waashk/dblp huggingface dataset:
an unsupervised algorithm for anchor shot detection. in this paper we present a novel algorithm for anchor shot detection (asd). asd is a fundamental step for segmenting news video into stories that is among key issues for achieving efficient treatment of news-based digital libraries. the proposed algorithm firstly uses a clustering method for individuating candidate anchor shots and then employs a two-stage pruning technique for reducing the number of falsely detected anchor shots. both clustering and pruning are carried out in an unsupervised way. the algorithm has been tested on a wide database and compared with other state-of-the-art algorithms, demonstrating its effectiveness with respect to them.
fair: towards a new feature for affinely-invariant recognition. in this paper we propose the first version of fair, a low-dimensional image neighborhood descriptor that shows performance comparable to sift introduced by lowe. the dimension of fair we tested is 30, compared to the dimension of 128 in sift. sensitivity of the fair descriptor to skew, rotation, image blur and noise is similar to sift. fair shows better localization in scale-space than sift. several extensions of fair that could improve its performance are discussed.
iterative error bound minimisation for aam alignment. the active appearance model (aam) is a powerful generative method used for modelling and segmenting deformable visual objects. linear iterative methods have proven to be an efficient alignment method for the aam when initialisation is close to the optimum. however, current methods are plagued with the requirement to adapt these linear update models to the problem at hand when the class of visual object being modelled exhibits large variations in shape and texture. in this paper, we present a new precomputed parameter update scheme which is designed to reduce the error bound over the model parameters at every iteration. compared to traditional update methods, our method boasts significant improvements in both convergence frequency and accuracy for complex visual objects whilst maintaining efficiency.
image classification: classifying distributions of visual features. we classify an image by generating a list of salient visual features present in the luminance channel, and matching the resulting variable-length feature list to categoryspecific generative models for such features. to facilitate quick computation, we use thresholded viola-jones rectangular features, each represented by a five-dimensional descriptor. for each image category, a probability distribution for feature-lists is given by a latent conditional independence (lci) model and classification is maximum likelihood. on the nist tax forms database [3], where intracategory variations include variable scan-lightness, skew, noise, and machine-printed form-filling, our method improves performance over published results, while requiring very little training data, and without relying on an extensive set of handcrafted features.
decoder banks: versatility, automation, and high accuracy without supervised training. a methodology using decoder banks is proposed for high-accuracy, fully automatic recognition of machine printed text across a wide range of challenging image qualities, without requiring manual intervention or supervised training. this approach is made possible by two crucial properties of document image decoding (did) technology: (1) it is trainable for high accuracy across a wide range of explicitly parameterized image degradations; and (2) decoders for arbitrary parameter settings can be generated automatically. we report the results of large-scale experiments on synthetic images which demonstrate that, when many pretrained decoders are applied in parallel to an input image with unknown parameters, the decoder that yields the highest accuracy is often the one that exhibits the highest did posterior 'viterbi score'. when implemented naively, in a brute-force manner, decoder banks are computationally intensive: but we suggest ways that this cost may be reduced with no loss of versatility, automation, or accuracy.
multiple target tracking by appearance-based condensation tracker using structure information. multiple target tracking is a challenging problem, especially when targets are frequently crossing each other. it becomes very difficult and confusing when some targets are often occluded by other targets. this paper proposes a novel tracking method for the problem using an appearance-based condensation tracker. in order to overcome difficulties in the occlusion problem, a target object is regarded as a set of partsthat constrains each other in the target structure. while each part is tracked basically in the condensation method, all the parts cooperate in the drift step of the condensation. experimental results show the effectiveness of the proposed method formultiple person tracking and human face tracking.
3-d modeling of an outdoor scene by multi-baseline stereo using a long sequence of images. three-dimensional (3-d) models of outdoor scenes are widely used for object recognition, navigation, mixed reality, and so on. because such models are often made manually with high costs, automatic 3-d modeling has been investigated. a 3-d model is usually generated by using a stereo method. however, such approaches cannot use several hundreds images together for dense depth estimation because it is dicult to accurately calibrate a large numberof cameras. in this paper, we propose a 3-d modeling method that first estimates extrinsic camera parameters of a monocular image sequence captured by a moving video camera, and then reconstructs a 3-d model of a scene. we can acquire a 3-d model of an outdoor scene accurately by using several hundreds input images.
change detection in streetscapes from gps coordinated omni-directional image sequences. as part of its technology, to achieve quick map updates, we propose a method for automatically detecting changes in streetscapes from images captured by car-mounted omnidirectional cameras. it comprises two stages; accurate alignment of a map and street images taken at various times, and detection of changes in streetscapes from the aligned data. the system will collect data via many free-running cars fitted with low-cost equipment to obtain images at various times and along routes. in the first stage, we process the alignment of the image frames taken at same locations and determine the accurate position information of each frame by a method composed of dimension reduction and dp matching. then in the second stage, we detect changes in streetscapes from images taken at various times. experiments with 44 data items which were collected over about a year, demonstrate the effectiveness of our method.
a color-based tracking by kalman particle filter. in this paper, a method for real-time tracking of moving objects is proposed. we applied kalman particle filter (kpf) to color-based tracking. this kpf is a particle filter including the principle of kalman filter, and it was adopted to the object contour tracking. we modified this kpf for color-based tracking. this modified kpf can approximate the probabilistic density of the position of the tracked object properly and needs fewer particles for tracking than conventional particle filters. we made experiments to confirm effectiveness of this method.
development of omni-directional stereo vision-based intelligent electric wheelchair. this paper describes the development of a support technology to enhance the independent mobility of disabled persons, featuring an electric wheelchair fitted with an innovative camera system "stereo omnidirectional system (sos)". the sos can capture omnidirectional color images and range information in real time, enabling automatic detection and avoidance of dangers in the wheelchair's traveling environment, and has a remote assistance function. this study deals principally with basic platform design, focusing on the basic technology required for application of the sos to electric wheelchairs, such as high-speed, highdefinition integrated processing of omni-directional images, and automatic correction of camera pose.
unsupervised robust clustering for image database categorization. content-based image retrieval can be dramatically improved by providing a good initial database overview to the user. to address this issue, we present in this paper the adaptive robust competition. this algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. in our approach, each image is represented by a high-dimensional signature in the feature space, and a principal component analysis is performed for every feature to reduce dimensionality. image database overview is computed in challenging conditions since clusters are overlapping with outliers and the number of clusters is unknown.
an lbp-based active contour algorithm for unsupervised texture segmentation. this paper presents a novel algorithm for unsupervised texture segmentation. the proposed algorithm incorporates the local binary pattern operator under a segmentation framework based on the active contour without edges model. the experiments performed, show that it can be used for fast segmentation of two-textured images, outperforming recent texture segmentation algorithms, with a segmentation quality that reaches 99% on average.
"eigenphases vs. eigenfaces". in this paper we present a novel method for performing robust illumination-tolerant and partial face recognition that is based on modeling the phase spectrum of face images. we perform principal component analysis in the frequency domain on the phase spectrum of the face images and we show that this improves the recognition performance in the presence of illumination variations dramatically compared to normal eigenface method and other competing face recognition methods such as the illumination subspace method and fisherfaces. we show that this method is robustl even when presented with partial views of the test faces, without performing any pre-processing and without needing any a-priori knowledge of the type or part of face that is occluded or missing. we show comparative results using the illumination subset of cmu-pie database consisting of 65 people showing the performance gain of our proposed method using a variety of training scenarios using as little as three training images per person. we also present partial face recognition results that obtained by synthetically blocking parts of the face of the test faces (even though training was performed on the full face images) showing gain in recognition accuracy of our proposed method.
road extraction by snake with inertia and differential features. in this paper we propose an extraction technique based on an active contour model (snake) considering inertia and differential features of an object in movie. in many methods for tracking a moving object, a snake is applied to a scene frame by frame, and initial positions of control points in a frame refer to results in the previous frame. we focus on inertia which works between previous and present frames. in this paper inertia is the tendency of a control point to resist changes in its state of motion in an image space. we propose an internal energy for snakes based on inertia of control points. proposed method is applied to extract road geometry from a video camera equipped on the head of a vehicle. internal energy functions based on differential features of road geometry is also introduced. experimental results indicate the availability of the proposed method.
unsupervised learning of dense hierarchical appearance represe. we describe an unsupervised, probabilistic method for learning visual feature hierarchies. starting from local, low-level features computed at random locations, the method combines features hierarchically. at each level of the hierarchy, pairs of features are identified that tend to occur at stable positions relative to each other, by clustering the configurational distributions of observed feature cooccurrences using expectation-maximization. stable pairs of features thus identified are combined into higher-level features. this learning scheme results in a graphical model that constitutes a probabilistic representation of a flexible visual feature hierarchy. for detection, evidence is propagated using nonparametric belief propagation, a recent generalization of particle filtering. in experiments, the proposed approach demonstrates effective learning and robust detection of objects in the presence of clutter and occlusion.
unsupervised texture segmentation by spectral-spatial-independent clustering. a novel color texture unsupervised segmentation algorithm is presented which processes independently the spectral and spatial information. the algorithm is composed of two parts. the former provides an over-segmentation of the image, such that basic components for each of the textures which are present are extracted. the latter is a region growing algorithm which reduces drastically the number of regions, and provides a region-hierarchical texture clustering. the over-segmentation is achieved by means of a color-based clustering (cbc) followed by a spatial-based clustering (sbc). the sbc, as well as the subsequent growing algorithm, make use of a characterization of the regions based on shape and context. experimental results are very promising in case of textures which are quite regular.
wavelet denoising of multicomponent images, using a gaussian scale mixture model. in this paper, denoising on multicomponent images is performed. the presented procedure is a spatial waveletbased denoising techniques, based on bayesian leastsquares optimization procedures, using a prior model for the wavelet coefficients that account for the intercorrelations between the multicomponent bands.the applied prior model for the multicomponent signal is a gaussian scale mixture (gsm) model. the method is compared to single-band wavelet denoising and to multiband denoising using a gaussian prior. experiments on a landsat multispectral remote sensing image are conducted.
multiscale watershed segmentation of multivalued images. in this paper, a new segmentation technique for multi-valued images is elaborated. the technique accesses multiscale edge information of a multivalued image by a concept, called multiscale fundamental form. at different scales, an edge map of the multivalued images is obtained, at which a watershed-based algorithm is applied. the multiscale behavior of the obtained watershed regions is employed to conduct a region merging procedure. in order to remove noise or local texture, prior to segmentation an anisotropic diffusion þlter is applied, also making use of the multiscale fundamental forms. in this way, the entire procedure is applied using multivalued processing.
using adapted levenshtein distance for on-line signature authentication. in this paper a new method for on-line signature authentication will be presented, which is based on a event-string modelling of features derived from pen-position and pressure signals of digitizer tablets. a distance measure well known from textual pattern recognition, the levenshtein distance, is used for comparison of signatures and classification is carried out applying a nearest neighbor classifier. results from a test set of 1376 signatures from 41 persons are presented, which have been conducted for four different feature sets. the results are rather encouraging, with correct identification rates of 96% at zero false classifications.
the epipolar geometry of the log-polar image plane. this paper presents an explicit formulation of the epipolar geometry of two oriented log-polar images.a method is derived for efficient pixel-by-pixel computation of epipolar lines in the log-polar plane, which are complicated non-linear functions.as an example, the method is applied for dense stereo matching: it is shown that it is more efficient to perform one-dimensional disparity estimation in the log-polar plane than to remap the images to cartesian coordinates.
off-line handwriting identification using hmm based recognizers. in this paper, an off-line, text independent system for writer identification using hidden markov model (hmm) based recognizers is described. for each writer we build an individual recognizer and train it on text lines written by that writer. a text line of unknown origin is presented to each of these recognizers. as a result we get, from each recognizer, a transcription including the log-likelihood score for the considered input. we rank all scores, and based on the assumption that the recognizer with the highest log-likelihood is the one that has been trained using text lines of this writer, we assign the text line to the writer whose score ranks first. we tested our system using over 2,200 text lines from 50 writers and have in 94.47% of all cases correctly identified the writer. using a simple confidence measure to define a rejection mechanism, we achieved an error rate of 0% by rejecting 15% of the results.
off-linewriter identification using gaussian mixture models. writer identification is the task of determining the author of a sample handwriting from a set of writers. in this paper, we propose gaussian mixture models (gmms) to address the task of off-line, text independent writer identification of text lines. the resulting system is compared to a system that uses a hidden markov model (hmm) based approach. while the gmm based system is conceptually much simpler and faster to train than the hmm based system, it achieves a significantly higher writer identification rate of 98.46% on a data set of 4,103 text lines coming from 100 writers.
performance evaluation metrics for motion detection and tracking. this paper introduces a methodology for evaluating the operational range of a video surveillance system in terms of robustness and reliability. we propose the generation of semi and full-synthetic video sequences under controlled variation of selected parameters. this data provides the necessary ground truth information for evaluating the motion detection and tracking systems. in addition, we propose several error metrics for quantitative evaluation.
a split & merge approach to metric-topological map-building. we present a novel split and merge based method for dividing a given metric map into distinct regions, thus effectively creating a topological map on top of a metric one. the initial metric map is obtained from range data that are converted to a geometric map consisting of linear approximations of the indoor environment. the splitting is done using an objective function that computes the quality of a region, based on criteria such as the average region width (to distinguish big rooms from corridors) and overall direction (which accounts for sharp bends). a regularization term is used in order to avoid the formation of very small regions, which may originate from missing or unreliable sensor data. experiments based on data acquired by a mobile robot equipped with sonar sensors are presented, which demonstrate the capabilities of the proposed method.
fast exact euclidean distance (feed) transformation. fast exact euclidean distance (feed) transformation is introduced, starting from the inverse of the distance transformation. the prohibitive computational cost of a naive implementation of traditional euclidean distance transformation, is tackled by three operations: restriction of both the number of object pixels and the number of background pixels taken in consideration and pre-computation of the euclidean distance. compared to the shih and liu 4-scan method the feed algorithm is often faster and is less memory consuming.
robust model driven matching method for face analysis with multi image photogrammetry. a vision based method to recover human faces from video sequences is presented. although video sequences acquired from multiple static synchronized ccd cameras have been used as a tool for 3-d reconstruction of the face before, the precision and reliability remain as concerning issues, which are addressed in this paper. moreover, the presented matching algorithm is invariant to scaling, rotation and insufficient calibration. a geometric primitive guides the matching approach through object space and therefore allows to compare gray values in an unique coordinate system.
recognizing human actions: a local svm approach. local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. in this paper we demonstrate how such features can be used for recognizing complex motion patterns. we construct video representations in terms of local space-time features and integrate such representations with svm classification schemes for recognition. for the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. the presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.
metric-based shape retrieval in large databases. this paper examines the problem of database organization and retrieval based on computing metric pairwise distances. a low-dimensional euclidean approximation of a high-dimensional metric space is not efficient, while search in a high-dimensional euclidean space suffers from the "curse of dimensionality". thus, techniques designed for searching metric spaces must be used. we evaluate several such existing exact metric-based indexing techniques, and show that they require extensive computational effort. this motivates the development of an approximate nearest neighbor search technique where the k nearest neighbors are used to approximate the local neighborhood of a point. the resulting k nn graph is searched in a best-first fashion producing excellent indexing efficiency.
emotion recognition based on joint visual and audio cues. recent technological advances have enabled human users to interact with computers in ways previously unimaginable. beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. however, one necessary ingredient for natural interaction is still missing - emotions. this paper describes the problem of bimodal emotion recognition and advocates the use of probabilistic graphical models when fusing the different modalities. we test our audio-visual emotion recognition approach on 38 subjects with 11 hci-related affect states. the experimental results show that the average person-dependent emotion recognition accuracy is greatly improved when both visual and audio information are used in classification.
skin detection: a bayesian network approach. the automated detection and tracking of humans in computer vision necessitates improved modeling of the human skin appearance. in this paper we propose a bayesian network approach for skin detection. we test several classifiers and propose a methodology for incorporating unlabeled data. we apply the semi-supervised approach to skin detection and we show that learning the structure of bayesian network classifiers enables learning good classifiers with a small labeled set and a large unlabeled set.
emotion recognition using a cauchy naive bayes classifier. recognizing human facial expression and emotion by computer is an interesting and challenging problem. in this paper we propose a method for recognizing emotions through facial expressions displayed in video sequences. we introduce the cauchy naive bayes classifier which uses the cauchy distribution as the model distribution and we provide a framework for choosing the best model distribution assumption. our person-dependent andperson-independent experiments show that the cauchy distribution assumption typically provides better results than the gaussian distribution assumption.