waashk/dblp huggingface dataset:
storage capacity of the exponential correlation associative memory. in this paper we analyze the pattern storage capacity of the exponential correlation associative memory (ecam). this architecture was first studied by chiueh and goodman &lsqb;3&rsqb; who concluded that, under certain conditions on the input patterns, the memory has a storage capacity that was exponential in the length of the bit-patterns. a recent analysis by pelillo and hancock &lsqb;9&rsqb;, using the kanerva picture of recall, concluded that the storage capacity was limited by 2n&minus;1/n2 patterns. both of these analyses can be criticised on the basis that they overlook the role of initial bit-errors in the recall process and deal only with the capacity for perfect pattern recall. in other words, they fail to model the effect of presenting corrupted patterns to the memory. this can be expected to lead to a more pessimistic limit. here we model the performance of the ecam when presented with corrupted input patterns. our model leads to an expression for the storage capacity of the ecam both in terms of the length of the bit-patterns and the probability of bit-corruption in the original input patterns. these storage capacities agree closely with simulation. in addition, our results show that slightly superior performance can be obtained by selecting an optimal value of the exponential constant.
levenshtein distance for graph spectral features. graph structures play a critical role in computer vision, but they are inconvenient to use in pattern recognition tasks because of their combinatorial nature and the consequent difficulty in constructing feature vectors. spectral representations have been used for this task which are based on the eigensystem of the graph laplacian matrix.however, graphs of different sizes produce eigensystems of different sizes where not all eigenmodes are present in both graphs. in this paper we use the levenshtein distance to compare spectral representations under graph edit operations which add or delete vertices.the spectral representations are therefore of different sizes.we use the concept of the string-edit distance to allow for the missing eigenmodes and compare the correct modes toeach other.we evaluate the method by first using generated graphs to compare the effect of vertex deletion operations.we then examine the performance of the method on graphs from a shape database.
a person and context specific approach for skin color classification. skin color is an important feature of faces. various applications benefit from robust skin color detection. depending on camera settings, illumination, shadows, people's tans, and ethnic groups skin color looks differently, which is a challenging aspect for detecting it automatically. in this paper, we present an approach that uses a high level vision module to detect an image specific skin color model. this model is then used to adapt parametric skin color classifiers to the processed image. this approach is capable to distinguish skin color from extremely similar colors, such as lip color or eyebrow color. its high speed and high accuracy make it appropriate for real time applications such as face tracking and recognition of facial expressions.
diversity/accuracy and ensemble classifier design. for an ensemble of multi-layer perceptrons (mlp), test error is shown in this paper to be well correlated with a diversity/accuracy measure computed between pairs of patterns. a weighted combination that uses the proposed measure to set the weights is shown to be less sensitive to the number of training epochs compared with majority vote.
probabilistic classification between foreground objects and background. tracking of deformable objects like humans is a basic operation in many surveillance applications. objects are detected as they enter the field of view of the camera and they are then tracked during the time they are visible. a problem with tracking deformable objects is that the shape of the object should be re-estimated for each frame. we propose a probabilistic framework combining object detection, tracking and shape deformation. we make use of the probabilities that a pixel belongs to the background, a new object or any of the known objects. instead of using arbitrary thresholds for deciding to which class the pixel should be assigned we assign the pixel based on the bayes criterion. preliminary experiments show the classification error drops to about half the error of traditional approaches.
the hidden birth dates of personalities of genesis. witztum, rips and rosenberg [4] have shown that when the book of genesis is written as two-dimensional arrays with the topology of a cylinder, equidistant letter sequences spelling words with related meaning appear non randomly in close proximity. here we adopt the quantitative tools developed in [4] to measure another type of pattern found in the same book. in the new patterns, equidistant letter sequences spelling out expressions appear in close proximity, on two-dimensional arrays, with conceptually related expressions appearing in the string of letters of the text. we measure the significance of this new type of pattern, using two samples of name-date word pairs, thus similar to the experiment done in [4]. the p-levels obtained were 0.00051 and 0.000046 respectively.
multi-camera real-time depth estimation with discontinuity handling on pc graphics hardware. this paper describes a system for dense depth estimation from multiple color images in real-time.our algorithm runs almost entirely on standard graphics hardware, leaving the main cpu free for other tasks such as i mage capture and higher level recognition. we follow a plane-sweep approach extended by truncated ssd scores, spatially shiftable windows and best camera selection to handle discontinuities.we do not need specialized hardware and exploit the computational power of freely programmable pc gpu hardware.dense depth maps are computed with up to 20 fps.
3d surface reconstruction by self-consistent fusion of shading and shadow features. in this paper a novel framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features is presented. based on the analysis of at least two pixel-synchronous images of the scene under different illumination conditions, this framework combines a shape from shading approach for estimating surface gradients and altitude variations with a shadow analysis that allows for an accurate determination of altitude differences on the surface. as a first step, the result of shadow analysis is used for selecting a consistent solution of the shape from shading reconstruction algorithm. as a second step, an additional error term derived from the fine-structure of the shadow is incorporated into the reconstruction algorithm. this framework is applied to three-dimensional reconstruction of regions on the lunar surface using ground-based ccd images. beyond the planetary science scenario, it is applicable to classical machine vision tasks such as surface inspection in the context of industrial quality control.
binarization of low quality text using a markov random field model. binarization techniques have been developed in the document analysis community for over 30 years and many algorithms have been used successfully. on the other hand, document analysis tasks are more and more frequently being applied to multimedia documents such as video sequences. due to low resolution and lossy compression, the binarization of text included in the frames is a non trivial task. existing techniques work without a model of the spatial relationships in the image, which makes them less powerful. we introduce a new technique based on a markov random field (mrf) model of the document. the model parameters (clique potentials) are learned from training data and the binary image is estimated in a bayesian framework. the performance is evaluated using commercial ocr software.
segmentation of vector fields by critical point analysis: application to brain deformation. mri examinations may be used to monitor the progress of neurological disease. arising structural changes can then be quantified using non-rigid registration procedures. however, the interpretation of the resulting large scale vector fields is difficult without further processing. we propose using contraction mapping to detect critical points such as attractors and repellors in order to characterize deforming areas. with the application to time series images we show that critical points help to get a better perception of the brain deformation and the underlying pathological process.
a software algorithm prototype for optical recognition of embossed braille. braille is a tactile format of written communication for sight-impaired people worldwide. this paper proposes a software solution prototype to optically recognise single sided embossed braille documents using a simple image processing algorithm and probabilistic neural network. the output is a braille text file formatted to preserve the layout of the original document which can be sent to an electronic embosser for reproduction. preliminary experiments have been performed with an excellent recognition rate, where the transcription accuracy is at 99%.
continuous gesture recognition using a sparse bayesian classifier. an approach to recognise and segment 9 elementary gestures from a video input is proposed and it can be applied to continuous sign recognition. an isolated gesture is recognised by first converting a portion of video into a motion gradient orientation image and then classifying it into one of the 9 gestures by a sparse bayesian classifier. the portion of video used is decided by using a sampling technique based on condensation framework. by doing so, gestures can be segmented from the video in a probabilistic manner. experiments show that the proposed method can achieve accuracy around 90% in both isolated and continuous gesture recognition without using special equipment such as glove devices and the system can run in real-time.
adaptive processing of face emotion tree structures. this paper describes a novel recursive neural network for adaptive processing of face emotion tree structures (feets). we proposed to use tree structures to represent gabor face emotion features. we demonstrated the robustness of our proposed system by testing against other well-known classifiers using the cohn-kanade au-coded facial expression database [1]. the system yields an accuracy of about 93% and 57% for known and unknown subjects respectively.
indexing and retrieval of 3d models by unsupervised clustering with hierarchical som. a hierarchical indexing structure for 3d model retrieval based on the hierarchical self organizing map (hsom) is proposed. the proposed approach organizes the database into a hierarchy so that head models are partitioned by coarse features initially and finer scale features are used in lower levels. the aim is to traverse a small subset of the database during retrieval. this is made possible by exploiting the multi-resolution capability of spherical wavelet features to successively approximate the salient characteristics of the head models, which are encoded in the form of weight vectors associated with the nodes at different levels (from coarse to fine) of the hsom. to avoid premature commitment to a possibly erroneous model class, search is propagated from a subset of nodes at each level, which is selected based on a fuzzy membership measure between the query feature vector and weight vector, instead of taking the winner-take-all approach. experiments show that, in addition to efficiency improvement, model retrieval based on the hsom approach is able to achieve a much higher accuracy compared with the case where no indexing is performed.
handwritten digit recognition using multi-layer feedforward neural networks with periodic and monotonic activation functions. the problem of handwritten digit recognition is tackled by multi-layer feedforward neural networks with different types of neuronal activation functions. three types of activation functions are adopted in the network, namely, the traditional sigmoid function, the sinusoidal function and a periodic function that can be considered as a combination of the first two functions. to speed up the learning, as well as to reduce the network size, the extended kalman filter (ekf) algorithm conjunct with a pruning method is used to train the network. simulation results show that periodic activation functions perform better than monotonic ones in solving multi-cluster classification problems such as handwritten digit recognition.
brush writing style classification from individual chinese characters. chinese calligraphic artwork usually contains a few or even a single character. in order to perform automatic brush writing style classification on these kinds of chinese calligraphic images, existing approaches need to be modified because they assume that many characters of the same style exist in the input image. a novel approach is proposed to address the brush writing style classification problem for single-character chinese calligraphic images by combining the texture analysis and the structural information through a set of parameterised ellipses.
improving text classifier performance based on auc. to evaluate the performance of text classifiers, we usually look at measures related to precision and recall, and most machine learning methods are optimized for these measures. in recent year, the use of receiver operating characteristics (roc) graph and its extension area under the roc curve (auc) in gauging classifier performance has attracted much attention from the machine learning community. this measure is especially useful when a data set is imbalanced or when operating characteristics are unknown. some researchers have started investigating the optimization of existing learning model for this new performance criterion. in this paper, we proposed modifications to the well-known weight updating text classifier sleeping-experts (se) for auc optimization. our experiments show that through our new sampling and updating strategy we can improve the classifier both in terms of auc and the traditional performance measures.
blind phase-amplitude modulation classification with unknown phase offset. this paper first discusses the maximum likelihood (ml) classifier for automatic classification of digital modulations. the classifier is optimum for classification of phase-amplitude modulated signals under ideal environment. however, this is not the case in the presence of phase offset owing to inaccurate estimation. in this paper, we propose a novel non-coherent ml classifier to mitigate the effect phase offset. the non-coherent ml classifier adopts a pre-classification phase correction stage through a closed form estimator based on higher order statistics. experimental results show improvement of classification accuracy at reasonable signal to noise ratio.
robust appearance-based tracking using a sparse bayesian classifier. an appearance-based approach to track an object that may undergo appearance change is proposed. unlike recent methods that store a detailed representation of object's appearance, this method allows an appearance feature with a reduced dimension to be used. through the use of a sparse bayesian classifier, high classification and detection accuracy can be maintained even if a reduced feature vector is used. in addition, the classifier allows online-training which enables online-updating of the original classification model and provides better adaptability. experiments show that the method can be used to track targets undergo appearance change due to the change in view-point, facial expression and lighting direction.
lexicon-based browsers for searching in news video archives. in this paper we present the methods and visualizations used in the mediamill video search engine. the basis for the engine is a semantic indexing process which derives a lexicon of 101 concepts. to support the user in navigating the collection, the system defines a visual similarity space, a semantic similarity space, a semantic thread space, and browsers to explore them. the search system is evaluated within the trecvid benchmark. we obtain a top-3 result for 19 out of 24 search topics. in addition, we obtain the highest mean average precision of all search participants.
enhanced canny edge detection using curvature consistency. edges are often considered as primary image artifacts for extraction by low-level processing techniques, and the starting point for many computer vision techniques. as a result, reliable edge detection has long been a research goal. this paper describes initial nvestigations into recovering reliable edges using curvature models. essentially, we modify canny's edge detector, using a curvature consistency process to adjust the gradient direct on estimates prior to finding the zero crossings in those directions.
integration frameworks for large scale cognitive vision systems - an evaluative study. owing to the ever growing complexity of present day computer vision systems, system architecture has become an emerging topic in vision research. systems that integrate numerous modules and algorithms of different i/o and time scale behavior require sound and reliable concepts for interprocess communication. consequently, topics and methods known from software and systems engineering are becoming increasingly important. especially framework technologies for system integration are required. this contribution results from a cooperation between two multinational projects on cognitive vision. it discusses functional and non-functional requirements in cognitive vision and compares and assesses existing solutions.
an xml based framework for cognitive vision architectures. distributed processing and memory structures are very important aspects of cognitive vision systems. both issues not only require sophisticated conceptual designs but also pose problems of software and systems engineering. in this paper, we describe a general xml based solution to these problems. practical experiences are reported to underline its suitability.
glasses detection by boosting simple wavelet features. in this paper we propose a novel method for glasses detection. the glasses detectors are learned by using a variation of boosting algorithm, called real adaboost [improved boosting algorithms using confidence-rated predictions], to boost simple wavelet feature based look-up-table type weak classifiers. two types of wavelet features, haar and gabor, have been investigated. experiments results are reported to show that our method has very high correctness and extremely fast running speed. based on this method we have developed a glasses detection system which can detect the glasses in facial images automatically.
visual line estimation from a single image of two eyes. this paper describes a conic-based algorithm for estimating visual line from a single monocular image. by assuming that the visual lines of the both eyes are parallel and the iris boundaries are circles, we propose a "two-circle" algorithm that can estimate the normal vector of the supporting plane of the iris boundaries, from which the visual line is calculated. our new method does not use either the eye corners, or some heuristic knowledge about the structure of the eye. another advantage of our algorithm is that a camera with an unknown focal length can be used without assuming the orthographical projection. this is a very useful feature because it allows one to use a zoom lens and to change the zooming factor whenever he or she likes. it also gives one more freedom of the camera setting because keeping the camera far from the eyes is not necessary in our method. the extensive experiments over simulated images and real images demonstrate the robustness and the effectiveness of our method.
face recognition based on discriminative manifold learning. in this paper, a discriminative manifold learning method for face recognition is proposed which achieved the discriminative embedding the high dimensional face data into a low dimensional hidden manifold. unlike the recently proposed lle, isomap and eigenmap algorithms, which are based on reconstruction purpose, our method use the rca algorithm to achieve nonlinear embedding and data discrimination at the same time. also, the lle and isomap algorithms are crucially depends on the appropriateness of the neighborhood construction rule, in this paper, a ck-nearest neighborhood rule is proposed to achieve better neighborhood construction. experimental results indicate the promising performance of the proposed method.
license plate extraction in low resolution video. extensive study has been conducted in the detection of license plate for the applications in intelligent transportation system (its). however, these results are all based on images acquired at a resolution of 680x480. in this paper, a new method is proposed to extract license plate from the surveillance video which is shot at lower resolution ( 320x240) as well as degraded by video compression. morphological operations of bottom-hat and morphology gradient are utilized to detect the lp candidates, and effective schemes are applied to select the correct one. the average rates of correct extraction and false alarms are 96.22% and 1.77%, respectively, based on the experiments using more than four hours of video. the experimental results demonstrate the effectiveness and robustness of the proposed method.
willhunter: interactive image retrieval with multilevel relevance measurement. relevance feedback has become a key component in cbir system. although most current relevance feedback approaches are based on dichotomous relevance measurement, this coarse measurement is a distortion of the reality. we study relevance feedback with multi-level relevance measurement to better identify the user needs and preferences. to validate the use of multi-level relevance measurement and our relevance feedback algorithm, we developed a cbir prototype system - willhunter.there are two novelties in our system, one is our svm-based fast learning algorithm; another is the easy-to-use graphical user interface, especially the relevance-measuring instrument. not only experiments are conducted to assess the algorithm, but also usability study is carried out to evaluate the user interface.
a semi-supervised svm for manifold learning. many classification tasks benefit from integrating manifold learning with semi-supervised learning. by formulating the learning task in a semi-supervised manner, we propose a novel objective function that combines the manifold consistency of whole dataset with the hinge loss of class label prediction. this formulation results in a svm-alike task operating on the kernel derived from the graph laplacian, and is capable of capturing the intrinsic manifold structure of the whole dataset and maximizing the margin separating labelled examples. results on face and handwritten digit recognition tasks show significant performance gain. the performance gain is particularly impressive when only a small training set is available, which is often the true scenario of many real-world problems.
a new method of object segmentation in the basketball videos. object segmentation is one of key issues in object based video analysis and retrieval. in this paper, a basketball segmentation algorithm is proposed. the algorithms involve a hierarchical basketball detection algorithm and a motion tracking algorithm based on adaptive object model. basketball detection algorithm is started firstly, only when a basketball is detected, the tracking algorithm is started in the next frame. moreover, there is a tracking result evaluation module in motion tracking algorithm, if it is true, the tracking algorithm is used in the next frame, otherwise, the detection module is started again. the experimental results from real basketball video confirm the efficiency of the proposed algorithms.