waashk/dblp huggingface dataset:
integrating faces and fingerprints for personal identification. an automatic personal identification system based solely on fingerprints or faces is often not able to meet the system performance requirements. face recognition is fast but not extremely reliable, while fingerprint verification is reliable but inefficient in database retrieval. we have developed a prototype biometric system which integrates faces and fingerprints. the system overcomes the limitations of face recognition systems as well as fingerprint verification systems. the integrated prototype system operates in the identification mode with an admissible response time. the identity established by the system is more reliable than the identity established by a face recognition system. in addition, the proposed decision fusion scheme enables performance improvement by integrating multiple cues with different confidence measures. experimental results demonstrate that our system performs very well. it meets the response time as well as the accuracy requirements.
optimising the complete image feature extraction chain. the hypothesis verification stage of the traditional image processing approach, consisting of low, medium, and high level processing, will suffer if the set of low level features extracted are of poor quality. we investigate the optimisation of the feature extraction chain by using genetic algorithms. the fitness function is a performance measure which reflects the quality of an extracted set of features. we will present some results and compare them with a hill-climbing approach.
3d shape recovery of smooth surfaces: dropping the fixed viewpoint assumption. we present a new method for recovering the 3d shape of a featureless smooth surface from three or more calibrated images illuminated by different light sources (three of them are independent). this method is unique in its ability to handle images taken from unconstrained perspective viewpoints and unconstrained illumination directions. the correspondence between such images is hard to compute and no other known method can handle this problem locally from a small number of images. our method combines geometric and photometric information in order to recover dense correspondence between the images and accurately computes the 3d shape. only a single pass starting at one point and local computation are used. this is in contrast to methods that use the occluding contours recovered from many images to initialize and constrain an optimization process. the output of our method can be used to initialize such processes. in the special case of fixed viewpoint, the proposed method becomes a new perspective photometric stereo algorithm. nevertheless, the introduction of the multiview setup, self-occlusions, and regions close to the occluding boundaries are better handled, and the method is more robust to noise than photometric stereo. experimental results are presented for simulated and real images.
a real-time large disparity range stereo-system using fpgas. in this paper, we discuss the design and implementation of a field-programmable gate array (fpga) based stereo depth measurement system that is capable of handling a very large disparity range. the system performs rectification of the input video stream and a left-right consistency check to improve the accuracy of the results and generates subpixel disparities at 30 frames/second on 480 × 640 images. the system is based on the local weighted phase- correlation algorithm [9] which estimates disparity using a multi-scale and multi-orientation approach. though fpgas are ideal devices to exploit the inherent parallelism in many computer vision algorithms, they have a finite resource capacity which poses a challenge when adapting a system to deal with large image sizes or disparity ranges. in this work, we take advantage of the temporal information available in a video sequence to design a novel architecture for the correlation unit to achieve correlation over a large range while keeping the resource utilisation very low as compared to a naive approach of designing a correlation unit in hardware.
a linear algorithm for motion from three weak perspective images using euler angles. in this paper, we describe a new simple linear algorithm for motion and structure from three weak perspective projections using euler angles. we first determine the epipolar equation between each pair of images, which determines the first and third euler angles for the rotation between that pair of images, leaving only the second euler angle undetermined. in the next step, combining the three rotations results in a very simple linear algorithm to determine the second euler angles, up to a necker reversal. experimental results on synthetic and real images are presented. the degenerate cases are discussed. the program can be ftped from http://www.cv.cs.ritsumei.ac.jp/noriko/motion.html.
viewpoint determination of image by interpolation over sparse samples. we address the problem of determining the viewpoint of an image without referencing to or estimating explicitly the 3-d structure pictured in the image. used for reference are instead a number of sample snapshots of the scene, each supplied with the associated viewpoint. by viewing image and its associated viewpoint as the input and output of a function, and the reference snapshot-viewpoint pairs as input-output samples of that function, we have a natural formulation of the problem as an interpolation one. the interpolation formulation allows imaging details like camera intrinsic parameters to be unknown, and the specification of the desired viewpoint to be not necessarily in metric terms. we describe an interpolation-based mechanism that determines the viewpoint of any given input image, which has the property that it fits all the given input-output reference samples exactly. experimental results on benchmarking image datasets show that the mechanism is effective in reaching quality viewpoint solution even with only a few reference snapshots.
an integrated model for evaluating the amount of data required for reliable recognition. many recognition procedures rely on the consistency of a subset of data features with a hypothesis as the sufficient evidence to the presence of the corresponding object. we analyze here the performance of such procedures, using a probabilistic model, and provide expressions for the sufficient size of such data subsets, that, if consistent, guarantee the validity of the hypotheses with arbitrary confidence. we focus on 2d objects and the affine transformation class, and provide, for the first time, an integrated model which takes into account the shape of the objects involved, the accuracy of the data collected, the clutter present in the scene, the class of the transformations involved, the accuracy of the localization, and the confidence we would like to have in our hypotheses. interestingly, it turns out that most of these factors can be quantified cumulatively by one parameter, denoted "effective similarity," which largely determines the sufficient subset size. the analysis is based on representing the class of instances corresponding to a model object and a group of transformations, as members of a metric space, and quantifying the variation of the instances by a metric cover.
script and language identification from document images. in this paper we present a detailed review of current script and language identification techniques. the main criticism of the existing techniques is that most of them rely on character segmentation. we go on to present a new method based on texture analysis for script identification which does not require character segmentation. a uniform text block on which texture analysis can be performed is produced from a document image via simple processing. multiple channel (gabor) filters and grey level co-occurrence matrices are used in independent experiments in order to extract texture features. classification of test documents is made based on the features of training documents using the k-nn classifier. initial results of over 95% accuracy on the classification of 105 test documents from 7 languages are very promising. the method shows robustness with respect to noise, the presence of foreign characters or numerals, and can be applied to very small amounts of text.
similarity matching. with complex multimedia data, wesee the emergence of database systems in which the fundamental operation is similarity assessment. before database issues can be addressed, it is necessary to give a definition of similarity as an operation. in this paper, we develop a similarity measure, based on fuzzy logic, that exhibits several features that match experimental findings in humans. the model is dubbed fuzzy feature contrast (ffc) and is an extension to a more general domain of the feature contrast model due to tversky. we show how the ffc model can be used to model similarity assessment from fuzzy judgment of properties, and we address the use of fuzzy measures to deal with dependencies among the properties.
online updating appearance generative mixture model for meanshift tracking. this paper proposes an appearance generative mixture model based on key frames for meanshift tracking. meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. the tracking therefore could fail if the appearance of the object varies substantially. in this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. the generative process is described by a bayesian graphical model. an online em algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. we applied this approach to track human head motion and to infer the head pose simultaneously in videos. experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses.
3d shape and motion analysis from image blur and smear: a unified approach. this paper addresses 3d shape recovery and motion estimation using a realistic camera model with an aperture and a shutter. the spatial blur and temporal smear effects induced by the camera's finite aperture and shutter speed are used for inferring both the shape and motion of the imaged objects.
histogram features-based fisher linear discriminant for face detection. the face pattern is described by pairs of template-based histogram and fisher projection orientation under the framework of adaboost learning in this paper. we assume that a set of templates are available first. to avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize the response of each template. by introducing a novel &#x201c;integral histogram image&#x201d;, we can compute histogram rapidly. then, we turn to fisher linear discriminant for each template to project histogram from d-dimensional subspace to one-dimensional subspace. best features, used to describe face pattern, are selected by adaboost learning. the results of experiments demonstrate that the selected features are much more powerful to represent the face pattern than the simple rectangle features used by viola and jones and some variants.
eye correction using correlation information. this paper proposes a novel eye detection method using the mct-based pattern correlation. the proposed method detects the face by the mct-based adaboost face detector over the input image and then detects two eyes by the mct-based adaboost eye detector over the eye regions. sometimes, we have some incorrectly detected eyes due to the limited detection capability of the eye detector. to reduce the falsely detected eyes, we propose a novel eye verification method that employs the mct-based pattern correlation map. we verify whether the detected eye patch is eye or non-eye depending on the existence of a noticeable peak. when one eye is correctly detected and the other eye is falsely detected, we can correct the falsely detected eye using the peak position of the correlation map of the correctly detected eye. experimental results show that the eye detection rate of the proposed method is 98.7% and 98.8% on the bern images and ar-564 images.
probability hypothesis density approach for multi-camera multi-object tracking. object tracking with multiple cameras is more efficient than tracking with one camera. in this paper, we propose a multiple-camera multiple-object tracking system that can track 3d object locations even when objects are occluded at cameras. our system tracks objects and fuses data from multiple cameras by using the probability hypothesis density filter. this method avoids data association between observations and states of objects, and tracks multiple objects in single-object state space. hence, it has lower computation than methods using joint state space. moreover, our system can track varying number of objects. the results demonstrate that our method has a high reliability when tracking 3d locations of objects.
adaptive multiple object tracking using colour and segmentation cues. we consider the problem of reliably tracking multiple objects in video, such as people moving through a shopping mall or airport. in order to mitigate difficulties arising as a result of object occlusions, mergers and changes in appearance, we adopt an integrative approach in which multiple cues are exploited. object tracking is formulated as a bayesian parameter estimation problem. the object model used in computing the likelihood function is incrementally updated. key to the approach is the use of a background subtraction process to deliver foreground segmentations. this enables the object colour model to be constructed using weights derived from a distance transform operating over foreground regions. results from foreground segmentation are also used to gain improved localisation of the object within a particle filter framework. we demonstrate the effectiveness of the approach by tracking multiple objects through videos obtained from the caviar dataset.
gender classification based on fusion of multi-view gait sequences. in this paper, we present a new method for gender classification based on fusion of multi-view gait sequences. for each silhouette of gait sequences, we first use a simple method to divide the silhouette into 7 (for 90 degree, i.e. fronto-parallel view) or 5 (for 0 and 180 degree, i.e. front view and back view) parts, and then fit ellipses to each of the regions. next, the features are extracted from each sequence by computing the ellipse parameters. for each view angle, every subject's features are normalized and combined as a feature vector. the combination of feature vector contains enough information to perform well on gender recognition. sum rule and svm are applied to fuse the similarity measures from 0°, 90°, and 180°. we carried our experiments on casia gait database, one of the largest gait databases as we know, and achieved the classification accuracy of 89.5%.
synthesis of exaggerative caricature with inter and intra correlations. we developed a novel system consisting of two modules, statistics-based synthesis and non-photorealistic rendering (npr), to synthesize caricatures of exaggerated facial features and other particular characteristics, such as beards or nevus. the statistics-based synthesis module can exaggerate shapes and positions of facial features based on non-linear exaggerative rates determined automatically. instead of comparing only the inter relationship between features of different subjects at the existing methods, our synthesis module applies both inter and intra (i.e. comparisons between facial features of the same subject) relationships to make the synthesized exaggerative shape more contrastive. subsequently, the npr module generates a line-drawing sketch of original face, and then the sketch is warped to an exaggerative style with synthesized shape points. the experimental results demonstrate that this system can automatically, and effectively, exaggerate facial features, thereby generating corresponding facial caricatures.
combined object detection and segmentation by using space-time patches. this paper presents a method for classifying the direction of movement and for segmenting objects simultaneously using features of space-time patches. our approach uses vector quantization to classify the direction of movement of an object and to estimate its centroid by referring to a codebook of the space-time patch feature, which is generated from multiple learning samples. we segmented the objects' regions based on the probability calculated from the mask images of the learning samples by using the estimated centroid of the object. even though occlusions occur when multiple objects overlap in different directions of movement, our method detects objects individually because their direction of movement is classified. experimental results show that object detection is more accurate with our method than with the conventional method, which is only based on appearance features.
image assimilation for motion estimation of atmospheric layers with shallow-water model. the complexity of dynamical laws governing 3d atmospheric flows associated to incomplete and noisy observations makes very difficult the recovery of atmospheric dynamics from satellite images sequences. in this paper, we face the challenging problem of joint estimation of time-consistent horizontal motion fields and pressure maps at various atmospheric depths. based on a vertical decomposition of the atmosphere, we propose a dense motion estimator relying on a multi-layer dynamical model. noisy and incomplete pressure maps obtained from satellite images are reconstructed according to shallow-water model on each cloud layer using a framework derived from data assimilation. while reconstructing dense pressure maps, this variational process estimates time-consistent horizontal motion fields related to the multi-layer model. the proposed approach is validated on a synthetic example and applied to a real world meteorological satellite image sequence.
an fpga-based smart camera for gesture recognition in hci applications. smart camera is a camera that can not only see but also think and act. a smart camera is an embedded vision system which captures and processes image to extract application-specific information in real time. the brain of a smart camera is a special processing module that performs application specific information processing. the design of a smart camera as an embedded system is challenging because video processing has insatiable demand for performance and power, but at the same time embedded systems place considerable constraints on the design. we present our work to develop gesturecam, an fpga-based smart camera built from scratch that can recognize simple hand gestures. the first completed version of gesturecam has shown promising real-time performance and is being tested in several desktop hci (human computer interface) applications.
real-time and marker-free 3d motion capture for home entertainment oriented applications. we present an automated system for real-time marker-free motion capture from two calibrated webcams. for fast 3d shape and skin reconstructions, we extend shape-from-silhouette algorithms. the motion capture system is based on simple and fast heuristics to increase the efficiency. multi-modal scheme using both shape and skin-parts analysis, temporal coherence, and human anthropometric constraints are adopted to increase the robustness. thanks to fast algorithms, low-cost cameras and the fact that the system runs on a single computer, our system is perfectly suitable for home entertainment device. results on real video sequences demonstrate our approach efficiency.
an efficient method for text detection in video based on stroke width similarity. text appearing in video provides semantic knowledge and significant information for video indexing and retrieval system. this paper proposes an effective method for text detection in video based on the similarity in stroke width of text (which is defined as the distance between two edges of a stroke). from the observation that text regions can be characterized by a dominant fixed stroke width, edge detection with local adaptive thresholds is first devised to keep text- while reducing background-regions. second, morphological dilation operator with adaptive structuring element size determined by stroke width value is exploited to roughly localize text regions. finally, to reduce false alarm and refine text location, a new multi-frame refinement method is applied. experimental results show that the proposed method is not only robust to different levels of background complexity, but also effective to different fonts (size, color) and languages of text.
user-guided shape from shading to reconstruct fine details from a single photograph. many real objects, such as faces, sculptures, or low-reliefs are composed of many detailed parts that can not be easily modeled by an artist nor by 3d scanning. in this paper, we propose a new shape from shading (sfs) approach to rapidly model details of these objects such as wrinkles and reliefs of surfaces from one photograph. the method first determines the surface's flat areas in the photograph. then, it constructs a graph of relative altitudes between each of these flat areas. we circumvent the ill-posed problem of shape from shading by having the user set if some of these flat areas are a local maximum or a local minimum; additional points can be added by the user (e.g. at discontinuous creases) - this is the only user input. we use an intuitive mass-spring based minimization to determine the final position of these flat areas and a fast-marching method to generate the surface. this process can be iterated until the user is satisfied with the resulting surface. we illustrate our approach on real faces and low-relief photographs.
unsupervised identification of multiple objects of interest from multiple images: discover. given a collection of images of offices, what would we say we see in the images? the objects of interest are likely to be monitors, keyboards, phones, etc. such identification of the foreground in a scene is important to avoid distractions caused by background clutter and facilitates better understanding of the scene. it is crucial for such an identification to be unsupervised to avoid extensive human labeling as well as biases induced by human intervention.most interesting scenes contain multiple objects of interest. hence, it would be useful to separate the foreground into the multiple objects it contains. we propose discover, an unsupervised approach to identifying the multiple objects of interest in a scene from a collection of images. in order to achieve this, it exploits the consistency in foreground objects - in terms of occurrence and geometry - across the multiple images of the scene.