nils-herrmann/clean_scirepeval huggingface dataset:
GPU-accelerated local tone-mapping for high dynamic range images

This paper presents a very fast local tone mapping method for displaying high dynamic range (HDR) images. Though local tone mapping operators produce better local contrast and details, they are usually slow. We have solved this problem by designing a highly parallel algorithm, which can be easily implemented on a Graphics Processing Unit (GPU) to harvest high computational efficiency. At the same time, the proposed method mimics the local adaption mechanism of the human visual system and thus gives good results for a wide variety of images.
Segmenting overlapping cell nuclei in digital histopathology images

Automatic quantification of cell nuclei in immunostained images is highly desired by pathologists in diagnosis. In this paper, we present a new approach for the segmentation of severely clustered overlapping nuclei. The proposed approach first involves applying a combined global and local threshold method to extract foreground regions. In order to segment clustered overlapping nuclei in the foreground regions, seed markers are obtained by utilizing morphological filtering and intensity based region growing. Seeded watershed is then applied and clustered nuclei are separated. As pixels corresponding to stained cellular cytoplasm can be falsely identified as belonging to nuclei, a post processing step identifying positive nuclei pixels is added to eliminate these false pixels. This new approach has been tested on a set of manually labeled Tissue Microarray (TMA) and Whole Slide Images (WSI) colorectal cancers stained for the biomarker P53. Experimental results show that it outperformed currently available state of the art methods in nuclei segmentation.
An efficient example-based approach for image super-resolution

A novel algorithm for image super-resolution with class-specific predictors is proposed in this paper. In our algorithm, the training example images are classified into several classes, and each patch of a low-resolution image is classified into one of these classes. Each class has its high-frequency information inferred using a class-specific predictor, which is trained via the training samples from the same class. In this paper, two different types of training sets are employed to investigate the impact of the training database to be used. Experimental results have shown the superior performance of our method.
Learning local pixel structure for face hallucination

In this paper, we present a novel learning-based face hallucination method based on the assumption that similar faces will have similar local pixel structures. We use the low- resolution (LR) input face to search a database for K example faces that are the most similar to the input and align them with the input accordingly. The local pixel structures of the target high-resolution (HR) image are learned from those warped HR example faces in a neighbor embedding manner, and a total variation (TV) constraint is employed to aid the learning of all pixels' embedding weights. The learned local pixel structures are then used as constraints to reconstruct a HR version of the input face. Experimental results show that the method performs well in terms of both reconstruction error and visual quality.
Spectral Images and Features Co-Clustering with Application to Content-based Image Retrieval

In this paper, we present a spectral graph partitioning method for the co-clustering of images and features. We present experimental results, which show that spectral co-clustering has computational advantages over traditional k-means algorithm, especially when the dimensionalities of feature vectors are high. In the context of image clustering, we also show that spectral co-clustering gives better performances. We advocate that the images and features co-clustering framework offers new opportunities for developing advanced image database management technology and illustrate a possible scheme for exploiting the co-clustering results for developing a novel content-based image retrieval method
Frequency layered color indexing for content-based image retrieval

Image patches of different spatial frequencies are likely to have different perceptual significance as well as reflect different physical properties. Incorporating such concept is helpful to the development of more effective image retrieval techniques. We introduce a method which separates an image into layers, each of which retains only pixels in areas with similar spatial frequency characteristics and uses simple low-level features to index the layers individually. The scheme associates indexing features with perceptual and physical significance thus implicitly incorporating high level knowledge into low level features. We present a computationally efficient implementation of the method, which enhances the power and at the same time retains the simplicity and elegance of basic color indexing. Experimental results are presented to demonstrate the effectiveness of the method.
Color image indexing using BTC

This paper presents a new application of a well-studied image coding technique, namely block truncation coding (BTC). It is shown that BTC can not only be used for compressing color images, it can also be conveniently used for content-based image retrieval from image databases. From the BTC compressed stream (without performing decoding), we derive two image content description features, one termed the block color co-occurrence matrix (BCCM) and the other block pattern histogram (BPH). We use BCCM and BPH to compute the similarity measures of images for content-based image retrieval applications. Experimental results are presented which demonstrate that BCCM and BPH are comparable to similar state of the art techniques.
Crowdsourcing based radio map anomalous event detection system for calibration-on-demand

Calibrations for Wi-Fi Indoor Positioning Systems (IPS) are expensive and time-consuming. Recalibrations are necessary periodically to compensate the drifts of radio map. Many crowdsourcing-based approaches have been proposed to calibrate the radio map strategically with user-contributed data. However, these approaches still suffer from several drawbacks. Calibration-on-demand is desirable to reduce the maintenance cost and to enhance the reliability of the Wi-Fi IPS. Wi-Fi IPS degrades only when the radio map changes significantly and lastingly. Such changes are caused by radio map anomalous events. Examples of these events include changes in indoor environment, existence of spurious data from the sensing system and unexpected failure in utility infrastructures. This paper presents a theoretical and experimental study of a crowdsourcing online Wi-Fi radio map anomalous event detection system. Detection of these events will enhance the operation intelligence of Wi-Fi IPS and enable high-level context-aware services. This system is consisted of an outlier detector for abnormal signal identification and an event discriminator for determination of event occurrence. Real world smartphone data and simulated data are used to test the performance of the system. Initial results show that the system is able to detect over 92% of simulated events and 87.5% of real world events with a low false alarm rate.
Tree partition voting min-hash for partial duplicate image discovery

Discovering partially duplicated images such as those of the same scenes, buildings or objects taken from different angles, distances and vantage points can be very useful in applications such as managing large image repositories and image search on the Internet. In this paper, we present a novel technique for partial duplicate image discovery. The new technique, termed tree partition voting min-hash (TmH), first partitions interest points within an image based on their geometric or photometric (appearance) properties using a spatial partition tree data structure and then finds potential partial duplicate images through a traditional partition min-hash (PmH) method . We have developed a k-d tree partition min-hash (kdTmH) and a random projection tree partition min-hash (rpTmH) technique and have also developed a weighted voting algorithm for improving the similarity measure of a pair of hashing sketches. We present experimental results on 3 datasets and show that TmH significantly outperforms PmH in terms of recall and precision performances without increasing complexity and that the new voting algorithms performs better than sketch matching techniques in the literature.
An Information Theoretic Model of Spatiotemporal Visual Saliency

This paper presents a principled and practical method for the computation of visual saliency of spatiotemporal events in full motion videos. Based on the assumption that uniqueness or informative-ness correlates with saliency, our model predicts the saliency of a spatiotemporal event based on the information it contains. To compute the uniqueness of the spatiotemporal events, we model the joint spatial and temporal conditional probability distributions of the spatiotemporal events and compute their spatiotemporal saliencies in a natural and integrated framework. To make the information theoretic model practical, we have developed methods to simplify the model and computational process. Testing results on several video sequences demonstrate that our model is effective in predicting visually salient spatiotemporal events and is comparable to state of the art. It is expected that our principled and practical model will find widespread applications in multimedia content analysis and processing.
Display HDR Image using a Gain Map

In this paper, we present a novel method for the display of high dynamic range images. The new method first computes a gain map image using a computational approach inspired by a machine learning algorithm and sums the gain map and the original image together; it then linearly scales the sum image to fit the dynamic range of the display devices. Results are presented to demonstrate the effectiveness of this new method and it is also shown that the new approach is an effective method for enhancing standard (8 bits/pixl) images.
Tone Mapping for HDR Image using Optimization A New Closed Form Solution

This paper studies an optimization approach for designing tone reproduction curve (TRC) based tone mapping operators for the display of high dynamic range (HDR) images in low dynamic range (LDR) reproduction media. Previous work has shown that the tone mapping problem can be formulated as that of optimizing a two-term cost function where adjusting the relative weightings of the two terms allows users to interactively control the appearance of the output image. However, only heuristic solutions to the tone mapping objective function have been found in past research. The main contribution of this paper is the re-formulation of the objective function to allow the introduction of a closed-form solution to the two-term tone mapping objective function. The new solution has simplified previous heuristic solutions and made this approach mathematically more elegant, computationally faster and practically easier to implement
Automatic Visual Theme Discovery from Joint Image and Text Corpora

This paper presents an unsupervised visual theme discovery framework as a better (more compact and effective) alternative for semantic representation of visual contents. Firstly, a tag filtering algorithm was proposed focusing on the tag&#x2019;s ability of visual content description. Then a spectral clustering algorithm is applied to cluster tags into visual themes based on their visual similarity and semantic similarity measures. User studies have been conducted to evaluate the effectiveness and rationality of the discovered visual themes and obtain promising results. Additionally, two common computer vision tasks, example based image search and keyword based image search to explore potential applications of the proposed framework. The experimental results show that visual themes significantly outperform tags on semantic image understanding and achieve state-of-art performance inthese two tasks.
Tone mapping HDR images using optimization: A general framework

This paper presents a novel tone mapping framework. First, we introduce a tone mapping fidelity principle which explicitly stipulates that tone-mapped image data should not only be visually enhanced but should also stay faithful to the original image. Second, this principle naturally translates tone mapping into a constrained optimization problem where a two-term cost function, one measures the difference between the tone mapped image and a visually enhanced version of the image, and the other measures the difference between the tone mapped image and the original image, is optimized. The relative weightings of the two terms in the cost function not only offers an insightful and simple mechanism to control the appearance of the tone mapped image but also enables the introduction of spatially varying or uniform weighting functions thus unifying local and global tone mapping in a single framework. We present results of tone mapping high dynamic range (HDR) images and low dynamic range JPEG images to demonstrate the effectiveness of the new tone mapping framework.
An optimal tone reproduction curve operator for the display of high dynamic range images

We present a new tone mapping method for the display of high dynamic range images in low dynamic range devices. We formulate high dynamic range image tone mapping as an optimisation problem. We introduce a two-term cost function, the first term favours linear scaling mapping, the second term favours histogram equalisation mapping, and jointly optimising the two terms optimally maps a high dynamic range image to a low dynamic range image. We control the mapping results by adjusting the relative weightings of the two terms in the objective function. We also present a fast and simple implementation for solving the optimisation problem. We present results to demonstrate that our method works very effectively.
JPEG Error Analysis and Its Applications to Digital Image Forensics

JPEG is one of the most extensively used image formats. Understanding the inherent characteristics of JPEG may play a useful role in digital image forensics. In this paper, we introduce JPEG error analysis to the study of image forensics. The main errors of JPEG include quantization, rounding, and truncation errors. Through theoretically analyzing the effects of these errors on single and double JPEG compression, we have developed three novel schemes for image forensics including identifying whether a bitmap image has previously been JPEG compressed, estimating the quantization steps of a JPEG image, and detecting the quantization table of a JPEG image. Extensive experimental results show that our new methods significantly outperform existing techniques especially for the images of small sizes. We also show that the new method can reliably detect JPEG image blocks which are as small as 8 × 8 pixels and compressed with quality factors as high as 98. This performance is important for analyzing and locating small tampered regions within a composite image.
Modelling the visual cortex using artificial neural networks for visual image reconstruction

Presents an artificial neural network model with some special neurons that are designed to function as the feature detectors found in the visual cortex, and applies it to the reconstruction of visual images. The model is a multilayer feedforward neural network. The neurons in the first hidden layer of the network are feature detectors of various scales and orientations. The connection strengths between the input and the first hidden layer are pre-set (and fixed) such that the outputs of this layer are some visually important features of various scales. The rest of the connection strengths in the network are decided through learning via the backpropagation algorithm in a self-supervising manner. Computer simulations were conducted, and the results seem to show that the artificial neural network model proposed in this paper is consistent with its biological counterpart (the visual cortex) in terms of the visual features detected by its feature detectors and its ability to reconstruct near-perfect input images from the output of these feature detectors. It is also shown that the system can be used for effective image data compression. Simulation results are presented which show promising potential of the new system for image coding applications.
Integrating Low-level and Semantic Features for Object Consistent Segmentation

The aim of semantic segmentation is to assign each pixel a semantic label. Numerous methods for semantic segmentation have been proposed in recent years and most of them chose pixel or super pixel as the processing primitives. However, as the information contained in a pixel or a super pixel is not discriminative enough, the outputs of these algorithms are usually not object consistent. To tackle this problem, we introduce the concept of object-like regions as a new and higher level processing primitive. We first experimentally showed that using object-like regions as processing primitives can boost semantic segmentation accuracy, and then proposed a novel method to produce object-like regions by integrating state-of art low-level segmentation algorithms with typical semantic segmentation algorithms through a novel semantic feature feedback mechanism. We present experimental results on the publicly available image understanding database MSRC21 and show that the new method can achieve state of the art semantic segmentation results with far fewer processing primitives.
Adaptive reconstruction of subsampled colour images

Subsampling chrominance signals is a common practice used in colour imaging systems to reduce the storage and transmission requirements. The lossy nature of the process, however, inevitably introduces colour artifacts. In this paper, an adaptive learning system is developed for the reconstruction of full resolution colour images from subsampled signals. Simulation results are presented which show that the images produced by the new technique have fewer visible colour artifacts than those constructed by traditional techniques.
Colour image coding by visually meaningful image patterns

Visually meaningful patterns (visual patterns) are a set of predefined image blocks representing information that the human visual system is very sensitive to. A colour image coding technique based on visual patterns is introduced. An image is divided into small blocks each of which is mapped to one of the visual patterns. Each block can then be represented by the pattern and by the colour information of that block. Colour quantisation and entropy coding are applied to achieve efficient coding. High compression ratios (35:1-70:1) have been achieved while maintaining good image quality, comparable to that of state-of-the-art techniques such as JPEG. In addition, the encoded images can be used in their compressed state in content based image retrieval systems based on colour and shape features.
Frequency sensitive Hebbian learning

A new learning algorithm is proposed for the training of single layer linear networks. The network studied has an input layer of N units and an output layer of M units. The input and output layer are fully connected via an M/spl times/N weight matrix. It is well known that such a network of linear processing units will generate M principal components of the input distribution when it is trained by Hebbian type learning algorithms, such as general Hebbian algorithm (GHA). It is also known that the same network structure of winner-take-all (WTA) units will produce M cluster centres of the input space when it is trained by gradient based competitive learning, such as Kohonen learning. The new algorithm also uses a Hebbian type learning mechanism, but unlike the previous algorithms such as GHA, it simultaneously classifies the input distribution into M subclasses and extracts the principal component of each subclass distribution. To achieve robust performance, a frequency sensitive competitive learning mechanism is incorporated into the process, hence the new algorithm is called frequency sensitive Hebbian learning (FSHL). We have applied the new algorithm to image data compression applications and simulation results are presented which indicate that the new FSHL will consistently outperform the optimal Karhunen-Loeve transform (KLT) and is competitive to Kohonen networks.
Notice of RetractionLane-mark extraction by frequency-based saliency visual attention

Lane mark extraction and detection system gives vital information for many intelligent transportation systems. There are several active research projects on the system, but none of them is using frequency-based saliency techniques to extract lane regions for Driving Assistant Systems (DASs). In our research, two frequency-based saliency map building techniques, Phase Fourier Transform (PFT) and Quadrature Phase Fourier Transform (QPFT), are implemented in the system and compared with each other according to detection rate. Through the comparison, PQFT, which simultaneously processes on four different channel shows its advantages over PFT, which works on only one intensity feature. The offered methods are tested on real traffic scenes images from UNMC-VIER Autovision database and it could detect discrete lane marks accurately.
Collaborative and content-based image labeling

Many on-line photo sharing systems allow users to tag their images so as to support semantic image search. In this paper, we study how one can take advantages of the already-tagged images to (semi-)automate the labeling of newly uploaded ones. In particular, we propose a hybrid approach for the prediction where user-provided tags and image visual contents are fused under a unified probabilistic framework. Kernel smoothing and collaborative filtering techniques are explored for improving the accuracy of the probabilistic models estimation. By comparing with some state-of-the-art content-based image labeling methods, we have empirically shown that 1) the proposed method can achieve comparable tag prediction accuracy when there is no user-provided tag, and that 2) it can significantly boost the prediction accuracy if the user can provide just a few tags.
An optimal key frame representation for video shot retrieval

In this paper, we propose an optimal representation scheme to construct an optimal key frame based on global statistics for video retrieval. Each pixel in this optimal key frame is constructed by considering the probability of occurrence of those pixels at the corresponding pixel position among the frames in a video shot. Therefore, this constructed key frame is called the "temporally maximum occurrence frame" (TMOF), which is an optimal representation of all the frames in a video shot. The retrieval performance of this representation scheme is further improved by considering the k pixel values with the largest probabilities of occurrence and the highest peaks at each pixel position for a video shot. The corresponding schemes are called k-TMOF and k-pTMOF, respectively. These key frame representation schemes are compared to other histogram-based techniques for video shot representation and retrieval. Experimental results show that our proposed representations outperform the alpha-trimmed average histogram for video retrieval.