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This paper addresses the automatic identification of pelagic species in acoustic backscatter data. Large quantities of data acquired during underwater acoustic surveys for environmental monitoring and resources management, visualized as echograms, are typically analyzed manually or semi-automatically by marine biologists, which is time-consuming and prone to errors and inter-expert disagreements. In this paper, we propose to detect pelagic species (schools of herring and of juvenile salmon) from echograms with a deep learning (DL) framework based on instance segmentation, allowing us to carefully study the acoustic properties of the targets and to address specific challenges such as close proximity between schools and varying size. Experimental results demonstrate our system’s ability to correctly detect pelagic species from echograms and to outperform an existing object detection framework designed for schools of herring in terms of detection performance and computational resources utilization. Our pixel-level detection method has the advantage of generating a precise identification of the pixel groups forming each detection, opening up many possibilities for automatic biological analyses.

Chest radiographs are used for the diagnosis of multiple critical illnesses (e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the automatic or semi-automatic analysis of these data are of particular interest. An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists, ultimately allowing for better medical care of lung-, heart- and chest-related conditions. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information that is typically lost in the down-sampling of high-resolution radiographs, a common step in computer-aided diagnostic pipelines. Our proposed approach requires only slight modifications to the input of existing state-of-the-art Convolutional Neural Networks (CNNs), making it easily applicable to existing image classification frameworks. We show that the extra high-frequency components offered by our method increased the classification performance of several CNNs in benchmarks employing the NIH Chest-8 and ImageNet-2017 datasets. Based on our results we hypothesize that providing frequency-specific coefficients allows the CNNs to specialize in the identification of structures that are particular to a frequency band, ultimately increasing classification performance, without an increase in computational load. The implementation of our work is available at github.com/DeclanMcIntosh/LeGallCuda., Published in 2021 18th Conference on Robots and Vision (CRV). 8 pages with referances

This paper provides a comprehensive comparative study of traditional, hybrid, and deep learning (DL) methods for detecting marine species in echograms. Acoustic backscatter data obtained from multi-frequency echosounders is visualized as echograms and typically interpreted by marine biologists via manual or semi-automatic methods, which are time-consuming. Challenges related to automatic echogram interpretation are the variable size and acoustic properties of the biological targets (marine life), along with significant inter-class similarities. Our study explores and compares three types of approaches that cover the entire range of machine learning methods. Based on our experimental results, we conclude that an end-to-end DL-based framework, that can be readily scaled to accommodate new species, is overall preferable to other learning approaches for echogram interpretation, even when only a limited number of annotated training samples is available.

Marine vessel traffic is one of the main sources of negative anthropogenic impact upon marine environments. The automatic identification of boats in monitoring images facilitates conservation, research and patrolling efforts. However, the diverse sizes of vessels, the highly dynamic water surface and weather-related visibility issues significantly hinder this task. While recent deep learning (DL)-based object detectors identify well medium- and large-sized boats, smaller vessels, often responsible for substantial disturbance to sensitive marine life, are typically not detected. We propose a detection approach that combines state-of-the-art object detectors and a novel Detector of Small Marine Vessels (DSMV) to identify boats of any size. The DSMV uses a short time series of images and a novel bi-directional Gaussian Mixture technique to determine motion in combination with context-based filtering and a DL-based image classifier. Experimental results obtained on our novel datasets of images containing boats of various sizes show that the proposed approach comfortably outperforms five popular state-of-the-art object detectors. Code and datasets available at https://github.com/tunai/hybrid-boat-detection.

Images captured underwater often suffer from suboptimal illumination settings that can hide important visual features, reducing their quality. We present a novel single-image low-light underwater image enhancer, L 2 UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information. We create two distinct models and generate two enhanced images from them: one that highlights finer details, the other focused on darkness removal. A multi-scale fusion process is employed to combine these images while emphasizing regions of higher luminance, saliency and local contrast. We demonstrate the performance of L 2 UWE by using seven metrics to test it against seven state-of-the-art enhancement methods specific to underwater and low-light scenes.

A back-of-the-napkin idea is typically created on the spur of the moment and captured via a few hand-sketched notes on whatever material is available, which often happens to be an actual paper napkin. This paper explores the preservation of such back-of-the-napkin ideas. Hand-sketched notes, reflecting those flashes of inspiration, are not limited to text; they can also include drawings and graphics. Napkin backgrounds typically exhibit diverse textural and colour motifs/patterns that may have high visual saliency from a low-level vision standpoint. We thus frame the extraction of hand-sketched notes as a background modelling and removal task. We propose a novel document background model based on texture mixtures constructed from the document itself via texture synthesis, which allows us to identify background pixels and extract hand-sketched data as foreground elements. Experiments on a novel napkin image dataset yield excellent results and showcase the robustness of our method with respect to the napkin contents. A texture-based background modelling approach, such as ours, is generic enough to cope with any type of hand-sketched notes.

This paper focuses on the rectification of camera-captured document images with varied layouts of mixed contents. Document images acquired via cameras, including smartphones, are typically plagued by perspective, geometric, and/or rotational distortion that hinders document analysis processes. In this paper, we propose an approach to camera-captured image rectification of text and non-text regions that handles perspective, geometric and rotational distortions present in planar and curled documents, extending a state-of-the-art content-based rectification method. We define surface projections via a three-tiered local transformation model, in which primary curved surface projections are formed from individual text regions, and secondary and tertiary surface projections are formed from non-text regions, resulting in a 'patchwork' combination of surfaces spanning the document image. This transformation model allows us to process document images with varied layouts of mixed contents, including large images and graphics, that also contain some justified text. Experiments and comparisons with a state-of-the-art content-based rectification approach on the public IUPR dataset demonstrate the value of the proposed approach on two levels: 1) a significantly improved rectification performance using standard optical character recognition metrics, along with increased document readability, and 2) an improved range of applicability, i.e. ability to correct document images showing various layouts and content types.

In this paper, we look at the societal effects of computer vision technologies from the perspective of the future minds in computer vision: senior year engineering students. Engineering education has traditionally focused on technical skills and knowledge. Nowadays, the need for educating engineers in socio-technical skills and reflective thinking, especially on the bright and dark sides of the technology they develop, is being recognized. We advocate for the integration of social awareness modules into computer vision courses so that the societal effects of technology can be studied together with the technology itself, as opposed to the often more generic 'impact of technology on society' courses. Such modules provide a venue for students to reflect on the real-world consequences of technology in concrete, practical contexts. In this paper, we present qualitative results of an observational study analyzing essays of senior year engineering students, who wrote about societal impacts of computer vision technologies of their choice. Privacy and security issues ranked as the top impact topics discussed by students among 50 topics. Similar social awareness modules would apply well to other advanced technical courses of the engineering curriculum where privacy and security are a major concern, such as big data courses. We believe that such modules are highly likely to enhance the reflective abilities of engineering graduates regarding societal impacts of novel technologies.

This paper focuses on the automatic quantitative performance analysis of bioprosthetic heart valves from video footage acquired during in vitro testing. Bioprosthetic heart valves, mimicking the shape and functionality of a human heart valve, are routinely used in valve replacement procedures to substitute defective native valves. Their reliability in both functionality and durability is crucial to the patients' well-being, as such, valve designs must be rigorously tested before deployment. A key quality metric of a heart valve design is the cyclical temporal evolution of the valve's area. This metric is typically computed manually from input video data, a time-consuming and error-prone task. We propose a novel, cost-effective approach for the automatic tracking and segmentation of valve orifices that integrates a probabilistic motion boundary model into a distance regularized level set evolution formulation. The proposed method constrains the level set evolution domain using data about characteristic motion patterns of heart valves. Experiments including comparisons with two other methods demonstrate the value of the proposed approach on three levels: an improved segmented orifice shape accuracy, a greater computational efficiency, and a better ability to identify video frames with orifice area content (open valve).

Nonverbal cues constitute a significant part of human communication. Traditionally the object of psychology, nonverbal communication studies now permeate fields such as social signal processing and human computer interaction. The ubiquity of digital recordings of human social interactions and of free sharing platforms offers many opportunities for the automated analysis of group interaction dynamics; yet, most research relies on multimodal cues and strict setups, which are incompatible with this vast pool of video data. In this paper, we focus on the automatic identification of non-talking participants in videos of panel conversations acquired in uncontrolled environments, based solely on visual nonverbal cues. Our approach characterizes human body motion with a novel feature descriptor based on a non-linear model of pixel change history; motor behavioral patterns derived from this descriptor are then utilized via supervised machine learning to identify non-talking participants in each frame and provide an assessment of the participants' engagement levels. Performance evaluation on a challenging dataset demonstrated the effectiveness of our approach to detect non-speakers, with an overall F-score of 86.2%, as well as its robustness to varied settings. To the best of our knowledge, this is the first attempt at identifying non-talking participants for engagement level assessments from a computer vision viewpoint, which has several relevant applications, such as content-based video retrieval and video summarization.

This paper addresses the problem of semantic overlap across document objects in the context of ground truth representation for document layout analysis. Document object categories often share primitives from a low-level perspective (e.g. regions inside bars in a bar chart resemble background), making it difficult to evaluate document layout segmentation methods based on pixel classification, as most datasets and ground truth models focus on document objects. We propose a novel ground truth model that utilizes structural and statistical pattern recognition concepts. Statistical pixel-based data derived from low-level elemental patterns are layered onto high-level structural object-based data. We also present evaluation metrics that take advantage of the layered ground truth model, allowing a contextual evaluation of pixel classification algorithms. We apply the proposed model to two recent pixel classification approaches, evaluated on business document images that exhibit a challenging mixture of textual, graphical, and pictorial elements through varied layouts. The proposed model allows to obtain very detailed, comprehensive, and intuitive information on the strengths and limitations of the evaluated approaches that would be impossible to obtain through other models.

The concept of sparseness has played an important role in classical signal processing applications such as the acquisition, sampling, and compression of high-dimensionality signals, as well as in various machine learning techniques. Computer vision applications have also benefited in more recent years from sparse representations, which can help recover semantic information from images. In this paper, we shed a unique light on the concept of sparseness and propose a comparative study of four popular sparseness measures applied in a novel way to the problem of texture segmentation. Low-dimensional, contextual, multi-resolution descriptors are derived directly from the sparseness of the pixels' responses to a Gabor filter bank, unlike traditional sparseness-based approaches, we do not impose constraints on or make assumptions about the sparseness of the data or of their representation. Textured images are segmented through pixel labelling via supervised machine learning using the sparseness-based descriptors. The behaviour of the four compared sparseness measures, namely Hoyer's measure, the Gini index, the kurtosis, and the normalized hyperbolic tangent, is analyzed with respect to general rules and desirable attributes using synthetic examples, and is evaluated for texture segmentation problems on the public Outex dataset with respect to texture classes and pixel pattern categories. We found that although the four measures all intend to capture the same information, they yield very different segmentation results, and we recommend the Gini index as the sparseness measure of choice for texture segmentation problems.

We propose a system that automatically detects abnormal, violent actions that are performed by individual subjects and witnessed by passive crowds. The problem of abnormal individual behavior, such as a fight, witnessed by passive bystanders gathered into a crowd has not been studied before. We show that the presence of a passive, standing crowd is an important indicator that an abnormal action might occur. Thus, detecting the standing crowd improves the performance of detecting abnormal, violent actions. The proposed method performs crowd detection first, followed by the detection of abnormal motion events. Our main theoretical contribution consists in linking crowd detection to abnormal, violent actions, as well as in defining novel sets of features that characterize static crowds and abnormal individual actions in both spatial and spatio-temporal domains. Experimental results are computed on a custom dataset, the Vancouver Riot Dataset, that we generated using amateur video footage acquired with handheld devices and uploaded on public social network sites. Our approach achieves a high precision and recall values, which validates our system reliability of localizing the crowds and the abnormal actions.

This paper focuses on video summarization of abnormal behavior for remote invigilation of online exams. While the last decade has seen a massive increase in e-learning and online courses offered at postsecondary institutions, preserving the integrity of online examinations still heavily relies on web video conference invigilation performed by a remote proctor. Live remote invigilation is limited in the number of students that can be handled at once, and manual post-exam review is labor intensive. We propose a novel computer vision-based video content analysis system for the automatic creation of video summaries of online exams to assist remote proctors in post-exam reviews. The proposed method models normal and abnormal student behavior patterns using head pose estimations and a semantically meaningful two-state hidden Markov model. Video summaries are created from detected sequences of abnormal behavior. Experimental results are promising and demonstrate the viability of the proposed approach, which could readily be expanded to generate real-time alerts for live remote invigilation.

Using video and imagery for assessing the distribution and abundance of marine organisms is a valuable sampling method in that it is non-invasive and permits large volumes of data to be acquired. Quickly and accurately processing large volumes of imagery is a challenge for human analysts, which motivates the need for automated processing methods. In this paper, we present a method for the automatic classification of sea stars in underwater images. The method uses a very small number of features and is efficient. The classification process is optimized by using evolutionary computational methods. Experimental results show excellent performance of our proposed optimized classification approach.

In this paper, we present a challenging dataset for the purpose of segmentation and change detection in photographic images of mountain habitats. We also propose a baseline algorithm for habitats segmentation to allow for performance comparison. The dataset consists of high resolution image pairs of historic and repeat photographs of mountain habitats acquired in the Canadian Rocky Mountains for ecological surveys. With a time lapse of 70 to 100 years between the acquisition of historic and repeat images, these photographs contain critical information about ecological change in the Rockies. The challenging aspects of analyzing these image pairs come mostly from the perspective (oblique) view of the photographs and the lack of color information in the historic photographs. The baseline algorithm that we propose here is based on texture analysis and machine learning techniques. Classifier training and results validation are made possible by the availability of expert manual ground-truth segmentation for each image. The results obtained with the baseline algorithm are promising and serve as a reference for new and improved segmentation and change detection algorithms.

In this paper, we present a non-invasive method of counting fish in their natural habitat using automated analysis of video data. Our approach uses three modular components to preprocess, detect, and track the fish. The preprocessing reduces noise present in the image while enhancing the fish using several different techniques. The fish detection is based on two background subtraction algorithms which are computed independently and later combined with logical operations. The tracking is then carried out by a heuristic blob tracking algorithm. The paper presents a description of the proposed counting method as well as its experimental validation.

This paper exploits the concept of sparseness to generate novel contextual multi-resolution texture descriptors. We propose to extract low-dimension features from Gabor-filtered images by considering the sparseness of filter bank responses. We construct several texture descriptors: the basic version describes each pixel by its contextual textural sparseness, while other versions also integrate multi-resolution information. We apply the novel low-dimension sparseness-based descriptors to the problem of texture segmentation and evaluate their performance on the public Outex database. The sparseness-based descriptors show a substantial improvement over Gabor filters with respect not only to computational costs and memory usage, but also to segmentation accuracy. The proposed approach also shows a desirable smooth, monotonic behavior with respect to the dimensionality of the descriptors.

The workshop on Computer Vision for the Analysis of Underwater Imagery (CVAUI 2014) was held in conjunction with the International Conference on Pattern Recognition (ICPR) on August 24th 2014 in Stockholm, Sweden. This workshop provides a forum for researchers to share and discuss new methods and applications for underwater image analysis. We received 19 full-length paper submissions, out of which 11 were accepted based on a thorough double-blind peer review process. We thank the members of Program Committee for lending their time and expertise to ensure the high quality of the accepted workshop contributions.

The identification of pond turtles is important to scientists who monitor local populations, as it allows them to track the growth and health of subjects over their lifetime. Traditional non-invasive methods for turtle recognition involve the visual inspection of distinctive coloured patterns on their plastron. This visual inspection is time consuming and difficult to scale with a potential growth in the surveyed population. We propose an algorithm for automatic identification of individual turtles based on images of their plastron. Our approach uses a combination of image processing and neural networks. We perform a convexity-concavity analysis of the contours on the plastron. The output of this analysis is combined with additional region-based measurements to compute feature vectors that characterize individual turtles. These features are used to train a neural network. Our goal is to create a neural network which is able to query a database of images of turtles of known identity with an image of an unknown turtle, and which outputs the unknown turtle's identity. The paper provides a thorough experimental evaluation of the proposed approach. Results are promising and point towards future work in the area of standardized image acquisition and image denoising.

This paper proposes a novel method for computer vision-based, marker-less analysis of daily human actions for detecting motion irregularities (sway). Sway occurs due to a temporary loss in balance and is an important indicator of decay in motor skills. One should note that the purpose of the proposed approach is not to recognize the performed activity (which is a controlled variable in our experimental design), but to detect irregularities in the performance of this activity. The proposed motion model is based on population Hidden Markov Models. This model has been trained and tested on a custom-designed database involving multiple daily actions. Experimental results demonstrate its robustness with respect to subject and speed variability in training sequences, as well as its ability to capture sway-type motion irregularities.

Canadian undergraduate and graduate programs in Engineering and Computer Science attract a large number of international students. This is a relatively recent phenomenon with social and academic implications that are not completely understood. We are aware that more can be done for the recruitment, retention, and more generally for increasing the quality of the learning experience of our international students. More efforts need to be made in order to foster and expand social and academic interactions between Canadian and international students, as well as student-faculty interactions. The research described in this paper aims to identify the first steps in creating an inclusive environment that fosters academic, social, and personal growth for both international and Canadian students. This study discusses data collected about the experience of international undergraduate students in the Faculty of Engineering our university. The purpose of the data collection was to determine their specific needs, and to solicit suggestions and recommendations about ways in which to address them.

This paper presents the results of a comparison study using the Visual VIPERS interface, a graphical interface which can be applied as an educational tool for novice computer vision students. The goal of the study was to evaluate the change in usability of the interface after the addition of a monitor tool, which can be used to view intermediate image results at specific stages of an algorithm. A user study was conducted in which participants were asked to find an error in a pre-assembled algorithm. Results indicate that participants using the older version of the interface (with no monitor tool) took, on average, less time to find the error than participants who used the monitor tool. However, interview responses indicated a greater level of understanding of the algorithm from participants who used the monitor tool. Interview responses also demonstrated a clear desire from users of the old interface for the addition of a debugging tool (such as the newly introduced monitor tool). Our belief is that participants using the monitor tool performed a more thorough search of the algorithm, and thus gained a greater understanding of how the algorithm operated, while attempting to determine the source of the error.

We propose a new method for view-invariant gait modeling using a single calibrated camera. Piecewise-continuous body parts trajectories extracted from a video sequence are rectified to appear as observed from a fronto-parallel view. Standard gait characteristics are then computed by combining rectified gait half-cycles from each trajectory. In this method, we make use of a walk model that allows changes in direction as well as changes in speed in order to decouple gait characteristics from distracting factors in the observed sequence. In contrast with previous work, our method is thus well suited for both clinical and surveillance applications. Simulated and real trajectories from an indoor setting are used to validate the proposed method.

The IMAGERIA project aims to investigate new paths in recruiting female students in engineering. The first outcome of this project was a one-day pilot workshop called IMAGERIA: Visual computing festival for girls and held in spring 2007. This workshop was based on the hypothesis that hands-on exposure to computer vision algorithms with applications to digital photography may be a significant incentive for female high-school students to enroll in Electrical and Computer Engineering or Computer Science undergraduate programs. The IMAGERIA workshop was structured as a sequence of interactive modules of image processing. As database, the participants were enabled to either use a provided image library or to acquire their own pictures using USB digital cameras. For each module of the workshop, the paper outlines the lessons that were learned and which will allow for further improvements in the structure of the workshop.

Learning core concepts in signal processing courses is difficult for undergraduate students in electrical engineering. In our opinion, this difficulty comes from the gap between understanding the mathematical formalism of such concepts and being able to make sense of them in a practical way.

This paper proposes a method to obtain fronto-parallel (side-view) body part trajectories for a walk observed from an arbitrary view. The method is based on homography transformations computed for each gait half-cycle detected in the walk. Each homography maps the body part trajectories to a simulated side view of the walk. The proposed method is stable as the resulting normalized trajectories are not influenced by missing or omitted parts of the raw trajectories. Experiments confirm that normalized trajectories are in agreement with the ones that would be obtained from a side view.

This paper discusses an interdisciplinary educational initiative led by an instructional team with backgrounds in engineering and psychology in the context of an ergonomics course for software engineers. Our case study evaluates the educational outcomes of a course project that dealt with the error analysis and prototype-level redesign of a software tool for elderly users. The paper presents the rationale for the choice of this project, the project organization, and the evaluation of project-related outcomes with respect to the course learning objectives.

This paper proposes a novel approach for the real-time detection of sleep onset in fatigued drivers. Sleep onset is the most critical consequence of fatigued driving, as shown by statistics of fatigue-related crashes. Therefore, unlike previous related work, we separate the issue of sleep onset from the global analysis of the physiological state of fatigue. This allows us for formulating our approach as an event-detection problem. Real-time performance is achieved by focusing on a single visual cue (i.e. eye-state), and by a custom-designed template-matching algorithm for on-line eye-state detection.

The approach proposed in this paper retrieves contours from transrectal ultrasound (TRUS) prostate images. The input images are sparsely annotated by radiologists for the purpose of brachytherapy planning and post-interventional monitoring. The theoretical contribution of the paper consists in the design of a task-oriented, bottom-up method which mimics perceptual grouping mechanisms for contour retrieval. The proposed approach is task-oriented because it embeds prior anatomical and procedural knowledge. From a practical standpoint, the proposed approach is of clinical relevance, since it allows for retrieving contours from images where the annotation is 'blended' with the image content. While new image annotation systems are able to store image content and annotations in a separate manner, many TRUS prostate databases still contain 'blended' annotations only. Our approach allows for contour retrieval and further 3D prostate modeling from such databases.

This paper presents an integrated region-based and gradient-based supervised method for segmentation of a patient magnetic resonance images (MRI) of the shoulder joint. The method is noninvasive, anatomy-based and requires only simple user interaction. It is generic and easily customizable for a variety of routine clinical uses in orthopedic surgery.

This paper proposes an approach to compute view normalized body part trajectories of pedestrians from monocular video sequences. The proposed approach first extracts the 2D trajectories of both feet and of the head from tracked silhouettes. On that basis, it segments the walking trajectory into piecewise linear segments. Finally, a normalization process is applied to head and feet trajectories over each obtained straight walking segment. View normalization makes head and feet trajectories appear as if seen from a fronto-parallel viewpoint. The latter is assumed to be optimal for gait modeling and recognition purposes. The proposed approach is fully automatic as it requires neither manual initialization nor camera calibration.

This paper describes a new 3D motion representation, the Volumetric Motion History Image (VMHI), to be used for the analysis of irregularities in human actions. Such irregularities may occur either in speed or orientation and are strong indicators of the balance abilities and of the confidence level of the subject performing the activity. The proposed VMHI representation overcomes limits of the standard MHI related to motion self-occlusion and speed and is therefore suitable for the visualization and quantification of abnormal motion. This work focuses on the analysis of sway, which is the most common motion irregularity in the studied set of human actions. The sway is visualized and quantified via a user interface using a measure of spatiotemporal surface smoothness, namely the deviation vector. Experimental results show that the deviation vector is a reliable measure for quantifying the deviation of abnormal motion from its corresponding normal motion.

MONNET is a visual surveillance system for tracking pedestrians over extended premises. The MONNET system is composed of intelligent nodes, which exchange information on the individually tracked pedestrians in an asynchronous manner. Each node in MONNET builds an appearance model for every observed pedestrian and compares it with models received from other nodes. The compact appearance models based on colour cues and face biometrics are stored locally on each node. The system is dynamically reconfigurable since its design allows for adding/removing nodes in a simple manner, comparable to the ‘plug and play’ technology. MONNET also contains an optional ‘observer’ node for interactive data visualization. This node displays a user interface which allows a human operator to observe and to interact in real-time with the distributed tracking process. MONNET was extensively tested with and without user input, and it is able to function correctly in both modes.

This paper proposes a method to automatically track human body parts in the context of gait modelisation and recognition. The proposed approach is based on a five points human model (head, hands, and feet) where the points are detected and tracked independently. Tracking is fully automatic (no manual initialization of the five points) since it will be used in a real-time surveillance system. Feet are detected in each frame by first finding the space between the legs in the human silhouette. The issue of feet self-occlusion is handled using optical flow and motion correspondence. Skin color segmentation is used to find hands in each frame and tracking is achieved by using a bounding box overlap algorithm. The head is defined as the mass center of a region of the upper silhouette.

Here, we propose a 3D reconstruction approach using shape-based interpolation and contour-based extrapolation. This approach aims to generate a 3D geometric model of a human shoulder from a sequence of MR parallel 2D cross-sections. While interpolation generates intermediate slices between every pair of adjacent input slices, extrapolation performs a smooth closing of the external surface of the model. We propose a new interpolation method based on conditional morphological dilation. Our extrapolation approach is based on surface smoothness constraints and gradually shrinks every extreme 2D cross-section of a bony structure towards a point, respectively. Surface rendering is accomplished through the generation of a triangular mesh using a parametric representation of 2D slice contours. After surface rendering, local surface irregularities are smoothed with Taubin's surface fairing algorithm [G. Taubin, (1995)].