Manipulation and re-use of images in scientific publications is a growing issue, not only for biomedical publishers, but also for the research community in general. In this work we introduce BINDER - Bio-Image Near-Duplicate Examples Repository, a novel dataset to help researchers develop, train, and test models to detect same-source biomedical images. BINDER contains 7,490 unique image patches for model training, 1,821 same-size patch duplicates for validation and testing, and 868 different-size image/patch pairs for image retrieval validation and testing. Except for the training set, patches already contain manipulations including rotation, translation, scale, perspective transform, contrast adjustment and/or compression artifacts. We further use the dataset to demonstrate how novel adaptations of existing image retrieval and metric learning models can be applied to achieve high-accuracy inference results, creating a baseline for future work. In aggregate, we thus present a supervised protocol for near-duplicate image identification and retrieval without any “real-world” training example. Our dataset and source code are available at hms-idac.github.io/BINDER.

This paper proposes the use of complex variables to represent votes in the Hough transform for circle detection. Replacing the positive numbers classically used in the parameter space of the Hough transforms by complex numbers allows cancellation effects when adding up the votes. Cancellation and the computation of shape likelihood via a complex number's magnitude square lead to more robust solutions than the “classic” algorithms, as shown by computational experiments on synthetic and real datasets. We note a resemblance to methods used in quantum theory.

We propose a data structure that captures global geometric properties in images: Histogram of Mirror Symmetry Coefficients. We compute such a coefficient for every pair of pixels, and group them in a 6-dimensional histogram. By marginalizing the HMSC in various ways, we develop algorithms for a range of applications: detection of nearly-circular cells, location of the main axis of reflection symmetry, detection of cell-division in movies of developing embryos, detection of worm-tips and indirect cell-counting via supervised classification. Our approach generalizes a series of histogram-related methods, and the proposed algorithms perform with state-of-the-art accuracy.

We describe a statistical approach to the problem of estimating the times of cell-division cycles in time-lapse movies of early mouse embryos. Our method is based on the likelihoods for cells of certain radii ranges to be in each frame — without actually locating or counting the cells. Computing the likelihoods consists of a voting scheme where votes come form quadruples of points in a way similar to the first step of the Randomized Hough Transform for ellipse detection. To locate divisions, we search for points of abrupt change in the matrix of likelihoods (built for all frames), and pick the two optimal division points using a dynamic programming algorithm. Our results for the first and second cell division cycles differ less than two frames from the medians of the annotated times in a database of 100 annotated videos, and outperform two other recent methods in the same set.

We present an ellipse finding and fitting algorithm that uses points and tangents, rather than just points, as the basic unit of information. These units are analyzed in a hierarchy: points with tangents are paired into triangles in the first layer and pairs of triangles in the second layer vote for ellipse centers. The remaining parameters are estimated via robust linear algebra: eigen-decomposition and iteratively reweighed least squares. Our method outperforms the state-of-the-art approach in synthetic images and microscopic images of cells.

Visual cues-based anticipation is a fundamental aspect of human-human interaction, and it plays an especially important role in the time demanding medium of group performance. In this work we explore the importance of visual gesture anticipation in music performance involving human and robot. We study the case in which a human percussionist is playing a four-piece percussion set, and a robot musician is playing either the marimba, or a three-piece percussion set. Computer Vision is used to embed anticipation in the robotic response to the human gestures. We developed two algorithms for anticipation, predicting the strike location about 10 mili-seconds or about 100 mili-seconds before it occurs. Using the second algorithm, we show that the robot outperforms, on average, a group of human subjects, in synchronizing its gesture with a reference strike. We also show that, in the tested group of users, having some time in advance is important for a human to synchronize the strike with a reference player, but, from a certain time, that good influence stops increasing.

This work presents a method for obtaining perceptually natural panoramic videos, that is, videos composed of wide-angle frames where straight lines and object shapes are preserved. The shape preservation for moving objects has a special treatment that ensures temporal coherence. Energies describing these properties are obtained for the case of a fixed omni-directional camera, and an optimization procedure is presented. Our optimization works per-frame, which makes the method scalable for arbitrarily long scenes.