PRI) data, seasonal time-series of crown tem- perature and PRI, and high-resolution airborne imagery. The work was conducted in an orchard where a reg- ulated deficit irrigation (RDI) experiment generated a gradient in water stress levels. Stomatal conductance (Gs) and water potential (Ψ) were measured over the season on each treatment block. The airborne data consisted on thermal and hyperspectral imagery acquired at the time of maximum stress differences among treatments, prior to the re-watering phase, using a miniaturized thermal camera and a micro-hyperspectral imager on board an unmanned aerial vehicle (UAV). The hyperspectral imagery was acquired at 40 cm resolution and 260 spectral bands in the 400-885 nm spectral range at 6.4 nm full width at half maximum (FWHM) spectral resolution and 1.85 nmsampling interval,enablingthe identificationof pure crownsfor extractingradiance andreflectance hyperspectral spectra from each tree. The FluorMOD model was used to investigate the retrieval of chlorophyll fluorescence by applying the Fraunhofer Line Depth (FLD) principle using three spectral bands (FLD3), which demonstrated that fluorescence retrievalwas feasible with the configuration of the UAV micro-hyperspectral in- strument flown over the orchard. Results demonstrated the link between seasonal PRI and crown temperature acquired from instrumented trees and field measurements of stomatal conductance and water potential. The sensitivity of PRI and Tc-Ta time-series to water stress levels demonstrated a time delay of PRI vs Tc-Ta during the recovery phase after re-watering started. At the time of the maximum stress difference among treatment blocks, the airborneimagery acquired fromthe UAV platform demonstrated that the crown temperature yielded the best coefficient of determination for Gs (r 2 Efficient and robust segmentation of less intrusively or non-cooperatively captured iris images is still a challenging task in iris biometrics. This paper proposes a novel two-stage algorithm for the localization and mapping of iris texture in images of the human eye into Daugman's doubly dimensionless polar coordinates. Motivated by the growing demand for real-time capable solutions, coarse center detection and fine boundary localization usually combined in traditional approaches are decoupled. Therefore, search space at each stage is reduced without having to stick to simpler models. Another motivation of this work is independence of sensors. A comparison of reference software on different datasets highlights the problem of database-specific optimizations in existing solutions. This paper instead proposes the application of Gaussian weighting functions to incorporate model-specific prior knowledge. An adaptive Hough transform is applied at multiple resolutions to estimate the approximate position of the iris center. Subsequent polar transform detects the first elliptic limbic or pupillary boundary, and an ellipsopolar transform finds the second boundary based on the outcome of the first. This way, both iris images with clear limbic (typical for visible-wavelength) and with clear pupillary boundaries (typical for near infrared) can be processed in a uniform manner.