nils-herrmann/clean_scirepeval huggingface dataset:
Control of postal parcels separation by artificial vision

A procedure for verifying the spacing of parcels in the mail sorting process has been developed. It is a multistep, silhouette-based method. It starts by digitizing grey-level images and ends with a decision as to whether parcels in the sorting area are improperly or properly spaced. This technique also locates and reads the address block on the parcels. Experimental results demonstrate the potential of this spacing verification scheme. It is noted that the approach is quite fast.<<ETX>>
A coherent computational approach to model bottom-up visual attention

Visual attention is a mechanism which filters out redundant visual information and detects the most relevant parts of our visual field. Automatic determination of the most visually relevant areas would be useful in many applications such as image and video coding, watermarking, video browsing, and quality assessment. Many research groups are currently investigating computational modeling of the visual attention system. The first published computational models have been based on some basic and well-understood human visual system (HVS) properties. These models feature a single perceptual layer that simulates only one aspect of the visual system. More recent models integrate complex features of the HVS and simulate hierarchical perceptual representation of the visual input. The bottom-up mechanism is the most occurring feature found in modern models. This mechanism refers to involuntary attention (i.e., salient spatial visual features that effortlessly or involuntary attract our attention). This paper presents a coherent computational approach to the modeling of the bottom-up visual attention. This model is mainly based on the current understanding of the HVS behavior. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are some of the features implemented in this model. The performances of this algorithm are assessed by using natural images and experimental measurements from an eye-tracking system. Two adequate well-known metrics (correlation coefficient and Kullbacl-Leibler divergence) are used to validate this model. A further metric is also defined. The results from this model are finally compared to those from a reference bottom-up model.
Full reference and reduced reference metrics for image quality assessment

In this paper, we propose efficient full reference and reduced reference image quality assessment metrics. These two metrics are based on human visual system properties to get the best correspondence with human judgments. The full reference metric is generic (independent from image distortion type) and convenient for image coding schemes comparison. From this metric, we have derived a reduced reference metric suitable for quality of service monitoring in a broadcasting purpose. The two metrics share common processings. The main difference between them comes from the selection of information in order to get a high-level representation of images for a reduced reference metric. If limited to a specific type of image coding scheme, the two metrics achieve similar performances.
Motion and structure based image segmentation for object oriented time-varying sequences coding

The paper presents an ascending approach to the construction of the spatio-temporal segmentation of time varying sequences. The particularity of the approach is to combine motion homogeneity of the segments with spatial fidelity of occlusion boundaries. The tracking process with the developed procedures of spatial and temporal adjustment allows a high quality reconstruction of the sequence frames by motion. It makes the approach especially interesting in the framework of object-oriented image sequence coding.
Performance assessment of a visual attention system entirely based on a human vision modeling

It is now commonly assumed that the human visual attention, which is a selecting process of the most relevant locations in a scene according to a particular behavior, is driven by both top-down (task-dependent) and bottom-up (signal-dependent) control. A new model attempting to simulate the bottom-up process has been designed Le Meur, O et al., (2004). This model is purely based on visual system properties that provides noticeable advantages compared to the classical published approaches. This paper focuses on the performance assessment of this model by achieving a comparison with real fixation points stemming from eye-tracking apparatus both subjectively and objectively.
From SD to HD Television: Effects of H.264 Distortions Versus Display Size on Quality of Experience

High definition television (HDTV) is the new broadcasting system designed to take the place of standard definition television (SDTV) at home in the near future. This system requires modification of many features in the broadcasting chain with an overall objective of reaching a noticeably higher quality of experience. Since broadcasters desire a high level of service acceptability, they require efficient measurements of quality of experience. The purpose of this paper is to provide such measurements concerning the noticeable artifacts in H.264 distortions over a range of display sizes and comparing HDTV to SDTV. A subjective characterization of some HDTV quality of experience aspects is proposed and the results are discussed.
A multi-resolution approach to extract the address block on flat mail pieces

A new approach is presented for dealing with the extraction of block address destination (BAD) on flat mail pieces. It is based on the construction of a pyramidal data structure which represents the image with a multi-resolution approach, so that much independence is provided with respect to variable parameters such as the size of characters, number of lines, and handwritten or machine printed. A top-down analysis is then performed on the pyramidal structure to interpret the various blocks segmented and their relationship between every level of the pyramid. Results are given in order to demonstrate the performances of this method.<<ETX>>
Masking and quantization laws in a visual subband coding scheme

To design psychovisual quantizers, experiments based on the visibility of the quantization noise have been conducted by Saadane, Senane and Barba (see Visual Communications and Image Processing, Oct. 1994). The results obtained show that for a given visual decomposition scheme and within a subband, the decision thresholds and reconstruction levels follow a linear law with an interval quantization varying with frequency and orientation. In this paper, and in order to examine the masking effects on the perception of quantization noise, further experiments have been conducted by keeping the same methodology and the same decomposition scheme as Saadane et. al. Two important results have been obtained. First the masking didn't affect the linear behavior observed when no masking was considered. Secondly, with the contrast definition used, and even a slight increase in the quantization interval has been observed for the radial subbands, the masking was essentially due to the angular subbands.<<ETX>>
Does where you Gaze on an Image Affect your Perception of Quality? Applying Visual Attention to Image Quality Metric

The aim of an objective image quality assessment is to find an automatic algorithm that evaluates the quality of pictures or video as a human observer would do. To reach this goal, researchers try to simulate the Human Visual System (HVS). Visual attention is a main feature of the HVS, but few studies have been done on using it in image quality assessment. In this work, we investigate the use of the visual attention information in their final pooling step. The rationale of this choice is that an artefact is likely more annoying in a salient region than in other areas. To shed light on this point, a quality assessment campaign has been conducted during which eye movements have been recorded. The results show that applying the visual attention to image quality assessment is not trivial, even with the ground truth.
Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform

This paper presents a new approach in automation for crack detection on pavement surface images. The method is based on the continuous wavelet transform. In the first step, a separable 2D continuous wavelet transform for several scales is performed. Complex coefficient maps are built. The angle and modulus information are used to keep significant coefficients. Then, wavelet coefficients maximal values are searched and their propagation through scales is analyzed. Finally, a post-processing gives a binary image which indicates the presence or not of cracks on the pavement surface image.
Robust segmentation of moving image sequences

This paper introduces a method for a hierarchical representation of moving image sequences. The basic level of this hierarchy is a spatial segmentation, rich in regions and whose contours are localised very precisely. The next levels are constructed by a motion-based progressive merging of regions. Different levels of this hierarchy are nested. This representation is sufficiently flexible to allow a "logical zoom" in the image content and to provide a scalable bit rate. So the two goals of content manipulation and of image compression can be reached.
A robust quality metric for color image quality assessment

In this paper, we propose a visual color image quality metric with full reference image for the evaluation of coding schemes. This metric is based on human visual system properties in order to get the best correspondence with human judgements. Contrary to some others objective criteria, it doesn't use any information on the type of degradations introduced by coding schemes. We use two main stages: the first one in order to compute visual representation of images (based on results from psychophysics experiments conducted in our laboratory) and the second in order to pool errors between visual representation of two images. We propose a new approach for this pooling stage based on the density of errors and their structure. We also show the interest of such pooling method. We compare results of the metric with human judgments on a database of 140 images distorted with 3 types of compression schemes (JPEG, JPEG2000 and a ROI-based algorithm). High performances are obtained leading to assure that the metric is robust, so this approach constitutes an alternative useful tool to PSNR for coding image searchers.
“Chromosome”: A rule-based image analysis system for chromosome classification

A fully automatic system is presented for the analysis and the classification of human chromosomes. Its architecture is based on two cooperating sub-systems: a rule-based system including two types of knowledge (cytogenetics, image analysis) and a tool box containing the low level processings such as pattern recognition and classification methods. The multi-agents design of this system makes further developments easier (modularity) and allows to generalize it to other image analysis applications.
Joint tracking of polygonal and triangulated meshes of objects in moving sequences with time varying content

This paper proposes a method for tracking of objects contained in video sequences. Each video object is represented both by a triangulated mesh and a polygonal mesh. The tracking of such models along a moving sequence is based on a full region-based polygonal tracking. The triangulated mesh is a union of Delaunay meshes on each polygonal region of the VOP. The mesh is articulated, that is, each polygonal region in a VOP is Delaunay-triangulated separately and all partial meshes are connected in the global triangulation. Tracking of triangulated meshes is based on detecting and indexing new objects in the video scene along the time in a polygonal tracking phase.
Probabilistic shape-based segmentation using level sets

In this paper, we present a new dynamic and probabilistic shape based segmentation method using statistical and variational approaches. We use two models in this paper: i) intensity and ii) shape. In the first phase, the intensity based segmentation is done using a basic statistical level set method. In the second phase, to which we contribute, the shape model is constructed using the implicit representation of the training shapes. The resulting probability density function is used to embed the shape model into the image domain with a new energy minimization solution. Our method' s invariance to parameter initialization is evaluated through validation, and various synthetic and clinical shape registration examples are implemented. Experiments show that our proposed algorithm enhances the conventional global registration results, overcomes segmentation challenges, and is robust under various noise levels, severe occlusions, and missing parts.
An AAM-based detection approach of lung nodules from LDCT scans

In this, paper a new approach for lung nodules detection from LDCT scans is proposed. Intensity models of the nodules are generated using an active appearance model formulation. Template matching is used to compute a similarity score between the AAM template and the input image. The goal is to maximize the similarity measure at different image pixels to increase nodule detection. Conventional template matching does not account for rotation variations. Our proposed template matching approach is formulated as an energy optimization problem that computes a transformation that includes rotation(s) parameters as well as the AAM weighting coefficients. The approach is flexible to different scans and different nodule locations because of the ability to handle the variations in the rotation between the template and the input images. The approach can employ different similarity measures. Experimental results will be shown using three similarity measures from the literature: NCC, ZNCC and ZSSD; which illustrate the efficiency of the proposed approach. ROC curves for various nodule types are constructed on a clinical study with known ground truth, showing significant enhancements over conventional parametric nodule models and traditional template matching criterion.
Human activity recognition using shape moments and normalized fourier descriptors

This paper presents a video based system for recognizing different continuous human activities in real time using a single stationary camera. The proposed system has a motion descriptor that contains shape moments as well as normalized Fourier descriptors. The main idea of this system is to detect moving objects in each frame and associate the detections to the same object over time. We employ an efficient thresholding technique to segment the objects of interest. The second stage of the proposed system is to extract the object features. The first feature part is constructed by extracting the object contour and estimate its normalized Fourier descriptors. The other part is extracted by the shape moments. We combined the shape moments to produce an invariant feature that is invariant to rotation, translation and scaling. We adopt the multi-class support vector machines (SVM) and Naive Bayes classifiers. The multi-class SVM shows better performance than the other method with a recognition rate up to 94.46%. We evaluated activity recognition on 325 videos of thirteen distinct Human activities (e.g., Walking, Running, Jumping, Hand-waving, Bending and some suspicious activities like Kicking, Punching, Fall floor and shooting gun, etc.) recorded for 260 different persons. Experimental results on three data set Weizman, KTH and HMDB validate the proposed system reliability and efficiency.
Elastic shape registration using an incremental free form deformation approach with the ICP algorithm

Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point-based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as criteria to establish point correspondences in each level of the IFFD framework. The IFFD control lattice resolution is increased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems.
2D PCA-based shape prior for level sets segmentation framework of the vertebral body

In this paper, a novel statistical shape modeling method is developed for the vertebral body (VB) segmentation framework. Two-dimensional principle component analysis (2D-PCA) technique is exploited to extract the shape prior. The obtained shape prior is then embedded into the image domain to develop a new shape-based segmentation approach. Our framework consists of four main steps: i) shape model construction using 2D-PCA, ii) Detection of the VB region using the Matched filter, iii) Initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, and iv) Registration of the shape prior and initially segmented region to obtain the final segmentation. The proposed method is validated on a Phantom as well as clinical CT images with various Gaussian noise levels. The experimental results show that the noise immunity and the segmentation accuracy of 2D-PCA based approach are much higher than conventional PCA approach.
Elastic Shape Registration Using an Incremental Free Form Deformation Approach with the ICP Algorithm

Shape registration is one of the most challenging problems in computer vision and medical imaging. The process is affected by the way the shape is represented and the form of transformation used to move the source shape. Our paper handles the elastic shape registration by combining the incremental free form deformation (IFFD) with the point-based registration technique using the sum of least squares method. The iterative closest point (ICP) algorithm is used as a criteria to establish point correspondences in each level of the IFFD framework. The free form deformation (FFD) is well known in the literature and works by forming a lattice of control points that can move and hence deform the domain grid points smoothly and uniformly under some shape constrains. The control lattice resolution is increased step by step to achieve a satisfactory deformation of the source shape to exactly match the target boundaries. Our point-based registration is based on least squares that measure the Euclidean distance between source and target boundaries in addition to the shape constrains. The minimization gives a closed form solution of the lattice control points positions. Promising results will be demonstrated for closed and open shapes and structures. The approach can also work for structures that contain multiple parts without any problems.
A novel probabilistic simultaneous segmentation and registration using level set

We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.
Modeling of the lung nodules for detection in LDCT scans

A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.
A new shape based segmentation framework using statistical and variational methods

In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information and there are no strong edges in some CT images. To overcome these challenges, image appearance and shape information of VBs are used. There are three phases of our experiments: i) the detection of the VB region using the Matched filter, ii) initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, iii) registration of the shape priors and initially segmented region to obtain the final segmentation. Preliminary results show that our proposed algorithm gives very encouraging results and can solve many segmentation and registration problems.
A 3D human teeth database construction based on a point-based shape registration

We propose a new system for building a 3D database for human teeth. Real extracted human teeth are scanned using a cone-beam CT scanner to build. The teeth models are segmented from the resulting images by means of level sets. The database includes 3D models of these teeth after minimizing global differences between the models. An affine transformation function is used to model the differences between the models. The transformation parameters are estimated by minimizing a least squares energy. A total of 280 teeth 3-D models have been created to build the real database and demonstrate the performance of the proposed framework.
Small-size lung nodule modeling and detection with clinical evaluation

In this paper examination of the template modeling process using the Active Appearance Modeling (AAM) approach for automatic detection of lung nodules is investigated. A template matching approach is formulated to compute a similarity score between the AAM templates and the input lung CT slice, where the goal is to maximize the similarity measure at different image pixels to increase nodule detection. The template matching approach is implemented using nine similarity measures. Performance validation for the robustness of the generated models is tested on three clinical databases.
Profile-based face recognition

In this paper, we introduce a new system for profile-based face recognition. The specific scenario involves a driver entering a gated area and using his/her side-view image (the driver remains seated in the vehicle) as identification. The system has two modes: enrollment and identification. In the enrollment mode, 3D face models of subjects are acquired and profiles extracted under different poses and stored to form a gallery database. In the identification mode, 2D images are acquired and the corresponding planar profiles are extracted and used as probes. Then, probes are matched to the gallery profiles to determine identity. The matching is accomplished using implicit shape registration via the vector distance functions. In our experiments, the approach using implicit registration exhibited higher accuracy than the iterative closest point methodology due to the use of more general transformations. The performance of our system is illustrated using a variety of databases.