waashk/dblp huggingface dataset:
a combination of generative and discriminative approaches to object detection. this paper presents a new simple algorithm which combines generative and discriminative approaches to object detection. the research makes two key contributions. the first contribution is the introduction of a new algorithm called the dt(decomposition-tree) which is capable of clustering on the manifold of object patterns(using gaussian clusters) and determining the thresholds of each cluster by using hard samples which are selected during learning. the second contribution is that the learning time of the dt algorithm has been reduced rapidly. because the dt algorithm shows spatial relationships of training patterns in the form of a tree, it requires relearning rather than new learning. to evaluate the performance of the proposed object detection algorithm, we experimented with face detection. the dt algorithm yields face detection performance comparable to that of the best previous systems[4]
using sphere images for calibrating fisheye cameras under the unified imaging model of the central catadioptric and fisheye cameras. this paper proposes a novel calibration method for fisheye cameras using sphere images which is an extension of the calibration method for central catadioptric cameras. we show that, each sphere image under fisheye cameras is tangent to the modified calibrating conic (mcc) at two double-contact image points. the calibration method is proposed by first finding the double-contact image points, then fitting the mcc, and final obtaining the intrinsic parameters using the ldlt factorization of the mcc.
stereo camera based non-contact non-constraining head gesture interface for electric wheelchairs. for persons with severe disabilities who find it difficult to operate the joystick or the chin stick of an electric wheelchair, we have developed an interface that allows the wheelchair to be operated by gestures such as head movements. by using a camera to perform visual sensing, we aim to implement a non-contact non-constraining interface where no wires or equipment of any kind have to be attached to the user, thereby making the interface much more convenient to use. range information obtained from a stereo camera is used to control the electric wheelchair in any situation regardless of whether it is used indoors or outdoors. we are keeping this study user-oriented by performing clinical trials with the help of actual users to determine where the camera should be situated, how it should be used, and so on.
skin color detection using multiple cues. in this paper, we present a novel space transformation to describe the skin and non-skin attributes, and build a new non-linear skin color classifier combining the spatial and probabilistic distributions of pixels. to weaken the illumination effect on images, we introduce a new gamma correction (gc) method. experimental results show that our approach has good performance in skin color detection.
binarization and recognition of degraded characters using a maximum separability axis in color space and gat correlation. this paper proposes a new technique of binarization and recognition of characters in color with a wide variety of image degradations and complex backgrounds. the key ideas are twofold. one is to automatically select one axis in the rgb color space that maximizes the between-class separability by a suitably chosen threshold for segmentation of character and background or binarization. the other is affine-invariant or distortiontolerant grayscale character recognition using global affine transformation (gat) correlation that yields the maximum correlation value between input and template images. in experiments, we use a total of 698 test images extracted from the public icdar 2003 robust ocr dataset containing a variety of single-character images in natural scenes. in advance, we classify those images into seven groups according to the degree of image degradations and/or background complexity. on the other hand, we only prepare a single-font set of 62 alphanumerics for templates. experimental results show an average recognition rate of 81.4%, ranging from 94.5% for clear images to 39.3% for seriously distorted images.
a novel approach to detecting adult images. this paper presents a novel approach to recognizing adult images. to effectively detect the rois (region of interest) with plentiful skin information, we structurize an image with regions and points. then based on the rois, we obtain some reliable features for image classification. experimental results show that our algorithm performs well in detecting objectionable images.
line-based affine invariant object location using transformation space decomposition. this paper presents a novel line-based affine invariant object location methodology. our algorithm employs a new line-based transformation space decomposition technique to exploit intrinsic structural information provided by line features. furthermore, we propose a new line-based distance transform to integrate with our algorithm to provide efficient transformation cell evaluation and subdivision in a coarse to fine manner. the algorithm is able to rapidly accelerate the searching process while maintaining high discriminative power and minimal storage requirement. the efficiency and discriminative power of this methodology are demonstrated using real-world examples with promising results.
human perception based color image quantization. we present a new algorithm for color image quantization based on human color perception properties. we construct two kinds of map by analyzing the spatial color distributions to take account of the human visual system; homogeneity map (h-map) and distinctiveness map (d-map). then, we assign weight value to all color vectors by combining these maps to consider two factors at the same time. to extract representative colors, we define a new cost function and use the lkma (local k-means algorithm) with weighted color vectors. in this stage, we utilize an incremental splitting scheme with a penalty term to determine optimal number of clusters adaptively. the experimental results show that proposed algorithm reproduces an image preserving significant local features while removing unimportant details of an original image from the viewpoint of human.
multi-snr gmms-based noise-robust speaker verification using 1/fa noises. environmental noises greatly degrade the performance of speaker verification system. this paper designs a novel noise-robust speaker verification system based on multi- snr techniques using 1/f\alpha noises. during training phase, the 1/f\alpha noises are employed to simulate environmental noises. to simplify the computation load, an optimal value of a is selected. to evaluate the performance of the system, speaker verification experiments are conducted on 5 kinds of environmental noises: "airport", "bus", "lobby", "restaurant", and "office" at 0db snr. the experimental results demonstrate that a notable degradation of verification error rates is obtained while using 1/f\alpha noises.
reconstruction of 3d face model from single shading image based on anatomical database. we propose a method to reconstruct 3d face model from a single face-frontal shading image based on a database of the original 3d face shapes which are created according to anatomical basis of the human head. in the proposed method, we reconstruct the 3d shape of the face from the input shading image by estimating small number of eigen- values taken by principal component analysis(pca) of the anatomical database of the head, rather than directly recov- ering the shape from the input shading image. the eigen- values are estimated based on the optimization of the error value computed by comparing shading information of input image to that of model image created from the shape rep- resented by the eigenvalues. for evaluating the effective- ness, we reconstruct various face shapes from single shad- ing images of the objects based on the proposed method. the reconstructed shape for a statue of head with the av- erage shape of the databases provides an error evaluation, which is 2.6mm in average. this is sufficiently accurate for the future applications, such as on-line order-made of face- wearing products.
multiple pedestrian detection and tracking based on weighted temporal texture features. this paper presents a novel method for detecting and tracking pedestrians from video images taken by a fixed camera. a pedestrian may be totally or partially occluded in a scene for some period of time. the proposed approach uses the appearance model for the identification of pedestrians and the weighted temporal texture features. we compared the proposed method with other related methods using color and shape features, and analyzed the features' stability. experimental results with various real video data revealed that real time pedestrian detection and tracking is possible with increased stability over 5-15% even under occasional occlusions in video surveillance applications.
confidence-driven architecture for real-time vision processing and its application to efficient vision-based human motion sensing. in this paper, we discuss a real-time vision architecture which provides a mechanism of controlling trade-off between the accuracy and the latency of vision systems. in vision systems, to acquire accurate information from input-images, the huge amount of computation power is usually required. on the other hand, to realize real-time processing, we must reduce the latency. therefore, under given hardware resources, we must make difficult trade-off between the accuracy and the latency so that the quality of the system's output keeps appropriate. to solve the problem, we propose confidence-driven scheme, which enables us to control the trade-off dynamically and easily without rebuilding vision systems. in the confidence-driven architecture, the trade-off can be controlled by specifying a generalized parameter called confidence, which relatively indicates how accurate the analysis should be. here, we present the concept of confidence-driven architecture, and then, we show a shared memory which uses confidence-driven scheme. using confidence-driven memory, we can use imprecise computation model to reduce the latency without a large decrease of accuracy.
automatic physiognomic analysis by classifying facial component feature. this paper presents a method for generating physiognomic information from facial images, by analyzing features of facial components. the physical personality of the face can be modeled by the combination of facial feature components. the facial region is detected from an input image, in order to analyze the various facial feature components. then, the gender of the subject is subsequently classified, and facial components are extracted. the active appearance model (aam) is used to extract facial feature points. from these facial feature points, 16 measures are computed to distinguish each facial component into defined classes, such as large eye, small mouth, and so on. after classifying facial components with each classification criterion and gender of subject, physiognomic information is generated by combining the classified results of each classification criteria. the proposed method has been tested with 200 persons' samples. the proposed method achieved a classification rate of 85.5% for all facial components feature.
emotional speech analysis on nonlinear manifold. this paper presents a speech emotion recognition system on nonlinear manifold. instead of straight-line distance, geodesic distance was adopted to preserve the intrinsic geometry of speech corpus. based on geodesic distance estimation, we developed an enhanced lipschitz embedding to embed the 64-dimensional acoustic features into a sixdimensional space. in this space, speech data with the same emotional state were located close to one plane, which was beneficial to emotion classification. the compressed testing data were classified into six archetypal emotional states (neutral, anger, fear, happiness, sadness and surprise) by a trained linear support vector machine (svm) system. experimental results demonstrate that compared with traditional methods of feature extraction on linear manifold and feature selection, the proposed system makes 9%-26% relative improvement in speaker-independent emotion recognition and 5%-20% improvement in speaker-dependent.
face representation by using non-tensor product wavelets. this paper presents a new approach to represent face by using a non-tensor product bivariate wavelet filters. a new non-tensor product bivariate wavelet filter banks with linear phase are constructed from the centrally symmetric matrices. our investigations demonstrate that these filter banks have a matrix factorization and they are capable of representing facial features for recognition. the implementations of our algorithm are made of three parts: first, face images are represented by the lowest resolution subbands after 2-level new non-tensor product wavelet decomposition. second, the principal component analysis (pca) feature selection scheme is adopted to reduce the computational complexity of feature representation. finally, support vector machines (svm) is applied for classification.the experimental results show that our method is superior to other methods in terms of recognition accuracy and efficiency.
document image ground truth generation from electronic text. the problem of generating synthetic data for the training and evaluation of document analysis systems has been widely addressed in recent years. with the increased interest in processing multilingual sources, however, there is a tremendous need to be able to rapidly generate data in new languages and scripts, without the need to develop specialized systems. we have developed an approach, which uses language support of the ms windows operating system combined with custom print drivers to render tiff images simultaneously with windows enhanced metafile directives. the metafile information is parsed to generate zone, line, word, and character ground truth including location, font information and content in any language supported by windows. the resulting images can be physically or synthetically degraded, and used for training and evaluating ocr systems. in this paper, we briefly survey related work and describe our system.
comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. this paper compares different similarity measures used for trajectory clustering in outdoor surveillance scenes. six similarity measures are presented and the performance is evaluated by correct clustering rate (ccr) and time cost (tc). the experimental results demonstrate that in outdoor surveillance scenes, the simpler pca+euclidean distance is competent for the clustering task even in case of noise, as more complex similarity measures such as dtw, lcss are not efficient due to their high computational cost .
neighborhood discriminant projection for face recognition. we propose a novel manifold learning approach, called neighborhood discriminant projection (ndp), for robust face recognition. the purpose of ndp is to preserve the within-class neighboring geometry of the image space, while keeping away the projected vectors of the samples of different classes. for representing the intrinsic within-class neighboring geometry and the similarity of the samples of different classes, the within-class affinity weight and the between-class affinity weight are used to model the within-class submanifold and the between-class submanifold of the samples, respectively. comprehensive comparisons and extensive experiments on face recognition are performed to demonstrate the effectiveness and robustness of our proposed method.
learning-based license plate detection using global and local features. this paper proposes a license plate detection algorithm using both global statistical features and local haar-like features. classifiers using global statistical features are constructed firstly through simple learning procedures. using these classifiers, more than 70% of background area can be excluded from further training or detecting. then the adaboost learning algorithm is used to build up the other classifiers based on selected local haar-like features. combining the classifiers using the global features and the local features, we obtain a cascade classifier. the classifiers based on global features decrease the complexity of the system. they are followed by the classifiers based on local haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. the encouraging detection rate is achieved in the experiments.
geodesic closest point constrained inter-subject non-rigid registration. in this paper, we propose an inter-subject brain registration method by combining the intensity and the geodesic closest point based similarity metric. each of the brain hemisphere can be topologically equalized into a sphere. the inter-subject variance can be better reduced by using cortical surface based analysis method[high-resolution intersubject averaging and a coor-dinate system for the cortical surface, surface-based analysis i: segmentation and surface reconstruc-tion]. a one to one mapping of the points on spherical surfaces of two subjects can be achieved by using this technique. we find the geodesic correspondence between subjects by using spherical registration first. then the correspondence on the cortical surface between subjects are used to guide the volumetric inter-subject registration. by adding these anatomical constraints of the cortical surface, the inter-subject registration result will be more anatomically meaningful and accurate. the cortical surface correspondence between subjects can be combined with the general non-rigid registration. in our experiments, the proposed method performs better than the method of hartkens et al [using points and surfaces to improve voxel-based non-rigid registration].
supervised learning for guiding hierarchy construction: application to osteo-articular medical images database. most merging and splitting segmentation methods aim to construct a hierarchical structure from an image by minimizing or maximizing a homogeneity measure. this latter generally includes radiometrical information, but rarely includes geometrical information and ignore the high level information on the image content. moreover, the hierarchies issued from these approaches may suffer from a structural instability and deficiency in the "semantic" of the regions related to the image content and to the energy or the criterion which does not contain any high level prior knowledge. in this paper, we propose to improve the semantic content of the hierarchy by adding a new term called "contextual cost". this term integrates the prior knowledge on the image, derived from a classifier after a supervised learning on the semantic classes composing the image. its purpose is to better drive the merging process in the construction of meaningful regions by penalizing spurious fusions.
elastic face, an anatomy-based biometrics beyond visible cue. this paper describes a face recognition method that is designed based on the consideration of anatomical and biomechanical characteristics of facial tissues. elastic strain pattern inferred from facial expression can reveal an individual's biometric signature associated with the underlying anatomical structure, and thus has the potential for face recognition. a method based on the continuum mechanics in finite element formulation is employed to compute the strain pattern. experiments show very promising results. the proposed method is quite different from other face recognition methods, and both its advantages and limitations, as well as future research for improvement are discussed.
super-resolution restoration of facial images in video. reconstruction-based super-resolution has been widely treated in computer vision. however, super-resolution of facial images has received very little attention. since different parts of a face may have different motions in normal videos, this paper proposes a new method for enhancing the resolution of low-resolution facial image by handling the facial image non-uniformly. we divide low-resolution face image into different regions based on facial features and estimate motions of each of these regions using different motion models. our experimental results show we can achieve better results than applying super-resolution on the whole face image uniformly.
filament preserving segmentation for sar sea ice imagery using a new statistical model. modelling spatial context constraints using markov random field (mrf) has been widely used in the segmentation of noisy images [7][10][17]. its applicability to sar sea ice segmentation has also been demonstrated recently [6]. however, most existing mrf models are not capable of preserving filaments, specifically leads and ridges for sar sea ice, which are valuable for ship navigation applications and helpful for identifying certain ice types. a new statistical context model is proposed that can preserve such narrow elongated features while producing similar smooth segmentation results as those of existing mrf based approaches.
point pattern matching for articulated or multiple objects. an efficient point pattern matching algorithm for articulated and multiple objects is presented in this paper. a local to global strategy is adopted to get three layered matches starting from an initial partition of the point set using clustering method. firstly, initial match with three point correspondences is obtained through local neighborhood and exhaustive search. then, cental match is expanded from initial match by alternating between next point matching and transformation update. finally, ambiguous boundary points are matched and classified into their correspondent parts using the estimated alignment transformations, and missing or extra points are detected and rejected as outliers. experiments on real images present satisfying results.
boosted markov chain monte carlo data association for multiple target detection and tracking. in this paper, we present a probabilistic framework for automatic detection and tracking of objects. we address the data association problem by formulating the visual tracking as finding the best partition of a measurement graph containing all detected moving regions. in order to incorporate model information in tracking procedure, the posterior distribution is augmented with adaboost image likelihood. we adopt a mrf-based interaction to model the inter-track exclusion. to avoid the exponential complexity, we apply markov chain monte carlo (mcmc) method to sample the solution space efficiently. we take data-oriented sampling driven by an informed proposal scheme controlled by a joint probability model combining motion, appearance and interaction among detected regions. proposed data association method is robust and efficient, capable of handling extreme conditions with very noisy detection.