nils-herrmann/clean_scirepeval huggingface dataset:
An algorithm for detecting multiple salient objects in images via adaptive feature selection

This paper presents an algorithm for detecting multiple salient objects in images. The algorithm extends our previous algorithm which was designed to detect only a single salient object. The new algorithm employs five feature maps (lightness distance, color distance, contrast, sharpness, and edge strength), along with a new image-adaptive technique for estimating the usefulness of each feature map based on a local measure of cluster density. As we will demonstrate, our new version can successfully detect multiple salient objects on images for which the previous version did not succeed. Testing on subsets of images from two databases shows that the proposed algorithm performs well on a variety of images containing multiple salient objects.
Digital Image Watermarking via Adaptive Logo Texturization

Grayscale logo watermarking is a quite well-developed area of digital image watermarking which seeks to embed into the host image another smaller logo image. The key advantage of such an approach is the ability to visually analyze the extracted logo for rapid visual authentication and other visual tasks. However, logos pose new challenges for invisible watermarking applications which need to keep the watermark imperceptible within the host image while simultaneously maintaining robustness to attacks. This paper presents an algorithm for invisible grayscale logo watermarking that operates via adaptive texturization of the logo. The central idea of our approach is to recast the watermarking task into a texture similarity task. We first separate the host image into sufficiently textured and poorly textured regions. Next, for textured regions, we transform the logo into a visually similar texture via the Arnold transform and one lossless rotation; whereas for poorly textured regions, we use only a lossless rotation. The iteration for the Arnold transform and the angle of lossless rotation are determined by a model of visual texture similarity. Finally, for each region, we embed the transformed logo into that region via a standard wavelet-based embedding scheme. We employ a multistep extraction stage, in which an affine parameter estimation is first performed to compensate for possible geometrical transformations. Testing with multiple logos on a database of host images and under a variety of attacks demonstrates that the proposed algorithm yields better overall performance than competing methods.
Performance-analysis-based acceleration of image quality assessment

Two stages are commonly employed in modern algorithms of image/video quality assessment (QA): (1) a local frequency-based decomposition, and (2) block-based statistical comparisons between the frequency coefficients of the reference and distorted images. This paper presents a performance analysis of and techniques for accelerating these stages. We specifically analyze and accelerate one representative QA algorithm recently developed by the authors (Larson and Chandler, 2010). We identify the bottlenecks from the abovementioned stages, and we present methods of acceleration using integral images, inline expansion, a GPGPU implementation, and other code modifications. We show how a combination of these approaches can yield a speedup of 47×.
Spatially-Adaptivewavelet Image Compression via Structural Masking

Wavelet-based spatial quantization is a technique to compress image data that adapts the compression to the data in each region of an image. This approach is motivated because quantization with a single step-size does not result in a uniform visual effect across each spatial location; different types of image content mask quantization errors in different ways. While many spatial quantization techniques determine step-sizes via local activity measures, the proposed method induces local quantization distortion based on experiments that quantify human detection of this distortion as function of both the contrast and the type of the image data. Three types in particular, textures, structures and edges, are considered. A classifier is utilized to detect to which of these three categories a local region of image data belongs, and step-sizes are then derived based on the contrast and class of each region. Class and contrast data are conveyed to the coder with explicit side information. For images compressed at threshold, the proposed method requires 3-10 % less rate than a similar previous approach without classification, and on average produces images that are preferred by 2/3 of tested viewers.
A Bayesian approach to predicting the perceived interest of objects

This paper presents an algorithm designed to compute the perceived interest of objects in images. We measured likelihood functions via a psychophysical experiment in which subjects rated the perceived visual interest of 562 objects in 150 images. These results were then used to determine the likelihood of perceived interest given various factors such as location, contrast, color, and edge-strength. These likelihood functions are used as part of a Bayesian formulation in which perceived interest is inferred based on the factors. Our results demonstrate that our algorithm can perform well in predicting perceived interest.
S3: A Spectral and Spatial Sharpness Measure

This paper presents a block-based algorithm designed to measure the local perceived sharpness in an image. Our method utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then the adjusted measures are combined via a weighted geometric mean. The resulting measure, S3 (Spectral and Spatial Sharpness), yields a perceived sharpness map in which greater values denote perceptually sharper regions. This map can be collapsed into a single index which quantifies the overall perceived sharpness of the whole image. We demonstrate the utility of the S3 measure for within-image and across-image sharpness prediction, for global blur estimation, and for no-reference image quality assessment of blurred images.
No-Reference Quality Assessment of JPEG Images via a Quality Relevance Map

This letter presents a no-reference quality assessment algorithm for JPEG compressed images (NJQA). Our method does not specifically aim to measure blockiness. Instead, quality is estimated by first counting the number of zero-valued DCT coefficients within each block, and then using a map, which we call the quality relevance map, to weight these counts. The quality relevance map for an image is a map that indicates which blocks are naturally uniform (or near-uniform) vs. which blocks have been made uniform (or near-uniform) via JPEG compression. Testing on various image-quality databases demonstrates that NJQA is either competitive with or outperforms modern competing methods on JPEG images.
Improving coherence estimation for high-resolution polarimetric SAR interferometry

This work presents a new method for filtering the coherence map issued from Synthetic Aperture Radar (SAR) polarimetric interferometric data. For each pixel of the interferogram, an adaptive neighborhood is determined by a region growing technique driven by the amplitude image information. Then, pixels in the derived adaptive neighborhood are complex averaged to yield the filtered value of the coherence, after performing a phase compensation step. The proposed method has been applied on airborne high-resolution polarimetric interferometric SAR images. Both subjective and objective performance analysis, including coherence edge detection, shows that the proposed method provides better results than the standard phase-compensated fixed multi-look filter and a linear adaptive coherence filter proposed by Lee et al.
Automatic segmentation of infantile hemangiomas

Infantile hemangiomas are the most common types of tumors that are found in infants and have an incidence of approximately 10% in the common population. Although most infantile hemangiomas are self-involuting, due to their fast proliferation they may threaten vital anatomical structures and physiological functions; also, the involution process may take up to several years. An accurate monitoring of the progress of hemangioma growth and regression is essential. We thus suggest using a computer aided follow-up monitoring of these lesions by an automatic detection and quantifying the lesion dynamic: regression or proliferation. In this study we propose some image enhancements methods and also a preliminary color based segmentation. We have tested our methods on 25 hemangioma cases and compared the automated segmentation results with clinician-determined segmentation using an area percentage error.
Automatic segmentation of infantile hemangiomas within an optimally chosen color space

Infantile hemangiomas (IH) are benign vascular tumors, most of them appearing in the first weeks and developing until six months of age. The evaluation of the lesion size is usually made by the physician through manual measurement, which is inaccurate. This paper presents an algorithm for the automatic segmentation of the hemangioma region, relying on the Maximum a Posteriori (MAP) classification method. The segmentation result is improved by regularization with discrete Markov fields (MAP-Markov). Then, a further improvement is performed, by eliminated distant non-hemangioma pixels. The optimal color space is chosen before segmentation, from five different color spaces, by iteratively computing the segmentation error 10 times on each color space and each of the 40 images from the database. The segmentation performance is evaluated in terms of border error.
Color image edge detection by parallel coordinates-based color distance

Although simple and based on physical realizable color primaries, the RGB color space cannot allow the direct definition of a topology-preserving, perceptually-compatible inter-color distance. The classical normalized inter-color distances are rather complex to implement and tune. We propose the use of a planar color representation plot, derived from multivariate data visualization using parallel coordinates, which allows the introduction of a luminance-invariant, geometry-based inter-color distance. We show that the new distance is used with good results in the implementation of distance-based color filters and edge detectors.
Performance testing and functional limitations of Normalized Autobinomial Markov Channels

The field of pedestrian detection has gained momentum in recent years, due to a large range of applications, including advanced robotics, aided surveillance and automotive safety. Its importance in the field of computer vision is confirmed by the large number of available algorithms, as well as the increased complexity of the public databases used for testing. To comply with the increased demands of the field, we perform extensive performance testing of the proposed Normalized Autobinomial Markov Channels (NAMC) algorithm using the Caltech Pedestrian Dataset. The proposed solution aims at isolating easily distinguishable body characteristics by learning contextual probabilistic dependencies. The functional limitations of the algorithm are derived by separately analyzing three test scenarios: scale, occlusion, aspect ratio. The obtained results demonstrate the efficiency of our approach, especially in the case of heavy occlusions, where the algorithm ranks first among the tested state-of-the art solutions.
Detection of mammographic microcalcifications using a refined region-growing approach

Detection and characterization of microcalcification clusters in mammograms is vital in daily clinical practice. The problem of detecting possible cancer areas is very complex due, on one hand, to the diversity in shape of the ill tissue and, on the other hand, to the poorly defined border between the healthy and the cancerous zone. Even though it has been studied for many years, there are still remaining challenges and directions for future research, such as developing better enhancement and segmentation algorithms. In this paper, we propose a microcalcification detection algorithm using an extended region growing technique in a mammography CAD (computer-aided diagnosis) system.
Automatic pap smear nuclei detection using mean-shift and region growing

The Babes-Papanicolaou test (also known as Pap smear) is a method of cervical cancer screening used to detect abnormal cells which are or can become cancerous. Since the visual inspection of pap smears is very time consuming, the need for automatic methods is required. This paper presents an algorithm for the automatic detection of nuclei within pap smears images. The algorithm relies in the highly effective mean-shift filtering method which enhances the contrast of nuclei areas. The segmentation consists of a region growing with starting points taken from the image gradient map. Size and eccentricity measures are used to keep only nuclei from the segmented regions. The method is validated on two different pap smear test databases and the detection rate is above 91%.
Automatic monitoring system for the detection and evaluation of the evolution of hemangiomas

In this paper we introduce an automatic monitoring system for the detection and the evaluation of the evolution of hemangiomas using a fuzzy logic system based on two parameters: area and redness. We have considered pairs of images (from two different moments in time) that show hemangiomas either evolving, stationary or regressing. The starting points of the algorithm are the rectangular regions of interest (ROI), manually selected for each of the two images, and automatically segmented using Fuzzy C-means. Using the area and the redness of the hemagiomas extracted with Fuzzy C-means, for the same patient, at different moments of time, the algorithm decides whether the hemangioma is evolving, stationary or regressing. Because it is also useful to understand how the tumor's shape is changing in time, we have also included a method of matching and overlapping the hemangioma regions.
On the influence of image enhancement on fractal-based automatic osteoporosis detection from calcaneum x-rays

The goal of this research is to investigate the effectiveness of various classical automatic image enhancement methods in the framework of calcaneum bone characterization in digitally-acquired x-ray images. The decision on the presence or absence of osteoporosis signs is taken within a nearest neighbor decision rule by using fractal parameters, such as the fractal dimension. A successful image processing chain (acquisition, enhancement and analysis) could lead to an automatic scoring procedure of the bone degradation in the diagnosis of osteoporosis or other bone-modifying diseases, by using cheap imaging equipment, such as consumer digital cameras and general-purpose computers.
Hand posture recognition using the intrinsic dimension

This paper presents a hand posture recognition algorithm based on the intrinsic dimension (ID). Hand segmentation is reliable with cluttered background due to the use of the time-of-flight (ToF) range camera. The hand pose recognition is performed using an image indexing technique with a reference database of 360 images. The database and the query images consist of nine types of hand postures. The similarity measure used for finding the best match with the query image is the correlation between the 1D representations of the 2D ID hand contours. The average recognition rate is above 96%, which is satisfactory.
Measurements with ToF Cameras and Their Necessary Corrections

The most important characteristic of time-of-flight (ToF) cameras is the ability to measure the distance to each image pixel. Thus, for each pixel, information on both its amplitude and distance to the camera are available. However, technological problems inherent to the acquisition principle lead to inaccuracies in estimating both characteristics: on one hand, there are errors in estimating the distance, especially for far-distance pixels. On the other hand, the detected amplitude decreases with the distance. Part of these inaccuracies are corrected with special camera-calibration software. In this paper, we propose two methods that attempt to further correct each information based on the other one. First, the amplitude image is enhanced by using distance information: a pixel-wise, distance-based correction of the amplitude brings to light details otherwise unnoticeable. Secondly, an amplitude-based distance modification corrects some of the distance estimation errors for far-distance pixels.
Facial enhancement and beautification for HD video cameras

Influenced by the widespread adoption of HDTV consumer imaging devices have begun to feature full HD (high density) video capture. With this high-resolution capability, small imperfections in human faces are captured with full HD video. When used for portrait imaging, the resulting video is frequently unsatisfactory to the user. Manufacturers of imaging devices require practical, real-time solutions which can mitigate facial details emphasized by the HD resolution, while preserving the overall quality of the portrait images. The high bandwidth and processing requirements of full HD video make this a challenging task. In this paper, a practical algorithm, now incorporated in a number of consumer devices is explained. Major challenges and their solutions are presented. Attention is given to real-time optimizations of the algorithm which is designed with a view to its suitability for partial or full hardware implementation.
Removal of artifacts from dermatoscopic images

Dermoscopy is the primary tool used for pigmented skin lesion diagnosis. Despite the use of this relative new clinical method dermoscopy based, diagnose is still subjective and the diagnosis detection accuracy is about 75-80%. In this paper we present several enhancement pre-processing techniques applied on dermatoscopic images, such as black frame removal and hair removal in an automatically manner. We have tested our algorithms on 45 dermoscopic images and compared the automated enhancement methods results with other existing methods.
Automatic detection of cervical cells in Pap-smear images using polar transform and k-means segmentation

We introduce a novel method of cell detection and segmentation based on a polar transformation. The method assumes that the seed point of each candidate is placed inside the nucleus. The polar representation, built around the seed, is segmented using k-means clustering into one candidate-nucleus cluster, one candidate-cytoplasm cluster and up to three miscellaneous clusters, representing background or surrounding objects that are not part of the candidate cell. For assessing the natural number of clusters, the silhouette method is used. In the segmented polar representation, a number of parameters can be conveniently observed and evaluated as fuzzy memberships to the non-cell class, out of which the final decision can be determined. We tested this method on the notoriously difficult Pap-smear images and report results for a database of approximately 20000 patches.
Automatic detection of hemangiomas using unsupervised segmentation of regions of interest

In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing the performances of each method, the ROIs had been manually labeled in 2 classes: pixels of hemangioma and pixels of non-hemangioma. The computed scores are given separately for each image, as well as global performances across all ROIs for both classes. The best classification of non-hemangioma pixels is obtained by FCM (90.98%), but RG-FCM offers the best classification for hemangioma pixels (91.51%) and also the best overall performance taking into account both classes (90.27%).
Adaptive-neighborhood best mean rank vector filter for impulsive noise removal

Rank-order based filters are usually implemented using reduced ordering, since there is no natural way to order vector data, such as color pixel values. This paper proposes a new statistics for multivariate data which is a mean rank obtained by aggregating partial ordering ranks. This statistics is then used for the reduced ordering of vector data; the median statistic is characterized by the best mean rank vector (BMRV). We devise two filtering structures based on the BMRV statistics: one that uses a classical square neighborhood, and one which is based on adaptive neighborhoods. We show that the proposed filters are highly effective for filtering color images heavily corrupted by impulsive noise, and compare favorably to state-of-the-art filtering structures.
A new approach in breast image registration

According to the World Health Organization, breast cancer is the most common cancer suffered by women in the world, which during the last two decades, has increased the women mortality in developing countries. Mammography is the best method used for the screening; the problem of detecting possible cancer areas is very complex due, on one hand, to the diversity in shape of the ill tissue and, on the other hand, to the poorly defined border between the healthy and the cancerous zone. An automated technique for the alignment of right and left breast images has been developed for use in the computerized analysis of bilateral breast images. Using this technique, the breast region is firstly identified by using an adaptive thresholding algorithm. The focus is on determining control points in the two mamograms; these points are used to put the two mammograms into correspondence. The algorithm's performance was evaluated on a large number of difficult cases and found to be adequate.
Automatic evaluation of hemangiomas for follow-up monitoring

Infantile hemangiomas are the most common types of tumors with an incidence of approximately 10% in the common population. An accurate monitoring of the progress of hemangioma growth and regression is essential for an effective treatment. This study presents an automatic evaluation of the evolution of hemangioma on a follow-up series of images based on color and area features. A color constancy approach is applied to correct the variation of ambient illumination. The hemangioma segmentation uses a two-level thresholding approach with some post-processing methods. The proposed method has been tested on 25 hemangioma cases annotated by clinicians.
Automatic measurement of infantile hemangiomas

Infantile hemangiomas (IH) are a type of benign vascular tumors that appear within the first 5 months of life. The assessment of lesion size and its evolution in time is done manually by the physician, using a ruler, and this measurement is not very accurate. This paper presents a method for automatic measurement of the IH size. The work is divided in two parts: automatic computation of the size of one millimeter in pixels, based on the Hough transform and the total variation, and automatic segmentation based on K-means clustering and a 2D total variation filtered image. The segmentation performance was evaluated on 20 IH images and a mean border error of 13.56% was obtained.