nils-herrmann/clean_scirepeval huggingface dataset:
An image retrieval algorithm using multiple query images

In this paper, an image retrieval algorithm using multiple query images is proposed. The algorithm is based on multihistogram intersection techniques. For each query image, a color histogram and a texture histogram are extracted. Then multihistogram intersection is used to measure the similarity between the query images and each image in the database. The ranking in similarity is used to determine the images to be retrieved. This approach can be applied to image retrieval with relevance feedback and to component based image retrieval. Results are provided showing the improvement in precision that is afforded by the multiexample retrieval paradigm.
Locally monotonic diffusion

Anisotropic diffusion affords an efficient, adaptive signal smoothing technique that can be used for signal enhancement, signal segmentation, and signal scale-space creation. This paper introduces a novel partial differential equation (PDE)-based diffusion method for generating locally monotonic signals. Unlike previous diffusion techniques that diverge or converge to trivial signals, locally monotonic (LOMO) diffusion converges rapidly to well-defined LOMO signals of the desired degree. The property of local monotonicity allows both slow and rapid signal transitions (ramp and step edges) while excluding outliers due to noise. In contrast with other diffusion methods, LOMO diffusion does not require an additional regularization step to process a noisy signal and uses no ad hoc thresholds or parameters. In the paper, we develop the LOMO diffusion technique and provide several salient properties, including stability and a characterization of the root signals. The convergence of the algorithm is well behaved (nonoscillatory) and is independent of the signal length, in contrast with the median filter. A special case of LOMO diffusion is identical to the optimal solution achieved via regression. Experimental results validate the claim that LOMO diffusion can produce denoised LOMO signals with low error using less computation than the median-order statistic approach.
Spatial domain digital watermarking of multimedia objects for buyer authentication

Most of the existing watermarking processes become vulnerable when the attacker knows the watermark insertion algorithm. This paper presents an invisible spatial domain watermark insertion algorithm for which we show that the watermark can be recovered, even if the attacker tries to manipulate the watermark with the knowledge of the watermarking process. The process incorporates buyer specific watermarks within a single multimedia object, and the same multimedia object has different watermarks that differ from owner to owner. Therefore recovery of this watermark not only authenticates the particular owner of the multimedia object but also could be used to identify the buyer involved in the forging process. This is achieved after spatially dividing the multimedia signal randomly into a set of disjoint subsets (referred to as the image key) and then manipulating the intensity of these subsets differently depending on a buyer specific key. These buyer specific keys are generated using a secret permutation of error correcting codes so that exact keys are not known even with the knowledge of the error correcting scheme. During recovery process a manipulated buyer key (due to attack) is extracted from the knowledge of the image key. The recovered buyer key is matched with the exact buyer key in the database utilizing the principles of error correction. The survival of the watermark is demonstrated for a wide range of transformations and forging attempts on multimedia objects both in spatial and frequency domains. We have shown that quantitatively our watermarking survives rewatermarking attack using the knowledge of the watermarking process more efficiently compared to a spread spectrum based technique. The efficacy of the process increases in scenarios in which there exist fewer numbers of buyer keys for a specific multimedia object. We have also shown that a minor variation of the watermark insertion process can survive a "Stirmark" attack. By making the image key and the intensity manipulation proms specific for a buyer and with proper selection of error correcting codes, certain categories of collusion attacks can also be precluded.
Biomedical Image Analysis: Tracking

In biological and medical imaging applications, tracking objects in motion is a critical task. This book describes the state-of-the-art in biomedical tracking techniques. We begin by detailing methods for tracking using active contours, which have been highly successful in biomedical applications. The book next covers the major probabilistic methods for tracking. Starting with the basic Bayesian model, we describe the Kalman filter and conventional tracking methods that use centroid and correlation measurements for target detection. Innovations such as the extended Kalman filter and the interacting multiple model open the door to capturing complex biological objects in motion. A salient highlight of the book is the introduction of the recently emerged particle filter, which promises to solve tracking problems that were previously intractable by conventional means. Another unique feature of Biomedical Image Analysis: Tracking is the explanation of shape-based methods for biomedical image analysis....
3D Segmentation of the Prostate via Poisson Inverse Gradient Initialization

Accurate segmentation and volumetric assessment of the enlarged prostate is critical to assessment of cancer progression. Moreover, 3D segmentation is necessary for treatment in both radiotherapy and brachytherapy. We propose a 3D segmentation solution for ultrasound images of the prostate based on deformable surfaces. The deformable surfaces are propelled by the vector field convolution (VFC) external force model. This external force has both computational efficiency and solution quality advantages over existing techniques such as gradient vector flow (GVF). A salient aspect of the segmentation solution proposed here is the ability to automatically initialize the deformable surface in 3D. The initialization method exploits a novel Poisson inverse gradient technique that essentially solves the inverse problem from the external force field to the external energy and determines the most likely coarse segmentation. We validate our 3D segmentation on simulated images of the prostate. Furthermore, simulated data show that PIG initialization leads to a 60% reduction in segmentation error for high curvature contours.
Classifying images in a histopathological dataset using the cumulative distribution transform on an automata architecture

The Automata Processor is a novel hardware accelerator that enables parallel implementation of classification in large datasets. To date, this pattern matching has been limited to one-dimensional problems that can be implemented as flexible string-matching methods such as those found in genomics. To map our multi-dimensional tissue image classification problem into a string matching processor, we first transform discriminative feature descriptors using a cumulative distribution transform. The transformed features are encoded into regular expressions which can be executed on the automata. The paper highlights a method of evaluating the similarity between images using these regular expressions in the automata processor. Our image retrieval and classification method improves on classification accuracy and achieves a run-time of less than one one-hundredth of a second per image which represents a three-fold improvement over competing architectures.
Detection and tracking of rolling leukocytes from intravital microscopy

We present a method for detecting and tracking rolling leukocytes from intravital video microscopy. The paper emphasizes a new method for detecting cells that is based on finding ellipses that have maximal outward normal gradient and minimal gradient variance around the boundary. A fast convolution-based implementation of the detector is discussed. For tracking, we present a radial active model that exploits a gradient vector flow field tailored to tracking objects in the direction of blood flow. Results are presented that compare the detection performance and tracking performance to that of existing methods.
A meta-algorithm for classification by feature nomination

With increasing complexity of the dataset it becomes impractical to use a single feature to characterize all constituent images. In this paper we describe a method that will automatically select the appropriate image features that are relevant and efficacious for classification, without requiring modifications to the feature extracting methods or the classification algorithm. We first describe a method for designing class distinctive dictionaries using a dictionary learning technique, which yields class specific sparse codes and a linear classifier parameter. Then, we apply information theoretic measures to obtain the more informative feature relevant to a test image and use only that feature to obtain final classification results. With at least one of the features classifying the query accurately, our algorithm chooses the correct feature in 88.9% of the trials.
External forces for active contours via multi-scale vector field convolution

In this paper we present a new method to generate the external force field for the active contour. The new approach, multi-scale vector field convolution (MVFC), is based on the vector field convolution (VFC) method, and extends VFC to a multi-scale process. Via automatic scale selection, the proposed method constructs the external force field used by the active contour by varying the convolution kernel width according to local image structure. The result is an adaptive force generation method that accommodates detailed, high curvature features with a large capture range, and demonstrates improved performance in terms of local convergence and multi-target segmentation over traditional VFC. Meanwhile, the new method preserves the low computation complexity as supported by VFC and exemplifies as a more efficient force generation method than gradient vector flow (GVF). Synthetic and real experiments demonstrate the efficacy of MVFC in terms of detail preservation, capture range, noise resilience and computational cost.
Graph-based classification of healthcare provider activity

Prior research demonstrates environmental reservoirs may play a role in the transmission of antibiotic-resistant bacteria within a hospital. Using video monitoring, we propose a technique based on graph Laplacian spectral analysis to classify human activities around sinks. The extracted feature-vectors are applied to a support vector machine to characterize different events in each video. Results are promising, demonstrating the potential of graph Laplacian spectral decomposition for the vision-based analysis of human activity. The activities mined from the videos in correlation with localized statistics of infections could lead to an understanding of the link between infection transmission and healthcare activities.
Morphological image segmentation by local monotonicity

Image segmentation is fundamental task in image processing and multimedia. We propose a general method for image segmentation based upon the relationship between mathematical morphology and the local monotonicity of signals. In this paper, we introduce a two-dimensional generalization of the concept of local monotonicity based on mathematical morphology. A multiscale representation of an image is morphologically generated, wherein the degree of local monotonicity determines scale. Then, a morphological edge detection operator exploits the monotonicity property in performing segmentation. The segmentation allows specification of object scale, edge detail, and contrast and is applicable to object-based video coding.
Automatic detection of direct radiation for digital fluoroscopy optimization

We present a histogram-based real-time solution to detecting directly irradiated regions in digital fluoroscopic images. Our method leverages the power of model matching, machine learning and domain knowledge to characterize and segment images using histograms. The input image is automatically identified as containing partial, all, or null direct radiation. The regions with direct radiation are segmented out via global thresholding according to image characterizations. The algorithm involves only one-dimensional processing. The test results achieved 99.82% accurate detection rate on a dataset of 9256 clinical images.
Registration of Surfaces to 3D Images Using Rigid Body Surfaces

We propose a constrained active surface model, called the rigid body surface, that deforms via rigid body transformation. In each iteration, the surface is deformed first via partial differential equations and then the least-squares estimate of the rigid body transformation between the deformed surface and predefined surface is calculated. The rigid body transformation estimate is based upon the singular value decomposition. Experimental results on surface-to-3D image and surface-to-surface registration illustrate the insensitivity of the rigid body surface to noise and initialization. In addition to registration, rigid body surfaces can be employed for image segmentation and tracking when the target shape is predefined.
Document page segmentation using multiscale clustering

The paper details a multiscale clustering technique for document page segmentation. In contrast to existing hierarchical (coarse-to-fine), multi-resolution methods, this image segmentation technique simultaneously uses information from different scaled representations of the original image. The final clustering of image segments is achieved through a fuzzy c-means based similarity measure between vectors in scale space. The segmentation process reduces the effects of insignificant detail and noise. Furthermore, object integrity is preserved in the segmentation process.
A Mean Field Solution to Anisotropic Edge Detection of Remotely Sensed Images

A new edge detection technique based on anisotropic diffusion is implemented using mean field theory. This optimization approach to edge detection provides localized, thinned, and linked edges that are useful for image segmentation and edge-based image registration algorithms. The method is well-suited for remote sensing applications that depend on accurate boundary detection, multiple region delineation, and high speed parallel implementation.
Active contour segmentation guided by AM-FM dominant component analysis

For the first time, we explore the application of active contours in the modulation domain by computing snakes on image modulations. As we demonstrate in the examples, such snakes are able to utilize information inherent in the dominant image modulations to acquire and track visually and semantically meaningful structures within the image. We use nonlinear AM-FM image representations to capture regions that are homogeneous in intensity and in texture. A geometric snake approach utilizing a fuzzy classifier is then applied to the image modulations. The combination of AM-FM analysis and the active contour evolution produces an efficacious image partition. As a preliminary demonstration of this novel approach, we apply the modulation domain snakes to the classical texture segmentation problem.
Morphological pyramids for multiscale edge detection

In this paper, we present an edge detector that is based on a morphological pyramid (MP) structure. The algorithm utilizes a multiresolution pyramidal structure created by successive morphological filtering and subsampling of the original image. The boundaries detected at coarse scale representations of the morphological pyramid (MP) are used to guide the detection of discontinuities at higher resolution levels. The segmentation and resulting edge detection yielded by the MP is particularly effective in the presence of impulse noise. We provide results that demonstrate superior solution quality over standard fixed resolution detectors and over previous multiresolution approaches. Because of the low computational cost of the MP edge detector, it is suitable for video tracking, image and video compression, and real-time object recognition.
Accelerated randomized blind source suppression of quasi-static reverberant artifacts in echocardiography

Echocardiographic image sequences are frequently corrupted by quasi-static “clutter” artifacts superimposed on the dynamic myocardium. Blind Source Separation (BSS) aIgorithms are often used to suppress clutter while retaining underlying tissue. In this paper, the sparse nature of reverberant artifacts in the time frequency domain is exploited for suppression to achieve reduced computational complexity and tissue signal loss. The performance of the method was validated on five datasets simulated in Field-II and three in vivo datasets and benchmarked with respect to the Singular Value Filter (SVF) algorithm. In simulated data, the proposed method yields a normalized average error of 1.15±0.09 a.u. over five simulated datasets, compared to the SVF, which yields an error of 14.13±1.98 a.u. on the same datasets. The suppressed signal energy was also estimated to quantify loss of `Tissue signal'. The average energy of the suppressed signal for the SVF was 35.60±3.27 a.u. The average energy of the suppressed signal of the proposed method was 25.44±1.95 a.u. Incorporating sparsity and signal characteristics allowed us to accelerate the clutter suppression by three orders of magnitude in this case.
A content based retrieval engine for circuit board inspection

In this paper we apply content based retrieval (CBR) to the automated inspection of printed circuit boards. Manual probing of faulty circuit boards is expensive and time consuming for electronics manufacturers. The proposed CBR system allows groups of similarly faulted boards to be identified and repaired simultaneously. We improve upon current matching techniques used in CBR, exploiting relative pairings of features rather than simple distance measures. The novel matching techniques are seen to improve upon traditional approach in cases of varying background conditions, environmental factors and inter-chip interactions.
NeuroSoL: Automated classification of neurons using the sorted Laplacian of a graph

One of the most exciting open problems in biological image analysis is the categorization of neurons based on observed morphology. Complex arborization patterns, varied orientations, different sizes, nonidentical alignments of local branches act as barriers to a systematic and quantitative analysis of neurons. This open problem demands a refined approach to capture salient and robust, global and local features to compare digitally-traced 3D neuron atlases. We propose NeuroSoL to consolidate global as well as local morphological and geometrical features by leveraging a specialized graph framework. Specifically, without imposing any restriction on the structure and connectivity, we use fully-sorted and mixed-sorted Laplacians of the neuron graph and the corresponding complementary graph. For meaningful clustering of neurons, we also develop a similarity metric after retrieving optimal local alignment between the feature descriptors of two neurons. Results on neuron datasets show the efficacy of our approach over state-of-the-art techniques.
Automatic segmentation of Drosophila neurons for content based retrieval using a minimum spanning tree approach

A critical part in our knowledge of the brain is the understanding of the structure and morphology of neurons. In recent years, we have witnessed a tremendous effort in developing libraries of neuronal structures that can be used for multiple purposes. These critical studies include modeling the brain connectivity and understanding how cellular structure regulates brain function. Presently, methods for tracing neuronal structures from microscopy images of neurons are not only tedious, but prone to user bias; hence they are not suitable for practical purposes. State of the art automatic neuron tracing algorithms fail to give satisfactory results for neuron images with low contrast, discontinuous filaments, and complex branching. In this paper, we develop Tree2Tree, an automatic neuron segmentation and morphology generation algorithm. Our algorithm uses a global tree linking strategy to connect smaller medial trees of visible components of the neuron in order to give an optimal explanation of the brightness patterns of the neuron branches. We intend to utilize these graph-based segmentation results in the retrieval of similar neurons from an extensive database of segmented neurons. We have tested our algorithm on confocal microscopy images of Drosophila neurons and our segmentation results are congruent with (within ±5.3 pixel RMSE) the manually obtained ground truth.
Segmentation of ultrasound images for phlebotomy applications

A global solution to the enhancement and segmentation of ultrasound images is proposed that is operable in both low contrast and high contrast imaging scenarios. The solution is based on two optimization processes: one that minimizes error with respect to the original image while minimizing the number of edge contours (above the number expected by the known topology), and the second that maximizes edge fidelity. The maximization of edge fidelity is achieved by way of connected filters that operate on connected components of a threshold-decomposed image. To test the algorithm, an application in the ultrasound imaging of human blood vessels is explored. The results show significant improvements (8X to 50X) in a vesselness measure of the segmented vessels as compared to that yielded by a traditional speckle reduction technique and to a diffusion-based technique.
Tracking fast-rolling leukocytes in vivo with active contours

We propose and demonstrate an active contour technique to track fast-rolling leukocytes observed in vivo from video microscopy. A rolling leukocyte is an activated white blood cell that interacts with the vessel wall (the endothelium) in the inflammatory process. Tracking is enhanced here to accommodate fast-moving cells. To tackle the task of tracking wherein only low temporal resolution is possible, we have introduced an energy-minimizing framework and obtained a partial differential equation (PDE) based active contour evolution technique. The proposed PDEs are shown to be an initialization-insensitive version of the gradient vector flow (GVF) proposed by Xu and Prince (1998). We modify the GVF-PDEs by adding a Dirichlet type boundary condition (BC) based on the initial position of the active contour and the direction of cell movement. Using actual intravital experiments, we compare the performance of the proposed active contour tracker with the Dirichlet BC, the active contour tracker without the BC, the correlation tracker and the centroid tracker. The comparative results provide evidence of the advantages of the proposed method in terms of increased number of frames successfully tracked and reduced localization error.
2-D binary locally monotonic regression

We introduce binary locally monotonic regression as a first step in the study of the application of local monotonicity for image estimation. Given an algorithm that generates a similar locally monotonic image from a given image, we can specify both the scale of the image features retained and the image smoothness. In contrast to the median filter and to morphological filters, a locally monotonic regression produces the optimally similar locally monotonic image. Locally monotonic regression is a computationally expensive technique, and the restriction to binary-range signals allows the use of Viterbi-type algorithms. Binary locally monotonic regression is a powerful tool that can be used in the solution of image estimation, image enhancement, and image segmentation problems.