nils-herrmann/clean_scirepeval huggingface dataset:
Empirical modeling for colorimetric characterization of digital cameras

One of the most complete techniques that can be used to digitize an artefact is to generate its photo-textured 3D model, which combines high precision metrical information with a faithful color description of the object surfaces. In this work we focus on how to obtain reliable color information by colorimetrically characterizing the color sensors of the imaging device. To this end, a novel target based characterization procedure is proposed that exploits empirical polynomial modeling for colorimetric data estimation. Experimental results are reported and discussed.
Food Recognition: A New Dataset, Experiments, and Results

We propose a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications. Each image depicts a real canteen tray with dishes and foods arranged in different ways. Each tray contains multiple instances of food classes. The dataset contains 1027 canteen trays for a total of 3616 food instances belonging to 73 food classes. The food on the tray images has been manually segmented using carefully drawn polygonal boundaries. We have benchmarked the dataset by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class. We have experimented with three different classification strategies using also several visual descriptors. We achieve about 79% of food and tray recognition accuracy using convolutional-neural-networks-based features. The dataset, as well as the benchmark framework, are available to the research community.
Semantic segmentation of food images for automatic dietary monitoring

Food image analysis has been one of the most important tasks accomplished for automatic dietary monitoring. In this work, we address semantic segmentation of food images with Deep Learning. Additionally, we explore food and non-food segmentation by getting advantage of supervised learning. Specifically, we have experimented SegNet model on these two food-related computer vision tasks. Experimental results show that followed approach brings appealing results on semantic food segmentation and significantly advances on food and non-food segmentation.
Multispectral loss-less compression using approximation methods

The large size of multispectral data files is currently a major issue in multispectral imaging. The transmission of multispectral data over networks, as well as the storage of large archives, are strongly limited, so that a clear need for good compression methods arises. In this paper, we explore the possibility of loss-less compression for multispectral data through a number of approximation methods that operate on the spectral domain. To evaluate the performance of these methods, we apply them to a representative spectra database, and consider the corresponding decrease in information entropy as well as the classical file size ratio.
From Color Sensor Space to Feasible Reflectance Spectra

The interaction of light and object surfaces generates color signals in the visible band that are responsible for digital acquisition system outputs. Inverting this mapping from the sensor space back to the wavelength domain is of great interest for many applications. Since 1964, with the idea of Cohen to exploit the characteristic of smoothness of surface reflectance functions, a lot of work has been done in the analysis, synthesis and recovering of spectral information using linear models. The general use of such models is for the establishment of a one-to-one relationship between sensor's data and reflectance spectrum, with the requirement of ensuring the quality of the recovered spectrum in terms of physical feasibility and naturalness. In this paper, we propose a solution to correct the outcome of a generic recovery method, in order to take into account quality constrains. Our strategy assumes the smoothness of the solution of the recovery method, an assumption implicitly satisfied from the adoption of linear models to represent reflectance functions.
Improving Color Constancy Using Indoor–Outdoor Image Classification

In this work, we investigate how illuminant estimation techniques can be improved, taking into account automatically extracted information about the content of the images. We considered indoor/outdoor classification because the images of these classes present different content and are usually taken under different illumination conditions. We have designed different strategies for the selection and the tuning of the most appropriate algorithm (or combination of algorithms) for each class. We also considered the adoption of an uncertainty class which corresponds to the images where the indoor/outdoor classifier is not confident enough. The illuminant estimation algorithms considered here are derived from the framework recently proposed by Van de Weijer and Gevers. We present a procedure to automatically tune the algorithms' parameters. We have tested the proposed strategies on a suitable subset of the widely used Funt and Ciurea dataset. Experimental results clearly demonstrate that classification based strategies outperform general purpose algorithms.
Recognizing Faces In 3D Images Even In Presence Of Occlusions

We have implemented a full automatic pipeline that is able to automatically detect, normalize and recognize the faces in presence of extraneous objects. The face detector uses curvature analysis, surface registration through ICP and Gappy PCA classification. After detection and normalization, the face images are restored using an occlusion detection algorithm followed by Gappy PCA reconstruction. Feature extraction and matching are performed by Fisherfaces recognition, but any state of the art approach may be applied. The proposed system has been tested using the 951 acquisitions from the UND database processed with an artificial occlusions generator. Realworld objects have been used to hide part of the faces. The results show that the approach can be adopted to improve the robustness of 3D face recognition systems in case of occlusions with an extension up to 30% of the face image.
Towards the automated interpretation of geological surveys

The development of an interactive system tailored to support the activity of the geologist, who examines the gravimetric map of a region to draw information about the geological underground structures, is described. Access to the system is managed by an interface that creates a visual environment for the expert engaged in designing and testing procedures that automatically may execute the activity of interpreting gravimetric maps. The gravimetric map is a suitable representation of a digital image whose pixel values are related to gravimetric measures.<<ETX>>
Graphic symbol recognition using a signature technique

The authors present a noise-robust and general-purpose method for the recognition of graphic symbols in line-drawing images. The method assumes that a noise-free symbol database is available, and that both the model symbols and the processed image have been broken down into elementary structures. Recognition is based on the hypothesis-and-test paradigm. The detection of an elementary structure allows us to use a signature filter, to hypothesize and then recursively reduce a set of matching symbols. Hypothesized symbols are then verified by exploiting distance transform and distance measurement.
Content-based color image retrieval with relevance feedback

We describe the main features of Quicklook, a prototype system that allows the user to query an image database and progressively refine the system's response by indicating the relevance, or nonrelevance of the retrieved items. Our experience with Quicklook demonstrates that user interaction with the system greatly improves retrieval results, making it possible, with no significant effort by the user, to tune the similarity measure used by the system to the user's notion of image similarity.
Using Hopfield networks in the nominal color coding of classified images

Nominal color coding is widely used by the image processing community to represent the output of a classification-segmentation process. In order to automate color-class association we propose a suitable description scheme for both the color set to be used and the image to be coded. Using this description we then define a suitable energy function for Hopfield's neural networks. The objective is to associate more "conspicuous" colors with less "visible" classes, assigning highly contrasting colors to classes with a high "adjacency".
Single and Multiple Illuminant Estimation Using Convolutional Neural Networks

In this paper, we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a convolutional neural network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non-linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of our method.
Visual video summaries post-processing using supervised and unsupervised classification

Visual summaries allow users to grasp the overall content of a video, and access specific sequences within the video itself. The post-processing method presented in this paper makes it possible create exhaustive and not redundant visual video summaries. Three steps compose the method: the first removes the meaningless key frames; the second groups the key frames into clusters to allow multi-level summary presentation; and the third identifies the default summary level.
A 3D face recognition system using curvature-based detection and holistic multimodal classification

We present here a fully automated system for face detection and recognition. In a scene acquired by a three-dimensional laser scanner the system can detect the presence of human faces; the faces detected are registered in a canonical position and then recognized. Both the 3D and 2D (i.e. pictorial) information provided by the scanner are exploited. The 3D representation is used to detect the presence of faces and normalize them. In the recognition step 3D and 2D information are independently analyzed, producing two different scores that are then combined to compute the output of the system.
UMB-DB: A database of partially occluded 3D faces

In this paper we present the UMB-DB 3D face database. The database has been built to test algorithms and systems for 3D face analysis in uncontrolled and challenging scenarios, in particular in those cases where faces are occluded. The database is composed of 1473 pairs of depth and color images of 143 subjects. Each subject has been acquired with different facial expressions, and with the face partially occluded by various objects such as eyeglasses, hats, scarves and hands. The total number of occluded acquisitions is 578. The database, that is freely available for research purposes, could be used for various investigations, some of which are suggested in the paper. For the sake of comparison, we report the results of some of the 3D face detection and recognition algorithms in the state of the art.
Color constancy using CNNs

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN.
Towards the design of low cost colorimetric imaging device

In this paper we present a preliminary study towards the design of a low cost colorimetric imaging device. We show that an effective color measurement is possible even using consumer camera sensors. We present here the colorimetric accuracy test results obtained with simulated data using the theoretical model of image formation and a camera simulation system. The final paper will also include the colorimetric accuracy test results obtained with real camera sensors. For all the experiments the multispectral response has been obtained by the overlap of colored filters on the existing color filter array pattern. Many consumer devices can benefit from the colorimetric accuracy of such a system, e.g. printers with automatic color management characterization or other consumer devices where an accurate color response is required.
Sampling Optimization for Printer Characterization by Direct Search

Printer characterization usually requires many printer inputs and corresponding color measurements of the printed outputs. In this brief, a sampling optimization for printer characterization on the basis of direct search is proposed to maintain high color accuracy with a reduction in the number of characterization samples required. The proposed method is able to match a given level of color accuracy requiring, on average, a characterization set cardinality which is almost one-fourth of that required by the uniform sampling, while the best method in the state of the art needs almost one-third. The number of characterization samples required can be further reduced if the proposed algorithm is coupled with a sequential optimization method that refines the sample values in the device-independent color space. The proposed sampling optimization method is extended to deal with multiple substrates simultaneously, giving statistically better colorimetric accuracy (at the α = 0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing use of a single set of characterization samples for multiple substrates.
A knowledge-based environment for assessment of color similarity

A discussion is given on a knowledge-based approach to the representation and implementation of the similarity evaluation of colors; all the aspects involved in color description and the relationships between color descriptions and similarity judgments may be explicitly represented in a quantitative, rigorous and reliable way. The authors choose CIELUV, which provides a flexible and powerful numerical description of color appearance, as the color description space and fuzzy logic as the knowledge representation language for modelling human judgments in the universe of discourse of CIELUV measures. An explanatory example of color image retrieval based on similarity measure is provided to aid the presentation of tools and strategies of the knowledge-based environment.<<ETX>>
Detection and Restoration of Occlusions for 3D Face Recognition

This paper presents an innovative restoration strategy which allows for an effective recognition of 3D faces, even when they are partially occluded by unforeseen, extraneous objects such as scarves, hats, glasses, and so on. First, the occluded regions are detected by considering their effects on the projections of the faces in a suitable face space; the non-occluded regions are then used to restore the missing information. Any recognition algorithm can be applied as usual to restored faces. This restoration strategy led to very satisfactory results on a test set of 52 three-dimensional faces presenting various kinds of occlusions
Relative Saliency Model over Multiple Images with an Application to Yarn Surface Evaluation

Saliency models have been developed and widely demonstrated to benefit applications in computer vision and image understanding. In most of existing models, saliency is evaluated within an individual image. That is, saliency value of an item (object/region/pixel) represents the conspicuity of it as compared with the remaining items in the same image. We call this saliency as absolute saliency, which is uncomparable among images. However, saliency should be determined in the context of multiple images for some visual inspection tasks. For example, in yarn surface evaluation, saliency of a yarn image should be measured with regard to a set of graded standard images. We call this saliency the relative saliency, which is comparable among images. In this paper, a study of visual attention model for comparison of multiple images is explored, and a relative saliency model of multiple images is proposed based on a combination of bottom-up and top-down mechanisms, to enable relative saliency evaluation for the cases where other image contents are involved. To fully characterize the differences among multiple images, a structural feature extraction strategy is proposed, where two levels of feature (high-level, low-level) and three types of feature (global, local-local, local-global) are extracted. Mapping functions between features and saliency values are constructed and their outputs reflect relative saliency for multiimage contents instead of single image content. The performance of the proposed relative saliency model is well demonstrated in a yarn surface evaluation. Furthermore, the eye tracking technique is employed to verify the proposed concept of relative saliency for multiple images.
An efficient method for detecting ghost and left objects in surveillance video

This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computation in background modeling and object tracking in surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of foreground blobs; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by constructing the real background using the candidate 's corresponding regions in the input and the background images. The effectiveness of our method has been validated by experiments over a variety of video sequences.
Automatic hybrid registration for 2-dimensional CT abdominal images

Registration of abdominal images plays an important role in clinical practice, however, because of the complex deformations of organ structure and volume, abdominal image registration remains a challenge. In this paper, a hybrid registration approach is proposed, which consists of two procedures: intensity-based registration procedure and landmark-based registration procedure. In intensity-based registration, in order to speed up registration convergence and to improve registration computation efficiency, a wavelet-based hierarchical method is proposed, in which the global displacements are corrected using mutual information algorithm. In the landmark-based registration, firstly, the landmark points are selected automatically; and then, the local non-linear deformations are corrected using the elastically thin-plate splines. By combining the advantages of intensity-based registration method and that of landmark-based method, the proposed approach can register the images accurately and efficiently. The registration performance of the proposed algorithm is validated by experiments on clinical computed tomography (CT) abdominal images.
A locally constrained statistical shape model for robust nasal cavity segmentation in computed tomography

Accurate segmentation of the nasal cavity plays a pivotal role for the creation of patient specific nasal models which are essential for the diagnosis and treatment planning of nasal-related disorders and diseases, as well as for research on nasal drug delivery. However, the structure of the nasal cavity is difficult to segment due to the lack of boundary distinction to other connected airway components, such as the paranasal sinuses, which exhibits the same intensity range. Existing algorithms fail at differentiating the cavity from its surrounding structures. This paper presents a new method at segmenting the nasal cavity, designed to differentiate between the nasal and non-nasal airway. Our algorithm makes use of a range of statistical knowledge of the complex nasal anatomy through the introduction of a robust multi-atlas initialization for seeds derivation and the incorporation of a similarity guided statistical shape model (SSM). Our approach combines the shape variation energy of the SSM together with a modified locally constrained random walk algorithm to segment the nasal cavity. Our proposed algorithm was evaluated on 20 CT images and outperformed comparative state-of-the-art and conventional algorithms.
Fast adaptive variable frame-rate coding

Due to limited bandwidth in mobile applications, it is a noticeable waste to use constant frame-rate video. We propose a novel method of variable frame-rate video coding - fast adaptive variable frame-rate coding. The method analyzes video contents and adjusts frame-rate dynamically and adaptively. Utilizing information generated by existing video encoders, the additional computing cost involved is acceptable. Compared with traditional constant frame-rate video coding, the method can remarkably decrease bitrate with almost invisible degradation of video quality. It can be applied to any motion-estimation based video codec. Experimental results on not only standard test video sequences but also practical video sequences prove that the method is valuable and viable.
Fuzzy Local Gaussian Mixture Model for Brain MR Image Segmentation

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.