Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic resonance imaging (MRI) structural sequences, are important imaging markers of cerebral small vessel disease and potential indicators of neurodegenerative conditions. Despite their clinical significance, automatic enlarged PVS (EPVS) segmentation remains challenging due to their small size, variable morphology, similarity with other pathological features, and limited annotated datasets. This paper presents the EPVS Challenge organized at MICCAI 2024, which aims to advance the development of automated algorithms for EPVS segmentation across multi-site data. We provided a diverse dataset comprising 100 training, 50 validation, and 50 testing scans collected from multiple international sites (UK, Singapore, and China) with varying MRI protocols and demographics. All annotations followed the STRIVE protocol to ensure standardized ground truth and covered the full brain parenchyma. Seven teams completed the full challenge, implementing various deep learning approaches primarily based on U-Net architectures with innovations in multi-modal processing, ensemble strategies, and transformer-based components. Performance was evaluated using dice similarity coefficient, absolute volume difference, recall, and precision metrics. The winning method employed MedNeXt architecture with a dual 2D/3D strategy for handling varying slice thicknesses. The top solutions showed relatively good performance on test data from seen datasets, but significant degradation of performance was observed on the previously unseen Shanghai cohort, highlighting cross-site generalization challenges due to domain shift. This challenge establishes an important benchmark for EPVS segmentation methods and underscores the need for the continued development of robust algorithms that can generalize in diverse clinical settings.

Accurate fetal brain tissue segmentation and biometric measurement are essential for monitoring neurodevelopment and detecting abnormalities in utero. The Fetal Tissue Annotation (FeTA) Challenges have established robust multi-center benchmarks for evaluating state-of-the-art segmentation methods. This paper presents the results of the 2024 challenge edition, which introduced three key innovations. First, we introduced a topology-aware metric based on the Euler characteristic difference (ED) to overcome the performance plateau observed with traditional metrics like Dice or Hausdorff distance (HD), as the performance of the best models in segmentation surpassed the inter-rater variability. While the best teams reached similar scores in Dice (0.81-0.82) and HD95 (2.1-2.3 mm), ED provided greater discriminative power: the winning method achieved an ED of 20.9, representing roughly a 50% improvement over the second- and third-ranked teams despite comparable Dice scores. Second, we introduced a new 0.55T low-field MRI test set, which, when paired with high-quality super-resolution reconstruction, achieved the highest segmentation performance across all test cohorts (Dice=0.86, HD95=1.69, ED=6.26). This provides the first quantitative evidence that low-cost, low-field MRI can match or surpass high-field systems in automated fetal brain segmentation. Third, the new biometry estimation task exposed a clear performance gap: although the best model reached a mean average percentage error (MAPE) of 7.72%, most submissions failed to outperform a simple gestational-age-based linear regression model (MAPE=9.56%), and all remained above inter-rater variability with a MAPE of 5.38%. Finally, by analyzing the top-performing models from FeTA 2024 alongside those from previous challenge editions, we identify ensembles of 3D nnU-Net trained on both real and synthetic data with both image- and anatomy-level augmentations as the most effective approaches for fetal brain segmentation. Our quantitative analysis reveals that acquisition site, super-resolution strategy, and image quality are the primary sources of domain shift, informing recommendations to enhance the robustness and generalizability of automated fetal brain analysis methods.

Large Vessel Occlusion is one of the most critical neurological emergencies in stroke care, requiring rapid and accurate diagnosis to optimize clinical outcomes. Automated detection tools have demonstrated the potential to significantly reduce treatment time, thereby improving patient prognosis. In this paper, we propose a novel two-stage deep learning approach for detecting large vessel occlusion and assessing its volume directly from computed tomography angiography. The first stage employs a two-dimensional convolutional neural network-based detector, built upon GravityNet, specifically adapted for single lesion detection with a novel pixel-based configuration. The second stage applies a three-dimensional false-positive reduction technique to refine predictions within the brain volume. Our method achieves 80% sensitivity at two false positives per case, demonstrating its robustness and effectiveness in detecting large vessel occlusions on computed tomography angiography.

Abstract Motion cue is pivotal in moving object analysis, which is the root for motion segmentation and detection. These preprocessing tasks are building blocks for several applications such as recognition, matching and estimation. To devise a robust algorithm for motion analysis, it is imperative to have a comprehensive dataset to evaluate an algorithm’s performance. The main limitation in making these kind of datasets is the creation of ground-truth annotation of motion, as each moving object might span over multiple frames with changes in size, illumination and angle of view. Besides the optical changes, the object can undergo occlusion by static or moving occluders. The challenge increases when the video is captured by a moving camera. In this paper, we tackle the task of providing ground-truth annotation on motion regions in videos captured from a moving camera. With minimal manual annotation of an object mask, we are able to propagate the label mask in all the frames. Object label correction based on static and moving occluder is also performed by applying occluder mask tracking for a given depth ordering. A motion annotation dataset is also proposed to evaluate algorithm performance. The results show that our cascaded-naive approach provides successful results. All the resources of the annotation tool are publicly available at http://dixie.udg.edu/anntool/ .

Breast magnetic resonance imaging (MRI) and X-ray mammography are two image modalities widely used for early detection and diagnosis of breast diseases in women. The combination of these modalities, traditionally done using intensity-based registration algorithms, leads to a more accurate diagnosis and treatment, due to the capability of co-localizing lesions and susceptibles areas between the two image modalities. In this work, we present the first attempt to register breast MRI and X-ray mammographic images using intensity gradients as the similarity measure. Specifically, a patient-specific biomechanical model of the breast, extracted from the MRI image, is used to mimic the mammographic acquisition. The intensity gradients of the glandular tissue are directly projected from the 3D MRI volume to the 2D mammographic space, and two different gradient-based metrics are tested to lead the registration, the normalized cross-correlation of the scalar gradient values and the gradient correlation of the vectoral gradients. We compare these two approaches to an intensity-based algorithm, where the MRI volume is transformed to a synthetic computed tomography (pseudo-CT) image using the partial volume effect obtained by the glandular tissue segmentation performed by means of an Expectation-Maximization algorithm. This allows us to obtain the digitally reconstructed radiographies by a direct intensity projection. The best results are obtained using the scalar gradient approach along with a transversal isotropic material model, obtaining a target registration error (TRE), in millimeters, of 5.65 ± 2.76 for CC- and of 7.83 ± 3.04 for MLO-mammograms, while the TRE is 7.33 ± 3.62 in the 3D MRI. We also evaluate the effect of the glandularity of the breast as well as the landmark position on the TRE, obtaining moderated correlation values (0.65 and 0.77 respectively), concluding that these aspects need to be considered to increase the accuracy in further approaches.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighting the need for robust automated segmentation methods. In this paper, we present a novel convolutional neural network based approach for accurate segmentation of the sub-cortical brain structures that combines both convolutional and prior spatial features for improving the segmentation accuracy. In order to increase the accuracy of the automated segmentation, we propose to train the network using a restricted sample selection to force the network to learn the most difficult parts of the structures. We evaluate the accuracy of the proposed method on the public MICCAI 2012 challenge and IBSR 18 datasets, comparing it with different traditional and deep learning state-of-the-art methods. On the MICCAI 2012 dataset, our method shows an excellent performance comparable to the best participant strategy on the challenge, while performing significantly better than state-of-the-art techniques such as FreeSurfer and FIRST. On the IBSR 18 dataset, our method also exhibits a significant increase in the performance with respect to not only FreeSurfer and FIRST, but also comparable or better results than other recent deep learning approaches. Moreover, our experiments show that both the addition of the spatial priors and the restricted sampling strategy have a significant effect on the accuracy of the proposed method. In order to encourage the reproducibility and the use of the proposed method, a public version of our approach is available to download for the neuroimaging community.

In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare our results with the local breast density (i.e., density map) obtained from the complementary full-field digital mammogram. To achieve this goal, we have developed a fully automatic framework, which registers MRI volumes to X-ray mammograms using a subject-specific biomechanical model of the breast. The optimization step modifies the position, orientation, and elastic parameters of the breast model to perform the alignment between the images. When the model reaches an optimal solution, the MRI glandular tissue is projected and compared with the one obtained from the corresponding mammograms. To reduce the loss of information during the ray-casting, we introduce a new approach that avoids resampling the MRI volume. In the results, we focus our efforts on evaluating the agreement of the distributions of glandular tissue, the degree of structural similarity, and the correlation between the real and synthetic density maps. Our approach obtained a high-structural agreement regardless the glandularity of the breast, whilst the similarity of the glandular tissue distributions and correlation between both images increase in denser breasts. Furthermore, the synthetic images show continuity with respect to large structures in the density maps.

The segmentation of the breast from the background and the pectoral muscle is the first pre-processing step in computerised mammographic analysis. This problem is usually solved by dividing it into two different segmentation strategies, one for the background and another one for the pectoral muscle. In this paper we tackle this problem jointly using a supervised single strategy. Namely, from a set of manually segmented mammograms, we model each of the three regions (breast, pectoral muscle, and background) using position, intensity, and texture information. Although the approach requires a training step, it allows a fast and reliable segmentation of new mammograms. The obtained results using 149 mammograms of the MIAS database show a high degree of overlap between manual and automatic segmentation.

Astronomical images are characterized by having a high component of noise, a non-homogeneous background, and a great number of sources (objects) difficult to identify even by experts. All these factors are especially remarkable in high wavelength images such as infrared and radio. Hence, great efforts have been done to solve the automatic detection of sources in this type of images. In this paper, we propose a new approach based on multiscale decomposition and the recently developed Distilled Sensing method. Their combined use allows the minimization of the complex background effects as well as the highlighting of the sources. The experimental results obtained using public infrared and radio images demonstrate the validity of the approach, detecting a greater number of true sources than the original Distilled Sensing and the well-known SExtractor algorithm.

A hybrid framework of probabilistic atlas and statistical shape and appearance model (SSAM) is proposed to achieve 3D prostate segmentation. An initial 3D segmentation of the prostate is obtained by registering the probabilistic atlas to the test dataset with deformable Demons registration. The initial results obtained are used to initialize multiple SSAMs corresponding to the apex, central and base regions of the prostate gland to incorporate local variabilities. Multiple mean parametric models of shape and appearance are derived from principal component analysis of prior shape and intensity information of the prostate from the training data. The parameters are then modified with the prior knowledge of the optimization space to achieve 2D segmentation. The 2D labels are registered to the 3D labels generated using probabilistic atlas to constrain the pose variation and generate valid 3D shapes. The proposed method achieves a mean Dice similarity coefficient value of 0.89±0.11 and mean Hausdorff distance of 3.05±2.25 mm when validated with 15 prostate volumes of a public dataset in a leave-one-out validation framework.

This paper presents a novel method to identify the 2D axial Magnetic Resonance (MR) slice from a pre-acquired MR prostate volume that closely corresponds to the 2D axial Transrectal Ultrasound (TRUS) slice obtained during prostate biopsy. The shape-context representations of the segmented prostate contours in both the imaging modalities are used to establish point correspondences using Bhattacharyya distance. Thereafter, Chi-square distance is used to find the prostate shape similarities between the MR slices and the TRUS slice. Normalized mutual information and correlation coefficient between the TRUS and MR slices are computed to find the information theoretic similarities between the TRUS-MR slices. The maximum of the weighted likelihood function of the afore-mentioned statistical similarity measures finally yields the MR slice that closely resembles the TRUS slice acquired during the biopsy procedure. The method is evaluated for 20 patient datasets and close matches with the ground truth are obtained for 16 cases.

Having large databases of annotated images is important for many applications in computer vision and computer graphics. Some of the largest databases of annotated images rely on user participation (as in Flickr, LabelMe or Peekaboom). In this paper we address the problem of performing semiautomatic object labeling as a way of providing new annotated images. Some current efforts in this direction provide bounding boxes as the annotations (i.e. OPTIMOL and Seville Systems). However, in this work we present an approach that relies on a boosting process to automatically create polygonal annotations for objects similar to those entered by users in tools such as LabelMe. In particular, we train single class boosting classifiers using local image features to perform the simultaneous object detection and segmentation. We validate our approach using different object classes from the LabelMe, the TUD and the Weizmann databases. Moreover, our experiments show that we are able to correctly annotate new data returned by internet search engines.

Prostate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentation. The proposed method achieves a mean Dice similarity coefficient value of 0.96±0.01, and a mean absolute distance value of 0.80±0.24 mm when validated with 24 images from 6 datasets in a leave-one-patient-out validation framework. Our proposed model is automatic, and performs accurate prostate segmentation in presence of intensity heterogeneity and imaging artifacts.

The automatic segmentation of extended real structures (such as SNRs, HII regions, bow shocks, etc) in large surveys is a difficult task due to their morphological complexity and their wide variety in scale and surface brightness. With the aim of dealing with these issues we propose in this paper an automatic segmentation method based on applying wavelet decomposition in the residual thresholded images. The use of this strategy, instead of a wavelet decomposition on the original image, allows to avoid the artifacts produced by strong sources. Experimental results with radio images demonstrate that the proposed method successfully segments extended structures at different scales and therefore is suitable for further morphological analysis and object recognition processes.

Numerous approaches to object detection and segmentation have been proposed so far. However, these methods are prone to fail in some general situations due to the proper object nature. For instance, classical approaches of object detection and segmentation obtain good results for some specific object classes (i.e. detection of pedestrians or segmentation of cars). However, these methods have troubles when detecting or segmenting object classes with different distinctive characteristics (i.e. cars and horses versus sky and road). In this paper, we propose a general framework to simultaneously perform object detection and segmentation on objects of different nature. Our approach is based on a boosting procedure which automatically decides according to the object properties whether is better to give more weight to the detection or segmentation process to improve both results. We validate our approach using different object classes from La belMe, TUD and Weizmann databases, obtaining competitive detection and segmentation results.

This paper presents a novel approach to simultaneously compute the motion segmentation and the 3D reconstruction of a set of 2D points extracted from an image sequence. Starting from an initial segmentation, our method proposes an iterative procedure that corrects the misclassified points while reconstructing the 3D scene, which is composed of objects that move independently. This optimization procedure is made by considering two well-known principles: firstly, in multi-body Structure from Motion the matrix describing the 3D shape is sparse, secondly, the segmented 2D points must give a valid 3D reconstruction given the rotational metric constraints. Our formulation results in a bilinear optimization where sparsity and metric constraints are enforced at each iteration of the algorithm. The final result is the corrected segmentation, the 3D structure of the moving objects and an orthographic camera matrix for each motion and each frame. Results are shown on synthetic sequences and a preliminary application on real sequences of the Hopkins 155 database is presented.

The detection of architectural distortions and abnormal structures in mammographic images can be based on the analysis of bilateral and temporal cases using image registration. This paper presents a quantitative evaluation of state-of-the art intensity based image registration methods applied to mammographic images. These methods range from a global and rigid transformation to local deformable paradigms using various metrics and multiresolution approaches. The aim of this study is to assess the suitability of these methods for mammographic image analysis. Evaluation using temporal cases based on quantitative analysis and a multiobserver study is presented which gives an indication of the accuracy and robustness of the different algorithms. Although previous studies suggested that local deformable methods were not suitable due to the generation of unrealistic distortions, in this work we show that local deformable paradigms (multiresolution B-Spline deformations) obtain the most accurate registration results.

The detection of architectural distortions and abnormal structures in mammographic images can be based on the analysis of bilateral and temporal cases using image registration. This work presents a quantitative evaluation of eight state-of-the art image registration methods applied to mammographic images. These methods range from a global and rigid transformation to local deformable paradigms using various metrics and multi-resolution approaches. The aim of this study is to assess the suitability of these methods for mammographic image analysis. Evaluation using temporal cases based on quantitative analysis gives an indication of the accuracy and robustness of the different algorithms. This work shows that local deformable paradigms (B-spline deformations) obtain the most accurate registration results.

We present in this paper a supervised approach for automatic detection of micro-calcifications. The system is based on learning the different morphology of the micro-calcifications using local features, which are extracted using a bank of filters. Afterwards, this set of features is used to train a pixel-based boosting classifier which at each round automatically selects the most salient one. Therefore, when a new mammogram is tested only the salient features are computed and used to classify each pixel of the mammogram as being part of a micro-calcification or actually being normal tissue. The experimental results shows the validity of our approach. Moreover, the robustness of our method is also demonstrated using a digitised database for the learning process and a different one for the testing, providing satisfactory results.

We present here an approach for automatic mass diagnosis in mammographic images. Our strategy contains three main steps. Firstly, region of interests containing mass and background are segmented using a level set algorithm based on region information. Secondly, the characterisation of each segmented mass is obtained using the Zernike moments for modelling its shape. The final step is the diagnosis of masses as benign or malignant lesions, which is done using the Gentleboost algorithm that also assigns a likelihood value to the final result. The experimental evaluation, performed using two different digitised databases and Receiver Operating Characteristics (ROC) analysis, proves the feasibility of our proposal, showing the benefits of a correct shape description for improving automatic mass diagnosis.

Several techniques have been proposed so far in order to perform faint compact source detection in wide field interferometric radio images. However, all these methods can easily miss some detections or obtain a high number of false positive detections due to the low intensity of the sources, the noise ratio, and the interferometric patterns present in the images. In this paper we present a novel strategy to tackle this problem. Our approach is based on using local features extracted from a bank of filters in order to provide a description of different types of faint source structures. We then perform a training step in order to automatically learn and select the most salient features, which are used in a Boosting classifier to perform the detection. The validity of our method is demonstrated using 19 real images that compose a radio mosaic. The comparison with two well-known state of the art methods shows that our approach is able to obtain more source detections, reducing also the number of false positives.

Autonomous Underwater Vehicles (AUVs) need positioning systems different than the Global Positioning System (GPS), which does not work in underwater scenarios. A possible solution to this lack of GPS signal are the Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms aim to build a map while simultaneously localize the vehicle within it. These algorithms suffer from several limitations in front of large scale scenarios. For instance, they do not perform consistent maps for large areas, mainly because uncertainties increase with the scenario. In addition, the computational cost increases with the map size. It has been demonstrated that the use of local maps reduces computational cost and improves map consistency. Following this idea, in this paper we propose a new SLAM technique based on using independent local maps, combined with a global level stochastic map. The global level contains the relative transformations between local maps. These local maps are updated once a new loop is detected and the amount of overlapping between local maps is high. Thus, maps sharing a high number of features are updated through fusion, maintaining the correlation between landmarks and vehicle. Experimental results on real data obtained from the REMUS-100 AUV show that our approach is able to obtain large map areas consistently.

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation.

In this paper we present a novel Structure from Motion (SfM) approach able to infer 3D deformable models from uncalibrated stereo images. Using a stereo setup dramatically improves the 3D model estimation when the observed 3D shape is mostly deforming without undergoing strong rigid motion. Our approach first calibrates the stereo system automatically and then computes a single metric rigid structure for each frame. Afterwards, these 3D shapes are aligned to a reference view using a RANSAC method in order to compute the mean shape of the object and to select the subset of points on the object which have remained rigid throughout the sequence without deforming. The selected rigid points are then used to compute frame-wise shape registration and to extract the motion parameters robustly from frame to frame. Finally, all this information is used in a global optimization stage with bundle adjustment which allows to refine the frame-wise initial solution and also to recover the non-rigid 3D model. We show results on synthetic and real data that prove the performance of the proposed method even when there is no rigid motion in the original sequence.

In this paper we focus on the estimation of the 3D Euclidean shape and motion of a non-rigid object which is moving rigidly while deforming and is observed by a perspective camera. Our method exploits the fact that it is often a reasonable assumption that some of the points are deforming throughout the sequence while others remain rigid. First we use an automatic segmentation algorithm to identify the set of rigid points which in turn is used to estimate the internal camera calibration parameters and the overall rigid motion. Finally we formalise the problem of non-rigid shape estimation as a constrained non-linear minimization adding priors on the degree of deformability of each point. We perform experiments on synthetic and real data which show firstly that even when using a minimal set of rigid points it is possible to obtain reliable metric information and secondly that the shape priors help to disambiguate the contribution to the image motion caused by the deformation and the perspective distortion.

In this paper we address the problem of projective reconstruction for deformable objects. Recent work in non-rigid factorization has proved that it is possible to model deformations as a linear combination of basis shapes, allowing the recovery of camera motion and 3D shape under weak perspective viewing conditions. However, the performance of these methods degrades when the object of interest is close to the camera and strong perspective distortion is present in the data. The main contribution of this work is the proposal of a practical method for the recovery of projective depths, camera motion and non-rigid 3D shape from a sequence of images under strong perspective conditions. Our approach is based on minimizing 2D reprojection errors, solving the minimization as four weighted least squares problems. Results using synthetic and real data are given to illustrate the performance of our method. 1

In this paper we investigate the effect of the illuminant tilt rotation over surface textures by analysing a set of image texture features extracted from the co-occurrence matrix. From the behaviour of each texture feature, a simple method able to predict the illuminant tilt angle of test images is developed. Moreover, the method is also used to perform a texture classification invariant to the illuminant tilt angle rotation. This study and experimental results over different real textures show that the illumination tilt angle can be accurately predicted, as part of the texture classification process. 1

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image.

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported.