waashk/dblp huggingface dataset:
appearance-based nude image detection. in this paper, we propose an appearance-based nude image detection system. the proposed system is novel in that shape information is used to classify the nude images, and detect small nude images in a large background image. first, the proposed system finds skin regions using texture characteristics of the human skin, which then generates the skin likelihood image. since the skin likelihood image contains shape information as well as skin color information, we used the skin likelihood image as a high level feature to classify the nude images. the image feature vector (resized skin likelihood image) is used as an input to a nonlinear-svm. experimental results show that the proposed system can achieve an excellent classification performance. additionally, the proposed system can detect small nude images in a large image.
a stochastic optimization approach for parameter tuning of support vector machines. support vector machines (svms) are both mathematically well-funded and efficient in a large number of real-world applications. however, the classification results highly depend on the parameters of the model: the scale of the kernel and the regularization parameter. estimating these parameters is referred to as tuning. tuning requires to estimate the generalization error and to find its minimum over the parameter space. classical methods use a local minimization approach. after empirically showing that the tuning of parameters presents local minima, we investigate in this paper the use of global minimization techniques, namely genetic algorithms and simulated annealing. this latter approach is compared to the standard tuning frameworks and provides a more reliable tuning method.
principal curve analysis for temporal data. in this paper, we develop an algorithm for the learning of the medial axis of temporal random point sets, employing the principal curve analysis. the principal curve analysis is a generalization of principal axis analysis, which is a standard method for data analysis in pattern recognition.
two-hand gesture recognition using coupled switching linear model. we present a method coupling multiple switching linear models. the coupled switching linear model is an interactive process of two switching linear models. coupling is given through causal influence between their hidden discrete states. the parameters of this model are learned via em algorithm. tracking is performed through the coupled-forward algorithm based on kalman filtering and a collapsing method. a model with maximum likelihood is selected out of a few learned models during tracking. we demonstrate the application of the proposedmodel to tracking and recognizing two-hand gestures.
hand gesture recognition for deaf people interfacing. in this paper, an approach for deaf-people interfacing using computer vision is presented. the recognition of alphabetic static signs of the spanish sign language is addressed. the proposed approach combines a number of norms to evaluate the distance of the current sign, to the sign models stored in a database (a dictionary). this solution leads to a largely selective criterion. the method is simple enough to provide real-time recognition, and works suitably for most letters.
historical hand-written string recognition by non-linear discriminant analysis using kernel feature selection. in this paper, we propose a method to compose a classifier by non-linear discriminant analysis using kernel method combined with kernel feature selection for holistic recognition of historical hand-written string. through experiments using historical hand-written string database hcd2, we show that our approach can obtain high recognition accuracy comparable to that of individual character recognition.
recognizing frontal face images using hidden markov models with one training image per person. recently, many important face recognition systems could deal well with frontal view face images. however few of them work well when there is only one training image per person. in this paper, we propose an approach to cope with the problem by using 1d discrete hidden markov model (1d-dhmm). the model training and recognition part were carried out on both vertical and horizontal directions. new way of extracting observations and using observation sequences in recognition is introduced. the haar wavelet transform was applied to the image to lessen the dimension of the observation vectors. our experiment results tested on the frontal view ar face database show that the proposed method outperforms the pca, lda, lfa approaches tested on the same database.
non-iterative two-dimensional linear discriminant analysis. linear discriminant analysis (lda) is a well-known scheme for feature extraction and dimensionality reduction of labeled data in a vector space. recently, lda has been extended to two-dimensional lda (2dlda), which is an iterative algorithm for data in matrix representation. in this paper, we propose non-iterative algorithms for 2dlda. experimental results show that the non-iterative algorithms achieve competitive recognition rates with the iterative 2dlda, while they are computationally more efficient than the iterative 2dlda.
an intelligent real-time vision system for surface defect detection. in recent years, there is an increased need for quality control in the manufacturing sectors. in the steel making, the rolling operation is often the last process that significantly affects the bulk microstructure of the steel. the cost of having defects on rolled steel is high because it takes more than 5000 kw-hr to produce a ton of steel. early detection of defects can reduce product damage and manufacturing cost. this paper describes a real-time visual inspection system that uses support vector machine to automatically learn complicated defect patterns. based on the experimental results generated from over one thousand images, the proposed system is found to be effective in detecting steel surface detects. the speed of the system for feature extraction and defect detection is less than 6 msec per one-megabyte image.
improved stone's complexity pursuit for hyperspectral imagery unmixing. as a blind source separation (bss) process, independent component analysis (ica) has recently been used in hyperspectral imagery (hsi) unmixing. it models a "mixed" pixel as a linear mixture of the constituent (endmember) spectra weighted by the correspondent abundance fractions. however, the unmixing results of ica are not satisfied. in this paper, a complexity based bss algorithm called complexity pursuit is introduced. compared to the other bss techniques, this algorithm has two major advantages. first, it does not ignore signal structure. second, the impact of noise can be largely reduced. in addition, an improved conjecture is proposed which makes complexity pursuit suitable for hsi unmixing. the experimental results show that complexity pursuit provides a promising approach to unmix hsi. keywords: complexity pursuit, independent component analysis, hyperspectral imagery unmixing
three-dimensional enhancement of confocal scanning laser fluorescence microscope images for vascular reconstruction. this paper presents a fast method for enhancing the images of the vascular network of zebrafish embryo from confocal scanning laser microscope (cslm) stained with fluorescent probe. specifically, we split the 3d enhancement process into two separate stages and adoptdifferent methods respectively to deconvolve the lateral and optical directions in the observed images. the resulting algorithm increased both lateral and optical resolution and decreased the out of focus light of the images. the enhanced volumetric image is characterized by an improvement both in contrast and edge definition. the algorithm can be efficiently implemented on a general pc and the computation time is small even for the case of large volume of images. the value of the method for enhancing fluorescence images of biological objects is demonstrated by a comparison of the results of 3d vascular reconstruction.
an advanced segmental semi-markov model based online series pattern detection. the online pattern detection technology is an important part of the time series analysis, and some methods have been proposed, in which subsequence matching based window-sliding is popular applied. for window-sliding, euclidean distance and dynamic time warping (dtw) are always used as subsequence matching, but they have the drawbacks of sensitivity and expensive computational load respectively. recently, the model based method is introduced into the field of online pattern detection, especially, the segmental semi-markov model shows better performance than sliding methods in many aspects. however, it has some limitations, e.g., it is difficult to estimate the parameters of the model, and nowaday methods are too rough, etc. in this paper the advanced segmental semi-markov model is proposed to improve the existed segmental semi-markov model. and it is successfully demonstrated on real data sets, including financial and medical data.
gaussian weighted histogram intersection for license plate classification. the conventional histogram intersection (hi) algorithm computes the intersected section of the corresponding color histograms in order to measure the matching rate between two color images. since this algorithm is strictly based on the matching between bins of identical colors, the final matching rate can be easily affected by color variation caused by various environment changes. in this paper, a gaussian weighted histogram intersection (gwhi) algorithm is proposed to facilitate the histogram matching via taking into account matching of both identical and similar colors. the weight is determined by the distance between two colors. the algorithm is applied to license plate classification. experimental results show that the proposed algorithm produces a much lower intra-class distance and a much higher inter-class distance than previous hi algorithms for tested images which are captured under various illumination conditions.
3d shape-based face recognition using automatically registered facial surfaces. in this paper, we address the use of three dimensional facial shape information for human face identification. we propose a new method to represent faces as 3d registered point clouds. fine registration of facial surfaces is done by first automatically finding important facial landmarks and then, establishing a dense correspondence between points on the facial surface with the help of a 3d face template-aidedthin plate spline algorithm. after the registration of facial surfaces, similarity between two faces is defined as a discrete approximation of the volume difference between facial surfaces. experiments done on the 3d rma dataset show that the proposed algorithm performs as good as the point signature method, and it is statistically superior to the point distribution model-based method and the 2d depth imagery technique. in terms of computational complexity, the proposed algorithm is faster than the point signature method.
a miniature stereo vision machine (msvm-iii) for dense disparity mapping. we have developed a miniature stereovision machine (msvm-iii) with three cameras for generating high-resolution dense disparity maps at the video rate. the msvm-iii only uses one fpga chip to compactly compute trinocular rectification, log filtering, and area-based matching. the machine, running at 60 mhz, could process more than 30 fps dense disparity maps with 640×480 pixels in 64-pixel disparity search range, and 120 fps with 320×240 pixels. moreover, the msvm-iii has an ieee 1394 port to a host at the video rate, an interface port to lcd as a miniature 3d imager, and a user board for controlling small mobile robot or other autonomous systems.
graph database filtering using decision trees. graphs are a powerful representation formalism for structural data. they are, however, very expensive from the computational point of view. in pattern recognition it is often necessary to match an unknown sample against a database of candidate patterns. in this process, however, the size of the database is introduced as an additional factor into the overall complexity of the matching process. to reduce the influence of that factor, an approach based on machine learning techniques is proposed in this paper. graphs are represented using feature vectors. based on these vectors a decision tree is built to index the database. the decision tree allows at runtime to eliminate a number of graphs from the database as possible matching candidates.
a pattern recognition approach to automated coronary calcium scoring. an automated method for coronary calcification detection is presented. first the heart region is extracted, in which objects potentially representing calcifications are obtained by thresholding. besides coronary calcifications, the set of objects includes other heart calcifications, bony structures and noise. for each object, features describing its size, shape, position and appearance are computed. several classifiers and classification strategies are evaluated. best results are obtained with a specifically designed sequence of knn classifiers that employ sequential forward feature selection. first obvious non-calcifications are removed, then calcifications are distinguished from non-calcifications and a final classifier discerns coronary calcifications from other cardiac calcifications. in 14 ct scans containing 61 coronary calcifications, 46 (75%) are detected at the expense of on average 0.9 false positive objects per scan.
substring alignment method for lexicon based handwritten chinese string recognition and its application to address line recognition. this paper presents a lexicon based method for chinese string recognition. in this method, we recognize a chinese string image as a whole by matching it against lexicons in a database. we first over-segment the input line image into a series of radicals and recognize all the possible radical combinations. we then search for candidate lexicons in a given database according to the extracted keywords. each lexicon is compared with the image to find the best match between the radicals and the given string by substring alignment. in this process, the segmentation and recognition results are determined synchronously. our method is tested on 500 handwritten images of chinese address and achieves a correct rate of 87% in address match.
identification of degraded traffic sign symbols by a generative learning method. we present a novel training method for recognizing traffic sign symbols undergoing image degradations. in order to cope with the degradations, it is desirable to use similarly degraded images as training data. our method artificially generates these data from an original image in accordance with the actual degradations. we experimentally confirmed the usefulness of our method for the camera-based traffic sign recognition.
likelihood word image generation model for word recognition. this paper describes a new word image generation model for word recognition. this model can generate a word image with likelihood based on linguistic knowledge, segmentation and character image. in the recognition process, first, the model generates the word image which approximates an input image best for each of a dictionary of possible words. next, the model calculates the distance value between the input image and each generated word image. the efficiency of the proposed method as evaluated in an experiment using type-written museum archive card images. results show that a recognition rate of 99.8% was obtained, compared with only 70.3%f or a recently published comparator algorithm.
convex quadratic programming for object localization. we set out an object localization scheme based on a convex programming matching method. the proposed approach is designed to match general objects, especially objects with very little texture, and in strong background clutter; traditional methods have great difficulty in such situations. we propose a convex quadratic programming (cqp) relaxation method to solve the problem more robustly. the cqp relaxation uses a small number of basis points to represent the target point space and therefore can be used in very large scale matching problems. we further propose a successive convexification scheme to improve the matching accuracy. scale and rotation estimation is integrated as well so that the proposed scheme can be applied to general conditions. experiments show very promising results for the proposed method in object localization applications.
synthesis of stereoscopic 3d videos by limited resources of range images. recently, depth-image-based rendering has been suggested for 3d video creation, in which a monoscopic video stream is recorded along with a depth (range) video stream and a stereoscopic video stream is then generated at the user end. the fundamental assumption of this framework is the full availability of range images at video rate. in this work we alleviate this hard demand and assume that only limited resources of range images are available, i.e. corresponding range images exist for some, but not all, color images of the monoscopic video stream. we propose to synthesize the missing range images between two consecutive range images. our approach eases the recording of 3d material by using less expensive range sensors and enables to enhance existing 2d video material with 3d effect by limited manual overhead. experiments on real videos have demonstrated very encouraging results. especially, one 3d video was generated from a 2d video without any sensory 3d data available at all. in a quality evaluation using an autostereoscopic 3d display the test viewers have attested similar 3d video quality for our synthesis technique and rendering based on depth ground truth.
face and head detection for a real-time surveillance system. this paper describes a face and head detection method for a real-time surveillance system. since there is no guarantee that surveillance cameras can capture frontal face or full-body of human, face and head detection has an advantage for the practical use. proposed method employs four directional features and linear discriminant analysis. it can detect face and head simultaneously, and reduce computation cost. in the experiments, comparison of two classifiers and evaluation of proposed human detection method were performed using still images and video scenes. the results showed that the performances of two classifiers were almost equivalent. thus, the classifier labeled face samples to one class was better in terms of computation cost. in the human detection experiment, the results were 87.2% (48/55) for human detection rate, and 83.6% (832/995) for reliability of detection. the proposed detection method was implemented on a pc and run at approximately over 10fps for vga input with motion detection.
reference point detection for fingerprint recognition. a fingerprint recognition algorithm needs to recover the pose transformation between the input fingerprint and the template. one solution is to determine a unique reference point for fingerprint alignment. this work develops a new algorithm to detect a unique reference point consistently for all types of fingerprints. our detection algorithm works on the orientation field smoothed with an adaptively varying neighborhood. the adaptive window is used to attenuate noise of orientation field effectively while maintaining the detailed orientation information in the high curvature area. a new approach of reference point localization is proposed that is based on hierarchical analysis of the orientation coherence. experiments demonstrate that our developed algorithm consistently locates a unique reference point with high accuracy for all types of fingerprints.
perfect perspective projection using a varifocal mirror and its application to three-dimensional close-up imaging. an imaging system with a focusing mechanism based on perfect perspective projection was devised using a varifocal mirror to achieve high-quality three-dimensional imaging and precise measurement of shapes. it was constructed so that the varifocal mirror was placed at the object focal point of the image-taking lens. the focal point of the lens was fixed when focusing with the varifocal mirror. magnification was exactly equal to the ratio of the focal length and the object point distance from the object focal point of the lens. this held at magnifications larger than unity. the surface shape of a spherical dent (3.5 mm in diameter) could accurately be measured with the shape-from-focus method because the dent could be viewed from the side in the perspective projection system.
supervised evaluation methodology for curvilinear structure detection algorithms. curvilinear structures are useful features in a variety of applications. compared to other commonly used features such as edges, there is relatively few work on curvilinear structure detection and its performance evaluation. in this paper we propose a novel supervised methodology for evaluating the performance of curvilinear structure detection algorithms. we consider the two aspects of performance, namely detection rate and detection accuracy, separately, in contrast to their mixed handling in earlier approaches that typically produces biased impression of detection quality. by doing so, the proposed performance measures giveus a more informative and precise performance characterization. we will demonstrate the advantages of our approach using both synthetic and real examples.
a compact model of human postures extracting common motion from individual samples. model-based markerless human motion capture is ofien affected by instabilities of estimation mainly due to high degrees of freedom and inaccuracies in the body model. the authors propose a compact model of human postures which extracts common motion across different persons from individual samples. our analysis on motion capture data shows that individualities appear as constant offsets that represent individual figures. the proposed model compactly describes the variations of postures in common motion by using a low-dimensional linear model. experimental results show that our model gives moderate constraints to improve the accuracy of posture estimation from a single image of an unknown person whose body size is unknown.
graph based image matching. given two or more images, we can define different but related problems on pattern matching such as image registration, pattern detection and localization, and common pattern discovery. these problems have different levels of purpose and difficulties, as a result, often associate with different solutions. in this paper, we propose a novel approach to solve these problems under a unified framework based on graph matching. we first split the images into small blocks and represent each block as a node in a bipartite graph. a maximum weighted bipartite graph matching algorithm is then employed in an iterative way to find the best transformation set. experimental results show that our approach can handle rotation, scaling and translation, as well as distortion and occlusion. another virtue of our approach is its efficiency.
shape alignment by learning a landmark-pdm coupled model. this paper revisits the model-based approaches for groupwise shape alignment. the key contribution is modeling the landmarks instead of considering them as nodes sliding along the shape contour. the shape group is thus modeled by a landmark-pdm coupled model instead of a constrained point distribution model (pdm). this coupled model is estimated by a stable four-stage estimation algorithm. there are two significant achievements. first, shapes are aligned in a fully unsupervised manner - both the number and location of landmarks are automatically decided. second, extremely noisy and largely deformed shapes can be robustly aligned. these are demonstrated using both synthesized and real data.