nils-herrmann/clean_scirepeval huggingface dataset:
Image coding quality assessment using fuzzy integrals with a three-component image model

Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.
Motion detection and tracking based on level set algorithm

A novel energy-minimizing model is proposed in this paper for motion detection and object tracking. In our model, the logarithmic image is employed to compensate for illumination change. Another feature of our approach is that the boundary force is no longer necessary. For solving the corresponding curve evolution equation efficiently, a local level set algorithm is adopted. When constructing the narrow band and resetting the level set function to the signed distance function, it is not needed to explicitly label the contour points during the evolution of contours. The local level set algorithm is based on partial differential equations (PDEs), which leads to a simple, flexible and stable scheme. This paper also proposes an appropriate stopping criterion for the level set algorithm without a need of explicitly extracting the locations of the evolving curve.
A Neural Root Finder of Polynomials Based on Root Moments

This letter proposes a novel neural root finder based on the root moment method (RMM) to find the arbitrary roots (including complex ones) of arbitrary polynomials. This neural root finder (NRF) was designed based on feedforward neural networks (FNN) and trained with a constrained learning algorithm (CLA). Specifically, we have incorporated the a priori information about the root moments of polynomials into the conventional backpropagation algorithm (BPA), to construct a new CLA. The resulting NRF is shown to be able to rapidly estimate the distributions of roots of polynomials. We study and compare the advantage of the RMM-based NRF over the previous root coefficient methodbased NRF and the traditional Muller and Laguerre methods as well as the mathematica roots function, and the behaviors, the accuracies of the resulting root finders, and their training speeds of two specific structures corresponding to this FNN root finder: the log and the FNN. We also analyze the effects of the three controlling parameters P0 p with the CLA on the two NRFs theoretically and experimentally. Finally, we present computer simulation results to support our claims.
Tracer kinetic modeling of /sup 11/C-acetate applied in the liver with positron emission tomography

It is well known that 40%-50% of hepatocellular carcinoma (HCC) do not show increased /sup 18/F-fluorodeoxyglucose (FDG) uptake. Recent research studies have demonstrated that /sup 11/C-acetate may be a complementary tracer to FDG in positron emission tomography (PET) imaging of HCC in the liver. Quantitative dynamic modeling is, therefore, conducted to evaluate the kinetic characteristics of this tracer in HCC and nontumor liver tissue. A three-compartment model consisting of four parameters with dual inputs is proposed and compared with that of five parameters. Twelve regions of dynamic datasets of the liver extracted from six patients are used to test the models. Estimation of the adequacy of these models is based on Akaike Information Criteria (AIC) and Schwarz Criteria (SC) by statistical study. The forward clearance K=K/sub 1/*k/sub 3//(k/sub 2/+k/sub 3/) is estimated and defined as a new parameter called the local hepatic metabolic rate-constant of acetate (LHMRAct) using both the weighted nonlinear least squares (NLS) and the linear Patlak methods. Preliminary results show that the LHMRAct of the HCC is significantly higher than that of the nontumor liver tissue. These model parameters provide quantitative evidence and understanding on the kinetic basis of /sup 11/C-acetate for its potential role in the imaging of HCC using PET.
A computerized plant species recognition system

In this paper, a computerized plant species recognition system (CPSRS) is presented. CPSRS is a Web-based application, which provides a familiar and efficient way to search and identify plant species in the field. It is built on the Java Web infrastructure to support platform-independent application. The Java applets and servlets are adopted to balance the computing burden in both client and server. The architecture of CPSRS is introduced to show how it is designed and works. Two types of plant species retrieval methods, text-based information retrieval and content-based leaf retrieval are discussed. With the text-based information retrieval method, the exact information of plant species is retrieved from the database according to the input searching criteria. For the content-based leaf retrieval, experimental results show that a recall rate of about 71.4% can be achieved when top five returned images are considered.
Machine learning techniques for ontology-based leaf classification

Leaf classification, indexing as well as retrieval is an important part of a computerized plant identification system. In this paper, an integrated approach for an ontology-based leaf classification system is proposed, wherein machine learning techniques play a crucial role for the automatization of the system. For the leaf contour classification, a scaled CCD code system is proposed to categorize the basic shape and margin type of a leaf by using the similar taxonomy principle adopted by the botanists. Then a trained neural network is employed to recognize the detailed tooth patterns. The measurement on an unlobed leaf is also conducted automatically according to the method used in botany. For the leaf vein recognition, the vein texture is extracted by employing an efficient combined thresholding and neural network approach so as to obtain more vein details of a leaf. Compared with the past studies, the proposed method integrates low-level features of an image and the specific knowledge in the domain (ontology) of botany, and therefore provides a more practical system for users to comprehend and handle. Primary experiments have shown promising results and proven the feasibility of the proposed system.
Comparison of image partition methods for adaptive image categorization based on structural image representation

Image categorization is very helpful for organizing large image databases efficiently, however, it is yet very challenging due to lack of effective image representations. Our previous work showed that structural representations were good at characterizing image contents, since image contents could be exploited from coarse to fine scales through the structures representation and fewer visual features are required. In this paper, several popular image partition methods are investigated for adaptive image categorization based on structural representation. Experimental results on seven categories of scenery images show that both the structure and node attributes are important to categorize image contents. In addition, the more similar the structures of each category, the better the categorization performance.
A flower image retrieval method based on ROI feature

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Daset al. (1999).
Feature filtering in relevance feedback of image retrieval based on a statistical approach

Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since the 1990s. A feature filter, whose parameters are computed by a statistical resampling approach, is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experimental results show that the proposed approach is more efficient and robust than the traditional method.
A hypersphere method for plant leaves classification

The recognition of the tens of thousands of kinds of plants on Earth is an important and difficult task. If we use a nearest neighbour classifier to solve it, millions of training data are needed to obtain a high correct recognition rate. However, the corresponding classification process is quite time-consuming. We propose a new approach using a moving median centers hypersphere technique to recognize different plant leaves. With this new method, we can successfully decrease the classification time, and reduce the storage size without sacrificing the classification accuracy. Experimental results using real data show that this method is really efficient and effective in classifying different plant leaves.
Image retrieval based on Sugeno fuzzy integral

In this paper, we propose a fuzzy similarity measure based on the Sugeno fuzzy integral and apply it to the content-based image retrieval. Fuzzy measure is used to formulate the subjective feedback information. Experimental results show that the proposed method can improve the efficiency and reliability of image retrieval system. Our method of formulating the subjective feedback information performs better than the weighted average operator (WAO) and a Choquet-integral-based similarity measure.
Data Management for Live Plant Identification

Plant identification by computers will surely find a wide range of applications including plant resource survey, plant data management, and education on plant taxonomy. Since computerized plant identification is a very challenging computer vision problem, the research and development in this field is still in its infancy. In this chapter, we first address the necessary and general issues regarding computer-aided plant identification and the management of a living plant database. We then describe a general approach that botanists adopt for plant identification, and typical systems for living plant identification and plant data management. This is followed by a discussion on a sophisticated approach for computer-aided plant identification supported by image processing, intelligent information processing, and plant data management systems. In this chapter, we also discuss various algorithms for processing leaf, flower and plant images, including image segmentation, leaf venation extraction, and flower region localization. In the discussion, we stress the unique features of plant images and special treatments needed for processing these images. Also discussed in this chapter are the feature extraction of leaf and flower images and the use of these features for leaf and flower image retrieval, a very important sub-task for computer-aided living plant identification. Finally, concluding remarks are made at the end of the chapter.
An algorithm for segmenting moving vehicles

An algorithm for region-based moving object segmentation is presented. The gray-scale image segmentation based on the mean shift algorithm (MSA) is first performed to segment each frame of a sequence into connective homogeneous regions. A method applying spatio-temporal continuity constraints of the motion vector image is then carried out to detect moving pixels robustly. Finally, each homogeneous region is labeled as either a moving-object region or a non-moving-object region according to the number of moving pixels it contains. Experimental results show that our algorithm is effective and robust in segmenting moving vehicles from noisy scenes.
Plant species recognition based on bark patterns using novel Gabor filter banks

This paper presents a novel style of Gabor filter banks designed for plant species recognition using their bark texture features. In this paper, texture is modeled as multiple narrowband signals that are characterized by their central frequencies and normalized ratios of amplitudes. The normalized ratio of amplitudes is employed as an energy weight for combining narrowband signals. Based on this texture model, a set of texture features can be extracted from each kind of plant bark that is used to characterize the plant and to design the corresponding Gabor filter bank. A classifier is constructed by these Gabor filter banks. Plant recognition experiments on a small database of bark images have been conducted and the effectiveness of our approach is confirmed by the experimental results.
Region-of-interest based flower images retrieval

Flower image retrieval is a very important step for computer-aided plant species identification. We propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of a flower and two shape-based sets of features, centroid-contour-distance (CCD) and angle code histogram (ACH), to characterize the shape features of a flower contour. Experimental results show that our flower region extraction approach based on color clustering and domain knowledge can achieve accurate flower regions. The retrieval results on a database of 885 flower images collected from 14 plant species show that our region-of-interest (ROI) based retrieval approach can perform better than the Swain method based on the global color histogram (Swain, M.J. and Ballard, D.H., , p.11-32, 1991).
Novel feature representation and matching techniques for content-based image retrieval

This thesis presents novel feature representation schemes and matching techniques for content-based image retrieval. There are three main contributions reported in the thesis. They include: (1) a block-constrained fractal coding scheme and an improved nona-tree decomposition based matching technique for image retrieval; (2) a thinning-based starting point localization method and the application of a fuzzy integral to the combination of different shape feature sets for plant leaf image retrieval; and (3) tree-structured content representation and its adaptive processing for content-based image retrieval with relevance feedback. 
In the first investigation, an image is partitioned into non-overlap blocks of a size similar to that of an iconic query image. Fractal code is efficiently generated for each block individually. For the similarity measure in matching the fractal code of two images, an improved nona-tree decomposition scheme is adopted to avoid matching the fractal code globally so as to significantly reduce computational complexity. 
In the second investigation, a robust fuzzy integral method is used to combine three shape feature sets, namely, centroid-contour distance (CCD) curve, moment invariants (MIs), and angle code histogram (ACH), for shape-based plant leaf image retrieval. Different from MIs and the ACH feature set, the CCD curve is neither scale nor rotation invariant. Hence, a normalization scheme is needed for the CCD curve to achieve scale invariance, and an efficient starting point localization method is required to achieve the rotation invariance with the similarity measure of CCD curves. In this investigation, a thinning-based method is proposed to locate possible starting points of a leaf contour to make our approach more computationally efficient for image matching. Our proposed starting point localization method can also benefit other shape representation schemes that are sensitive to starting points. In order to combine the three feature sets objectively yet consistently with human perception, a fuzzy integral is employed to combine the similarity measures of the three shape feature sets. The fuzzy integral approach has two distinct advantages: (1) releasing the user from a burden of tuning the combination parameters required for a weighted summation based approach; (2) guaranteeing an optimal or near optimal combination performance. 
In the third investigation, a novel structural representation of image content and shape pattern, and its adaptive processing is proposed for content-based image retrieval with relevance feedback.
An improved algorithm for learning long-term dependency problems in adaptive processing of data structures

Many researchers have explored the use of neural-network representations for the adaptive processing of data structures. One of the most popular learning formulations of data structure processing is backpropagation through structure (BPTS). The BPTS algorithm has been successful applied to a number of learning tasks that involve structural patterns such as logo and natural scene classification. The main limitations of the BPTS algorithm are attributed to slow convergence speed and the long-term dependency problem for the adaptive processing of data structures. In this paper, an improved algorithm is proposed to solve these problems. The idea of this algorithm is to optimize the free learning parameters of the neural network in the node representation by using least-squares-based optimization methods in a layer-by-layer fashion. Not only can fast convergence speed be achieved, but the long-term dependency problem can also be overcome since the vanishing of gradient information is avoided when our approach is applied to very deep tree structures.
Region-based binary tree representation for image classification

Image classification is a very challenging problem due to lack of effective representations. In this paper, a region-based binary tree representation incorporating with adaptive processing of data structures is proposed to address this problem. After an image is segmented, a binary tree is established to characterize its contents by using region merging method. Finally, an adaptive processing of data structure algorithm is employed to perform the classification task with binary tree representation. Experimental results on seven categories of scenery images show this region-based structural representation is superior to our previous work based on quadtree representation.
Content-Based Image Retrieval with Relevance Feedback Using Adaptive Processing of Tree-Structure Image Representation

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.
Shape based leaf image retrieval

The authors present an efficient two-stage approach for leaf image retrieval by using simple shape features including centroid-contour distance (CCD) curve, eccentricity and angle code histogram (ACH). In the first stage, the images that are dissimilar with the query image are first filtered out by using eccentricity to reduce the search space, and fine retrieval follows by using all three sets of features in the reduced search space in the second stage. Different from eccentricity and ACH, the CCD curve is neither scaling-invariant nor rotation-invariant. Therefore, normalisation is required for the CCD curve to achieve scaling invariance, and starting point location is required to achieve rotation invariance with the similarity measure of CCD curves. A thinning-based method is proposed to locate starting points of leaf image contours, so that the approach used is more computationally efficient. Actually, the method can benefit other shape representations that are sensitive to starting points by reducing the matching time in image recognition and retrieval. Experimental results on 1400 leaf images from 140 plants show that the proposed approach can achieve a better retrieval performance than both the curvature scale space (CSS) method and the modified Fourier descriptor (MFD) method. In addition, the two-stage approach can achieve a performance comparable to an exhaustive search, but with a much reduced computational complexity.
Automatic segmentation of Chinese chunks using a neural network

Chunk analysis method is a new idea proposed in recent years in developing new machine translation methods. Chinese chunk analysis plays a very important role in machine translation from Chinese to foreign languages. The automatic segmentation of Chinese chunks is the basis of Chinese chunk analysis. It is important for processing real text and corpus construction. This paper presents a method for automatic segmentation of Chinese chunks based on 3-layers neural networks. The corpus we used has been processed with Chinese word segmentation and phrase identification and tagging. In the neural networks model, the input data is the segmentation situation of every character and its combinations with neighbor characters in a Chinese sentence. The output is the segmentation results of every character in a Chinese sentence. The preliminary results show that the method is feasible and effective.