The paper explores students' learning about gases in chemistry through constructing computational models of complex systems with the new Much.Matter.in.Motion platform (MMM; [7]). The design of MMM is based on the agent-based modelling approach to complex systems. The interface is governed by an epistemological structure into which programming blocks are integrated. They are inserted into one of three sections: properties, actions and interactions, for each population of entities. 22 Seventh-grade students' learning of science and systems concepts and modeling practices using the MMM are compared with 28 students' learning with a normative curriculum. Findings shows that conceptual learning is deeper and more integrated; the system's levels are better distinguished and related. Students' modeling expresses a gradual increase in explorative-ness and most importantly, show a gradual shift from relying on the external modeling platform to activating and developing their own internal mental models on the fly.

Privacy preserving and data security is one of the major concerns of our world. In this study we deal with one aspect, namely shoulder surfing, where sensitive information may be accessed by looking at the display or keyboard of the user. We propose a method of hiding information in color images so that it can not be perceived by the naked eye and requires a spectral filter to be seen. We term such images Spectral Hiding Images. In this work we developed a system which can automatically generate Spectral Hiding Images. We focus on a basic class of images containing a single numeric digit. We train three deep networks to determine the salient digit in an image, determine the hidden and masked digits in a spectral hiding image and to generate diverse spectral hiding digit images. Mass producing such Spectral Hiding Images using our system will allow for screen content hiding and password hiding. Additionally we show that several Gestalt principles of human perception, are expressed in the trained networks' behavior.

Stroke is one of the most common adult injuries, with 6.5 million stroke survivors in the US alone. We use a novel motion capture system together with machine learning tools to evaluate the standard stroke rehabilitation scale, the Fugl-Meyer Assessment (FMA). FMA involves the patient performing specific motor actions. A medical professional rates the performance and provides an FMA score. We have developed a multi depth-camera system using off the shelf consumer depth cameras. Its novelty is in its ability to perform synchronization, data integration and most importantly, calibration on the fly automatically without the need of a professional operator. The camera system tracks the subject's body and outputs a stream of skeleton representations, which allows to evaluate the patients's motor performance. Using a multi camera system rather than a single camera allows capturing motion on all sides of the patient body, as required by the FMA. The system was evaluated in a pilot study at a major hospital. Applying machine learning techniques on the skeleton streams, the system was able to correctly asses FMA scores on 2 of the standard motions with close to 100% success rate. This serves as a proof of concept for the feasibility of creating a full FMA home based assessment tool.

The authors introduce a novel marker‐less multi‐camera setup that allows easy synchronisation between 3D cameras as well as a novel pose estimation method that is calculated on the fly based on the human body being tracked, and thus requires no calibration session nor special calibration equipment. They show high accuracy in both calibration and data merging and is on par with equipment‐based calibration. They deduce several insights and practical guidelines for the camera setup and for the preferred data merging methods. Finally, they present a test case that computerises the Fugl‐Meyer stroke rehabilitation protocol using the authors’ multi‐sensor capture system. They conducted a Helsinki‐approved research in a hospital in which they collected data on stroke patients and healthy subjects using their multi‐camera system. Spatio‐temporal features were extracted from the acquired data and machine learning‐based evaluations were applied. Results showed that patients and healthy subjects can be correctly classified at a rate of above 90%. Furthermore, they show that the most significant features in the classification are strongly correlated with the Fugl‐Meyer guidelines. This demonstrates the feasibility of a low‐cost, flexible and non‐invasive motion capture system that can potentially be operated in a home setting.

A fast pattern matching scheme termed matching by tone mapping (MTM) is introduced which allows matching under nonlinear tone mappings. We show that, when tone mapping is approximated by a piecewise constant/linear function, a fast computational scheme is possible requiring computational time similar to the fast implementation of normalized cross correlation (NCC). In fact, the MTM measure can be viewed as a generalization of the NCC for nonlinear mappings and actually reduces to NCC when mappings are restricted to be linear. We empirically show that the MTM is highly discriminative and robust to noise with comparable performance capability to that of the well performing mutual information, but on par with NCC in terms of computation time.

Video analysis often requires mapping of activity or object locations from image coordinates to ground plane coordinates. This process is termed Plane Rectification. In this paper we propose a geometric method to find plane rectification using the plane's vanishing line and the vertical vanishing point. Unlike common methods that provide sophisticated algebraic solutions and non-linear optimizations, the proposed approach is intuitive and simple to implement while providing a geometric explanation and interpretation of the plane rectification. We show that the proposed approach provides stable and accurate solutions also in the presence of noise.

In this paper, we propose a novel video foreground detection method that exploits the statistics of 3D spacetime patches. 3D space-time patches are characterized by means of the subspace they span. As the complexity of real-time systems prohibits performing this modeling directly on the raw pixel data, we propose a novel framework in which spatiotemporal data is sequentially reduced in two stages. The first stage reduces the data using a cascade of linear projections of 3D space-time patches onto a small set of 3D Walsh-Hadamard (WH) basis functions known for its energy compaction of natural images and videos. This stage is efficiently implemented using the Gray-Code filtering scheme [2] requiring only 2 operations per projection. In the second stage, the data is further reduced by applying PCA directly to the WH coefficients exploiting the local statistics in an adaptive manner. Unlike common techniques, this spatiotemporal adaptive projection exploits window appearance as well as its dynamic characteristics. Tests show that the proposed method outperforms recent foreground detection methods and is suitable for real-time implementation on streaming video.

We propose a fast pattern matching scheme termed Matching by Tone Mapping (MTM) which allows matching under non-linear tone mappings. We show that, when tone mapping is approximated by a piecewise constant function, a fast computational scheme is possible requiring computational time similar to the fast implementation of Normalized Cross Correlation (NCC). In fact, the MTM measure can be viewed as a generalization of the NCC for non-linear mappings and actually reduces to NCC when mappings are restricted to be linear. The MTM is shown to be invariant to non-linear tone mappings, and is empirically shown to be highly discriminative and robust to noise.

Removal of shadows from a single image is a challenging problem. Producing a high-quality shadow-free image which is indistinguishable from a reproduction of a true shadow-free scene is even more difficult. Shadows in images are typically affected by several phenomena in the scene, including physical phenomena such as lighting conditions, type and behavior of shadowed surfaces, occluding objects, etc. Additionally, shadow regions may undergo post-acquisition image processing transformations, e.g., contrast enhancement, which may introduce noticeable artifacts in the shadow-free images. We argue that the assumptions introduced in most studies arise from the complexity of the problem of shadow removal from a single image and limit the class of shadow images which can be handled by these methods. The purpose of this paper is twofold: First, it provides a comprehensive survey of the problems and challenges which may occur when removing shadows from a single image. In the second part of the paper, we present our framework for shadow removal, in which we attempt to overcome some of the fundamental problems described in the first part of the paper. Experimental results demonstrating the capabilities of our algorithm are presented.

Motion in modern video coders is estimated using a block matching algorithm that calculates the distance and direction of motion on a block-by-block basis. In this paper, a novel fast block-based motion estimation algorithm is proposed. This algorithm uses an efficient projection framework that bounds the distance between a template block and candidate blocks. Fast projection is performed using a family of highly efficient filter kernels--the gray-code kernels--requiring only 2 operations per pixel per kernel. The projection framework is combined with a rejection scheme which allows rapid rejection of candidate blocks that are distant from the template block. The tradeoff between computational complexity and quality of results can be easily controlled in the proposed algorithm; thus, it enables adaptivity to image content to further improve the results. Experiments show that the proposed adaptive algorithm outperforms other popular fast motion estimation algorithms.

Several approaches to shadow removal in color images have been introduced in recent years. Yet these methods fail in removing shadows that are cast on curved surfaces, as well as retaining the original texture of the image in shadow boundaries, known as penumbra regions. In this paper, we propose a novel approach which effectively removes shadows from curved surfaces while retaining the textural information in the penumbra, yielding high quality shadow-free images. Our approach aims at finding scale factors to cancel the effect of shadows, including penumbra regions where illumination changes gradually. Due to the fact that surface geometry is also taken into account when computing the scale factors, our method can handle a wider range of shadow images than current state-of-the-art methods, as demonstrated by several examples.

In this paper, we introduce a family of filter kernels--the Gray-Code Kernels (GCK) and demonstrate their use in image analysis. Filtering an image with a sequence of Gray-Code Kernels is highly efficient and requires only two operations per pixel for each filter kernel, independent of the size or dimension of the kernel. We show that the family of kernels is large and includes the Walsh-Hadamard kernels, among others. The GCK can be used to approximate any desired kernel and, as such forms, a complete representation. The efficiency of computation using a sequence of GCK filters can be exploited for various real-time applications, such as, pattern detection, feature extraction, texture analysis, texture synthesis, and more.

Recently, a novel approach to pattern matching was introduced, which reduces time complexity by two orders of magnitude over traditional approaches. It uses an efficient projection scheme which bounds the distance between a pattern and an image window using very few operations on average. The projection framework combined with a rejection scheme allows rapid rejection of image windows that are distant from the pattern. One of the limitations of this approach is the restriction to square dyadic patterns. In this paper we introduce a scheme, based on this approach which allows fast search for patterns of any size and shape. The suggested method takes advantage of the inherent recursive tree-structure introduced in the original scheme.

This paper suggests a new measure of symmetry for bifurcating structures, which relies not only on topology and ordering, but also on quantitative properties (e.g. length of branches). This measure is based on the amount of energy required to symmetrize the structure, hence is especially suitable to biological structures. It is tested by its ability to differentiate between two classes of leaves.

A novel approach to pattern matching is presented in which time complexity is reduced by two orders of magnitude compared to traditional approaches. The suggested approach uses an efficient projection scheme which bounds the distance between a pattern and an image window using very few operations on average. The projection framework is combined with a rejection scheme which allows rapid rejection of image windows that are distant from the pattern. Experiments show that the approach is effective even under very noisy conditions. The approach described here can also be used in classification schemes where the projection values serve as input features that are informative and fast to extract.

A novel approach to pattern matching is presented, which reduces time complexity by two orders of magnitude compared to traditional approaches. The suggested approach uses an efficient projection scheme which bounds the distance between a pattern and an image window using very few operations. The projection framework is combined with a rejection scheme which allows rapid rejection of image windows that are distant from the pattern. Experiments show that the approach is effective even under very noisy conditions. The approach described here can also be used in classification schemes where the projection values serve as input features that are informative and fast to extract.

Motion estimation, motion detection and tracking invariably require finding a particular pattern in a set or sequence of images. The task involves finding appearances of a given pattern in an image under various transformations and at various locations. This process is of very high time complexity since a search must be implemented both in the transformation domain and in the spatial domain. Contributing to this complexity is the chosen distance metric that measures the similarity between patterns. The Euclidean distance, for example, may change drastically when a small transformation is applied to the pattern. Applying a different metric distance might be advantageous, though at the expense of loosing the norm structure of the Euclidean space. In this work we present a new method for fast search in the transformation domain, which can also be applied in metric spaces. The method is based on recursive decomposition of the transformation domain, and a rejection scheme, which enables the process to quickly reject as irrelevant large percentages of this decomposition.

A drawback of most watermarking techniques is the need for some additional information in order to retrieve the watermark. Additionally, the robustness of the watermark decreases as the number of information bits stored in the image increases. We present a watermarking technique which requires no information for decoding in addition to the watermarked image. The watermark is multi-level with few bits embedded robustly at low levels and longer watermark sequences embedded less robustly at higher levels. This allows detection of "tampered" and "attacked" images by detection of existence of the watermark at low levels and deterioration of the watermark at high levels. In order to prevent interference of the watermarks at different levels various image representation spaces are used. The watermark is shown to be non-visible and robust.

In this paper we introduce the idea of using computer vision techniques for improving and enhancing watermarking capabilities. Specifically, we incorporate geometric hashing techniques into the watermarking methodology. Geometric hashing was developed to detect objects in a visual scene under a class of geometric transformations. The technique is incorporated into watermarking to detect watermarks encoded under transformations. This allows randomization of the watermark code without the need of maintaining the random generator seed. In turn, this randomization increases robustness under attacks such as collusion (determining the watermark from multiple watermarked examples). Depending on the embedding domain, robustness of the watermark under geometric attacks can be achieved.

Digital watermarking is a labeling technique for digital images which embeds a code into the digital data so the data is marked. Watermarking techniques previously developed deal with on-line digital data and have been developed to withstand digital attacks such as image processing, compression and geometric transformations. We deal with printed images rather than on-line data. An important issue is to intercept and prevent forgery in printed material such as currency notes and bank checks and to track and validate sensitive and secret printed material. Watermarking in such printed material can also be used for embedding additional information (source, date, type of transaction etc). We propose a method of embedding watermarks in printed images by inherently, exploiting the printing process. The method is visually unobtrusive to the printed image, the watermark is easily extracted and is robust under reconstruction errors. The decoding algorithm is automatic given the watermarked image.

Symmetry is treated as a continuous feature and a continuous measure of distance from symmetry in shapes is defined. The symmetry distance (SD) of a shape is defined to be the minimum mean squared distance required to move points of the original shape in order to obtain a symmetrical shape. This general definition of a symmetry measure enables a comparison of the "amount" of symmetry of different shapes and the "amount" of different symmetries of a single shape. This measure is applicable to any type of symmetry in any dimension. The symmetry distance gives rise to a method of reconstructing symmetry of occluded shapes. The authors extend the method to deal with symmetries of noisy and fuzzy data. Finally, the authors consider grayscale images as 3D shapes, and use the symmetry distance to find the orientation of symmetric objects from their images, and to find locally symmetric regions in images.

Symmetry is usually viewed as a discrete feature: an object is either symmetric or non-symmetric. Following the view that symmetry is a continuous feature, a continuous symmetry measure (CSM) has been developed to evaluate symmetries of shapes and objects. In this paper the authors extend the symmetry measure to evaluate the imperfect symmetry of fuzzy shapes, i.e. shapes with uncertain point localization. The authors find the probability distribution of symmetry values for a given fuzzy shape. Additionally, for every such fuzzy shape, the authors find the most probable symmetric shape.

Following the view that symmetry is a continuous feature, a continuous symmetry measure (CSM) is developed to evaluate symmetrics of shapes and objects. The symmetry measure is extended to evaluate the symmetry of occluded shapes. Using the symmetry measure, occluded shapes are reconstructed by locating the center of symmetry of the shape.>

The authors view symmetry as a continuous feature, and define a continuous symmetry measure (CSM) of shapes. The general definition of symmetry measure allows a comparison of the amount of symmetry of different shapes and the amount of different symmetries of a single shape. Furthermore, the CSM is associated with the symmetric shape that is closest to the given one, enabling visual evaluation of the CSM.>

The authors view symmetry as a continuous feature and dependent on resolution. Combining a continuous symmetry measure (CSM) with a multiresolution scheme, the authors present a method that hierarchically detects symmetric and almost symmetric patterns. Evaluation of symmetry at low frequencies guides the process to find the symmetry at higher frequencies.>

The authors study the notion of chirality for two-dimensional binary shapes, and introduce measures to test whether a shape is symmetric, and if not whether it is left-handed or right-handed. The measures are based on boundary analysis, and perform well even when digital images of left-handed shapes differ from the mirror images of right-handed shapes. Such situations may occur due to natural variations and digitization errors. The measures can also successfully treat partially occluded shapes, and provide indications on the change of chirality as resolution changes.>