waashk/dblp huggingface dataset:
matching images features in a wide base line with ica descriptors. in this paper we present a method to recognize images features with a wide base line between learning and recognition phases. the method is based in feature descriptors derived from independent component analysis (ica). this technique is inspired by the problems of mobile robot mapping and localization using single camera. in the learning phase the descriptors are created to capture the variations in the appearance of each feature across a small base line tracking and stored in a database. the recognition phase proceeds to match descriptors created from the incoming video (with a wide base line respect to the learning phase) in the database. the implementation shows good computational performance.
a stereo and color-based method for face pose estimation and facial feature extraction. this paper describes a method to perform face pose estimation and high resolution facial feature extraction on the basis of stereoscopic color images. unlike other approaches no light projection is required at running time. in our method face detection is based on color driven clustering of 3d points derived from stereo. a mesh model is registered with the post-processed face cluster using a variant of the iterative closest point algorithm (icp). pose is derived from correspondence. then, pose and model information is used for face normalization and facial feature localization. results show, stereo and color are powerful cues for finding the face and its pose under a wide range of poses, illuminations and expressions (pie). head orientation may vary in out of plane rotations up to ± 45°.
knowledge based image enhancement using neural networks. in this paper we combine the concept of adaptive filters with neural networks in order to be able to include high level knowledge about the contents of the image in the filtering process. adaptive image enhancement algorithms often utilize low level knowledge like gradient information to guide filtering parameters. the advantage is that these filters do not need any specific knowledge and can thus be applied to a broad spectrum of images. however, for many problems this low level information is not sufficient to achieve good results. for example in medical imaging it is often very important that some features are preserved while others are suppressed. usually these features cannot be distinguished by low level information. therefore we propose a method to incorporate high level knowledge in the filtering process in order to adjust the parameters of any given filter thus creating a guided filter. we present a scheme for acquiring this high level knowledge which allows us to apply our method to all kinds of images using pattern recognition and special preprocessing techniques. the design of the guided filter itself is easy as for the high level knowledge only some sample pixels including their neighborhood and the desired parameters for these pixels are necessary.
luminance quasi-preserving color quantization for digital steganography to palette-based images. this paper proposes a method to apply bpcs-steganography that we have already proposed for gray scale images to palette-based images which consists of a palette storing color vector information and an index image whose pixel value is corresponding to a index in the palette. a palette-based images can be represented by combining r g and b color component images. we embed secret information into the g images. a number of color vectors in a palette after embedding by bpcs would be over the maximum number, which is usually 256. in order to reduce the number of colors, the rest two component images are then changed in a way that minimizes the square error. the idea behind the color quantization is that the degrading of images manipulated to reduce color is worse than the degrading which occurs with the embedding.
prominent symmetry points as landmarks in finger print images for alignment. for the alignment of two fing erprints position of certain landmarks are needed. these should be automatically extracted with low misidentification rate. as landmarks we suggest the prominent symmetry points (core-points) in the fing erprint. they are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. complex filter s, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. experimental results are reported.
human tracking using floor sensors based on the markov chain monte carlo method. the aim of this paper is to develop a human tracking system that is resistant to environmental changes and covers wide area. simply structured floor sensors are low-cost and can track people in a wide area. however, the sensor reading is discrete and missing; therefore, footsteps do not represent the precise location of a person. a markov chain monte carlo method (mcmc) is a promising tracking algorithm for these kinds of signals. we applied two prediction models to the mcmc: a linear gaussian model and a highly nonlinear bipedal model. the gaussian model was efficient in terms of computational cost while the bipedal model discriminated people more accurate than the gaussian model. the gaussian model can be used to track a number of people, and the bipedal model can be used in situations where more accurate tracking is required.
process mapping and functional correlation in surface metrology: a novel clustering application. surface finish of engineering components is measured and controlled to achieve a desired function. surface finish can also be used to provide feedback to the manufacturing process. an area of active research in surface metrology is in developing tools and techniques for providing greater insight into the relationship between surface texture, a component's function and the manufacturing process. in this context, this paper explores the use of different clustering techniques such as k-means, isodata and neural networks to relate surface metrology data to a component's function and the manufacturing process that produced the part.
model-based segmentation of leukocytes clusters. human leukocytes (white blood cells) can be divided into about twenty subclasses and the estimation of their distribution, called differential counting, is an important diagnostic tool in various clinical settings. automatic differential counters based on digital image analysis require good segmentation algorithms to locate each cell and the accuracy of the subsequent classification depends on the correct segmentation of solitary cells as well as complex cell clusters.early leukocyte segmentation algorithms relied on various thresholding schemes to locate the nucleus and cytoplasm of solitary cells but could not handle clusters. recently we described a complete segmentation procedure that solves the cluster-separation problem using moving interface models and a model-based combinatorial optimization scheme. in this paper, the algorithm is improved and its accuracy is evaluated.
statistical classification of raw textile defects. in this paper, the problem of classification of defects occurring in a textile manufacture is addressed. a new classification scheme is devised in which different features, extracted from the gray level histogram, the shape, and co-occurrence matrices, are employed. these features are classified using a support vector machines (svm) based framework, and an accurate analysis of different multi-class classification schemes and svm parameters has been carried out. the system has been tested using two textile databases showing very promising results.
biometric identification of mice. we present a new application area for biometric recognition: the identification of laboratory animals to replace today's invasive methods. through biometric identification a non invasive identification technique is applied with a code space that is restricted only by the uniqueness of the biometric identifier in use, and with an error rate that is predictable. in this work we present the blood vessel pattern in a mouse-ear as a suitable biometric identifier used for mouse identification. genuine and impostor score distributions are presented using a total of 50 mice. an eer of 2.5% is reported for images captured at the same instance of time which verifies the distinctive property of the biometric identifier.
object-based video coding using pixel state analysis. in archiving video for surveillance, frame-based coding has been used and it makes storage size large because the whole image is stored even if there is no object in the image. on the other hand, object-based coding has the capability to make storage size small, because it distinguishes between the foreground and the background regions of the image, and stores only foreground objects such as people. this paper describes object-based coding by pixel state analysis. in our method, pixel state analysis detects the foreground objects and background regions in video frames. furthermore, it distinguishes foreground object pixels as stationary or transient pixels. for stationary pixels, it is possible to restore the color intensity by refering to the same pixel location in the last frame. therefore, our method makes the storage size smaller. additionally, the transient pixels of foreground objects are compressed using lzh codec. since lzh codec uses lossless compression, the object region can be compressed with lesser loss in image quality. we have evaluated our system over 9 test sequences and obtained an improvement of 15% in compression ratio and better quality for the moving parts of the object region compared to mpeg-4.
correcting show-through effects on document images by multiscale analysis. this paper describes a new approach to restoring color document images where the backside image shows through the paper sheet. a new framework is presented for correcting show-through components using digital image processing techniques. first, the foreground components on the front side are separated from the background and backside components through locally adaptive binarization for each color component and edge magnitude thresholding. background colors are estimated locally through color thresholding to generate a restored image, and then corrected adaptively through multiscale analysis along with comparison of edge distributions between the original and the restored image. the proposedmethod is able to correct unneeded image components through analysis of the front side image alone. experimental results are given to verify effectiveness of the proposed method.
3d reconstruction from uncalibrated cameras and uncalibrated projectors from shadows. recently, projector camera systems have been studied extensively for designing man machine interaction systems. in this paper, we propose a new method for calibrating uncalibrated projector camera systems. in particular, we show that it is possible to calibrate camera projector systems projectively and reconstruct 3d information reliably by using shadows of objects made my projector lights. we also show that by using the projectively calibrated projector camera systems we can achieve virtual music instruments, such as virtual piano.
1d-hmm for face verification: model optimization using improved algorithm and intelligent selection of training images. in this paper, we present an optimized version of 1d-hmm for real-time face verification. dct coefficients of face images are used as observation vectors in hmm states. three modifications have been proposed to improve the overall performance of the approach: (1) replacing baum-welch algorithm with a clustering algorithm, (2) adding a clustering performance measure to the clustering algorithm and (3) selecting an intelligent training set among available images in data set. despite its lower computational complexity, this approach shows better verification performance compared with other 1d-hmm methods. the proposed algorithm has been successfully tested on the well known orl face data set, exhibiting an accuracy of 96%. this is more than 10% higher than the verification results of the classical 1d-hmms and is comparable with the results obtained with 2d-hmms, which is much more complex than 1d-hmm.
laplacian based non-linear diffusion filtering. this paper aims to introduce a diffusion filtering based on the laplacian map. classical non-linear diffusion filtering using the gradient-map-controlled local diffusivity. the laplacian maps has similar geometric properties with the gradient map for the extraction of the region boundaries. laplacian-based diffusion function has the same property with the perona-malik type and the weickert type diffusion functions for the small scale. however, for the large scale, the diffusion operation has similar geometrical properties with the linear diffusion filtering. therefore, the filtering operation in this paper provides a method for the combination of hierarchical expression based on linear and non-linear diffusion filtering operations.
view dependent enhancement of the dynamic range of video. there are many applications for computer vision where a scene observed contains a wide range of brightness. often, the low dynamic range of a camera limits the accuracy of information that can be extracted from the video. frames may contain saturated pixels of bright targets, poor resolution and noisy data for dark regions, or both. in this paper, we propose a method for generating high dynamic range (hdr) videos by combining successive frames. the first phase is to set the exposures for each frame contributing to one hdr frame. the exposures are automatically adapted according to image contents to provide a maximum amount of information about the target. hdr frames are then combined in a maximum likelihood manner, based on the noise model of image acquisition. experiments for texture based classification show that utilizing a proposed methodology, even the hdr videos built in real-time, contribute to many topical vision systems.
adaptive fusion for diurnal moving object detection. fusion of different sensor types (e.g. video, thermal infrared) and sensor selection strategy at signal or pixel level is a non-trivial task that requires a well-defined structure. in this paper, we provide a novel fusion architecture that is flexible and can be adapted to different types of sensors. the new fusion architecture provides an elegant approach to integrating different sensing phenomenology, sensor readings, and contextual information. a cooperative coevolutionary method is introduced for optimally selecting fusion strategies. we provide results in the context of a moving object detection system for a full 24 hours diurnal cycle in an outdoor environment. the results indicate that our architecture is robust to adverse illumination conditions and the evolutionary paradigm can provide an adaptable and flexible method for combining signals of different modality.
stereo correspondence using stripe adjacency graph. this paper presents a new approach to solve the problem of stereo correspondence. in order to extract visible surfaces with similar texture from two different images, our method encodes input images to a series of so-called stripe adjacency graphs (sag). a stripe is a connected region with only one segment on each scan-line and a sag is a group of stripes with neighbourhood relationships. the algorithm retrieves surfaces from binocular images according to the matching degree between two sub-graphs in two images. the extracted surfaces are evaluated by the global matching cost that is defined as the sum of inter-stripe and intra-stripe energy. the experimental results show that our algorithm is a fast as well as an effective algorithm. it can give a dense disparity map.
high-resolution video generation using morphing. in imaging devices such as ccds, there is a trade-off between the image resolution and frame rate because of the limitation of data transfer speed. this creates difficulties in producing a high-quality imaging system using only one sensor. therefore, we use a camera that has two imaging sensors with different spatio-temporal resolutions. the camera captures a high-resolution image sequence at a low frame rate and a low-resolution image sequence at a video rate. we propose using an image-morphing method to generate high-resolution video sequences from these two image sequences.
simultaneous determination of object shape and color by moire analysis using a reflection model. a method for determining the shape and color of an object simultaneously based on moiré analysis using a reflection model is presented. an rgb color system is employed to represent color information, and the color of an object is determined from the reflectance of rgb planes. utilizing color consistency and the specular reflection of reflected light, reflectance ambiguity for each rgb plane is reduced when using even a single light source, significantly improving the reliability of shape and reflectance determination for each rgb plane.
recovering non-overlapping network topology using far-field vehicle tracking data. this paper presents a weighted statistical method to learn the environment's topology using a large amount of far field vehicle tracking data collected by multiple, stationary non-overlapping cameras. first, an appearance model is constructed by the combination of normalized color and overall model size to measure the moving object's appearance similarity across the non-overlapping views. then based on the similarity in appearance, weighted votes are used to learn the temporally correlating information and hence to estimate the mutual information. by exploiting the statistical spatio-temporal information, our method can automatically learn the possible links between disjoint views and recover the topology of the network. the effectiveness of the proposed method is demonstrated by experimental results both on simulated and real video surveillance data.
bayesian approach with nonlinear kernels to feature extraction. presented here is a new algorithm for finding the positions of features in images of samples from particular object classes, such as human faces. existing algorithms that address this problem mostly deal only with image variations resulting from simple translation in the image plane, as well as differences of objects in the classes, by searching for features across the image plane. in our new algorithm, larger classes of image variations, including those resulting from object rotation in 3d space and scaling (i.e. translation in depth) are handled, in addition to image plane translation. in order to do this, we develop a new kernel-based map (maximum a posteriori) estimation technique using gaussian distribution in a potentially higher dimensional space to model the relationship between images and feature positions. experimental results of facial feature extraction in images of human faces taken from varying viewing directions and from varying distances demonstrate the superior performance of the new method relative to that of existing algorithms.
2d cascaded adaboost for eye localization. in this paper, 2d cascaded adaboost, a novel classifier designing framework, is presented and applied to eye localization. by the term "2d, we mean that in our method there are two cascade classifiers in two directions: the first one is a cascade designed by bootstrapping the positive samples, and the second one, as the component classifiers of the first one, is cascaded by bootstrapping the negative samples (please refer to fig.1). the advantages of the 2d structure include: (1) it greatly facilitates the classifier designing on huge-scale training set; (2) it can easily deal with the significant variations within the positive (or negative) samples; (3) both the training and testing procedures are more efficient. the proposed structure is applied to eye localization and evaluated on four public face databases, extensive experimental results verified the effectiveness, efficiency, and robustness of the proposed method.
biometrics based asymmetric cryptosystem design using modified fuzzy vault scheme. we propose a novel biometrics cryptosystem where one can send and receive secure information using just the fingerprints. this cryptosystem is a judicious blend of the asymmetric cryptosystem like rsa and the symmetric fuzzy vault scheme having the advantages of both the aforementioned cryptosystems. we have proposed a modification of the fuzzy vault scheme to make it more robust against variations in the values of biometric features. finally we propose the use of invariant features as a key to producing a hierarchical security system where the same key (fingerprint) can be used to generate encrypted messages at different levels of security.
statistical borders for incremental mining. data streams - data flows in which the information arrives in a timely manner - have recently become a major subfield of knowledge extraction. one of their most important singularity is that only a part of the information remains available at a time, which makes it necessary to cope with uncertainty. in this paper, we introduce a novel statistical approach which biases the initial support for patterns mining. this approach holds the advantage to maximize one of two parameters (precision or recall) chosen by the user, while guaranteeing a statistical near optimal degradation of the other. this leads us to introduce the statistical borders, the relevant sets of frequent patterns in incremental mining of data streams. experiments performed on sequential patterns demonstrate the potential of this approach.
a coupon classification method based on adaptive image vector matching. this paper describes a coupon classification system based on image vector matching. this method features following two points. (1) extract a feature vector from a gray-scale image using a feature map which is derived from training coupon images. (2) classify a coupon image by adaptive mask distance to cope with the recognition difficulty such as partial cutting of coupon and putting stamp on it. we have implemented this method and experimented with collected samples. it achieved 100% of recognition rates, processing speed 11.76msec/sheet to 969 images for 42 kinds of coupon samples.
improving clustering algorithms through constrained convex optimization. inspired by the recent successes of boosting algorithms, a trend in unsupervised learning has begun to emphasize the need to explore the design of weighted clustering algorithms. in this paper, we handle clustering as a constrained minimization of a bregman divergence. theoretical results show benefits resembling those of boosting algorithms, and bring new modified weighted versions of clustering algorithms such as k-means, expectation-maximization (em) and k-harmonic means. experiments display the quality of the results obtained, and corroborate the advantages that subtle data reweightings may indeed bring to clustering.
grouping with bias for distribution-free mixture model estimation. some authors have recently devised adaptations of spectral grouping algorithms to integrate prior knowledge, as constrained eigenvalues problems. in this paper, we adapt recent statistical grouping algorithms to this task, as a non-parametric mixture model estimation problem. the approach appears to be attractive for its theoretical benefits, and its experimental results, as light bias brings dramatic improvements over unbiased approaches on hard images.