waashk/dblp huggingface dataset:
kernel autoassociator with applications to visual classification. autoassociator is an important issue in concept learning, and the learned concept of a particular class can be used to distinguish the class from the others. for nonlinear autoassociation, this paper presents a new model referred to as kernel autoassociator. using kernel feature space as a potential nonlinear manifold, the model formulates the autoassociation as a special reconstruction problem from kernel feature space to input space. two methods are developed to solve the problem. we evaluate the autoassociator with artificial data, and apply it to handwritten digit recognition and multiview face recognition, yielding positive experimental results.
reducing artifacts in bdct-coded images by adaptive pixel-adjustment. generating artifacts is a major problem with compressed images coded by the block discrete cosine transform (bdct). an iterative deblocking method based on projection onto convex sets (pocs) is presented in this paper. the method considers pixel-wise local adaptability. the adjustment of a pixel is determined by local properties of the pixel. a coded image is iteratively projected onto two locally adaptive smoothness constraint sets, a locally adaptive quantization constraint set and an intensity constraint set to obtain an improved image where artifacts are reduced. the proposed method is tested on jpeg encoded images with excellent results.
automatic surveying of cutaneous hemangiomas. this paper presents a method for the fully automatic surveying of cutaneous hemangiomas by means of a hemangioma segmentation and a ruler visible in the images. the algorithm computes the spatial resolution of an image. hemangioma segmentation is accomplished by a single-layer perceptron classification by means of pixel color features. the algorithm was evaluated on a set of 120 images. it achieves satisfactory results on images with clearly visible, saturated hemangiomas.
core-based structure matching algorithm of fingerprint verification. fingerprint matching algorithm is a key issue of the fingerprint recognition, and there already exist many fingerprint matching algorithms, according to the dependence of the core point, fingerprint matching algorithms are divided into two groups, core-based match algorithms and noncore-based match algorithms. most of the noncore-based matching algorithm is time consuming, therefore, they are not suitable for online application; meanwhile, the core-based matching algorithm is efficient than the noncore-based matching algorithm, but it highly depends on the core detection precision. in this paper, we present a new core-based structure matching algorithm which considers both efficient and precision. firstly we used core detection algorithm to get the core position, then we define some local structure of the core area. used these local structure, we can find some correspondent points of the two fingerprint image. secondly, we use the correspondent points in the first stage to match the global feature of the fingerprint. experimental results show that the performance of the proposed algorithm is good.
robust background subtraction and maintenance. background subtraction is one of the main techniques to extract moving objects from background scenes. a mixture of gaussians is a common model for background subtraction that has been used in many applications. however modelling background pixels using this model results into a low-level process at pixel level. some of its main drawbacks are: a subtracted (moving object) region may contain holes; it can not solve partial occlusion problems, and it requires updates in cases of shadows or sudden changes in the scene. we present a multi-layered mixture of gaussians model named pixelmap. we combine the mixture of gaussians model with concepts defined by region level and frame level considerations. our experimental results show that our method improved the accuracy of extracting moving objects from background. a single stationary camera has been used.
an image segmentation framework based on patch segmentation fusion. in this paper we present an image segmentation framework based on patch segmentation fusion. an image is first split into small patches. segmentation is then performed on each patch using the algorithms of standard normalized cut [9], mean shift clustering [3], or k-means clustering. each region in a patch segmentation is assigned a label so as to represent different parts. after that, a connectedness value is calculated between any two overlapping patch segmentations with certain kinds of labeling. a weight called border strength is calculated for a segmentation with a certain labeling. we optimize a global criterion function that quantifies the consistency and quality of patch segmentations by a simulated annealing algorithm [5] in order to find the optimal patch segmentations and labeling. finally, global segmentation is reconstructed by fusing patch segmentations by multiple techniques. experimental results on natural images are reported. precision and recall rates are also calculated to evaluate the performance quantitively.
tracking of point targets in ir image sequence using multiple model based particle filtering and mrf based data association. particle filtering is being investigated extensively due to its important feature of target tracking based on nonlinear and non-gaussian model. it tracks a trajectory with a known model at a given time. it means that particle filter tracks an arbitrary trajectory only if the time instant when trajectory switches from one model to another model is known apriori. because of this reason particle filter is not able to track any arbitrary trajectory where transition from one model to another model is not known. for real world application, trajectory is always random in nature and may follow more than one model. in this paper we propose a novel method, which overcomes the above problem. in the proposed method a multiple model based approach is used along with particle filtering, which automates the model selection process for tracking an arbitrary trajectory. in the proposed approach, there is no need to have apriori information about the exact model that a target may follow. for data association, markov random field (mrf) based method has been utilized. it allows us to exploit the neighborhood concept for data association, i.e. the association of a measurement influences an association of its neighbor measurement.
efficient, simultaneous detection of multiple object classes. at present, the object categorisation literature is still dominated by the use of individual class detectors. detecting multiple classes then implies the subsequent application of multiple such detectors, but such an approach is not scalable towards high numbers of classes. this paper presents an alternative strategy, where multiple classes are detected in a combined way. this includes a decision tree approach, where ternary rather than binary nodes are used, and where nodes share features. this yields an efficient scheme, which scales much better. the paper proposes a strategy where the object samples are first distinguished from the background. then, in a second stage, the actual object class membership of each sample is determined. the focus of the paper lies entirely on the first stage, i.e. the distinction from background. the tree approach for this step is compared against two alternative strategies, one of them being the popular cascade approach. while classification accuracy tends to be better or comparable, the speed of the proposed method is systematically better. this advantage gets more outspoken as the number of object classes increases.
multiple regions of interest image coding using compensation scheme and alternating shift. maxshift method is the efficient region of interest (roi) coding algorithm recommended by jpeg2000. however, it cannot support the multiple roi coding based on different degrees of interest. in the paper, a new multiple roi coding method called bcashift (bitplanes compensation alternating shift) is presented. for multiple rois, the new method can encode the significant bitplanes of rois and background (bg) by alternating shift model, which ensures these rois to be encoded according to different degrees of interest. the least significant bitplanes of bg can be last encoded based on compensation scheme. experimental result shows that the presented method cannot only efficiently encode multiple rois with different degrees of interest in an image, but also flexibly adjust the compression quality between roi and bg by selecting an arbitrary scaling value without any roi shape information.
insulators recognition for 220kv/330kv high-voltage live-line cleaning robot. this paper approaches the problem of recognizing and localizing insulators with curved smooth and weak textured surfaces in a cluttered scene for the 220kv/330kv high-voltage live-line cleaning robot (hlcr). in this work we introduce susan edge-based scale invariant feature (sesif) to describe insulators, which is robust to noise, cluttered scene, changes on viewpoint and illumination. a coarse-to-fine method is applied to recognize and localize insulators using sesif. firstly, a coarse location is estimated with general hough transform and ransac method. secondly, an accurate location of insulator is found with an edge-based tracker. results are demonstrated for a 220kv insulator in a cluttered scene.
preprocessing of handwritten date images on chinese cheque. the preprocess procedure of handwritten date images in our automatic chinese cheque processing system is introduced. it consists of three major components: preprinted characters removal, binarization and extra strokes removal. a pixel-contour distance based method is proposed to remove preprinted characters while preserving the connection of object strokes. in binary, a local feature called morphological double edge is used to enhance the character strokes, and then a global threshold is selected by a new recursive method. extra strokes locate below date characters. we remove them by contour analysis. experiment results on real-life cheque images demonstrate the efficiency of our proposed methods.
semantic object segmentation by a spatio-temporal mrf model. in this paper, a region-based spatio-temporal markov random field (stmrf) model is proposed to segment moving objects semantically. the stmrf model combines segmentation results of four successive frames and integrates the temporal continuity in the uniform energy function. the segmentation procedure is composed of two stages: one is the short-term's classification and the other is temporal integration. at the first stage, moving objects are extracted by a region-based mrf model between two frames in a frame group of four successive frames. at the second stage, the ultimate semantic object is labeled by minimization the energy function of the stmrf model. such phased segmentation process is corresponding to a multi-level simulated anneal strategy. experimental results show that the proposed algorithm can efficiently capture the motion semantic meaning of objects and accurately extract moving objects.
a bayesian framework for automatic concept discovery in image collections. this paper develops a bayesian framework for automatic hidden semantic concept discovery to address effective semantics-intensive content based image retrieval. each image in the database is segmented to regions associated with homogenous color, texture, and shape features. by employing self-organization map learning, a uniform and sparse region-based representation is obtained. with this representation a probabilistic model based on the statistical-hidden-class assumptions of the image database is developed, to which expectation-maximization technique is applied to analyze semantic concepts hidden in the database. an elaborated retrieval algorithm is designed to support the probabilistic model. the semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative vector, to the discovered semantic concepts. the proposed approach has a solid statistical foundation and the experimental evaluations on a database of 10,000 general-purposed images demonstrate its promise of the retrieval effectiveness.
type-2 fuzzy hidden markov models to phoneme recognition. this paper presents a novel extension of hidden markov models (hmms): type-2 fuzzy hmms (type-2 fhmms). the advantage of this extension is that it can handle both randomness and fuzziness within the framework of type-2 fuzzy sets (fss) and fuzzy logic systems (flss). membership functions (mfs) of type-2 fuzzy sets are three-dimensional. it is the third dimension that provides the additional degrees of freedom that make it possible to handle both uncertainties. we apply the type-2 fhmm as acoustic models for phoneme recognition on timit speech database. experimental results show that the type-2 fhmm has a comparable performance as that of the hmm but is more robust to noise, while it retains almost the same computational complexity as that of the hmm.
driver fatigue detection based intelligent vehicle control. driver fatigue problem is one of the important factors that cause traffic accidents. therefore the vision-based driver fatigue detection is the most prospective commercial applications of hci. however, it is a challenging issue due to a variety of factors such as head and eyes moving fast, external illuminations interference and realistic lighting conditions, etc. this tends to significantly limit its scope of application. in this paper, we present an intelligent vehicle control based on driver fatigue detection. firstly, the face is located using haar algorithm and eye location is found with projection technique. after finding eye templates, we propose a new real time eye tracking method based on unscented kalman filter. thirdly, driver fatigue can be detected whether the eyes are closed over 5 consecutive frames using vertical projection matching. finally, if driver fatigue is confirmed, the vehicle cruise control is start-up with slow speed, and maintains set slow speed such as 5 km/h. the experimental results show that intelligent vehicle control based on driver fatigue detection will be availability in traffic.
stroke segmentation of chinese characters using markov random fields. this paper presents markov random fields (mrfs) to segment strokes of chinese characters. the distortions caused by the thinning process make the thinning-based stroke segmentation difficult to extract continuous strokes and handle the ambiguous intersection regions. the mrfs reflect the local statistical dependencies at neighboring sites of the stroke skeleton, where the likelihood clique potential describes the statistical variations of directional observations at each site, and the smoothness prior clique potential describes the interactions among observations at neighboring sites. based on the cyclic directional observations by gabor filters, we formulate the stroke segmentation as an optimal labeling problem by the maximum a posteriori (map) criterion. the results of stroke segmentation on the etl-9b character database are encouraging.
a two-level method for unsupervised speaker-based audio segmentation. in this paper, we propose a two-level segmentation method that detects speaker changes in a continuous audio stream effectively. in our approach, we divide the change detection process into two levels: region level that detects the potential change regions containing candidate speaker change points, and boundary level that searches and refines the true change points. at the region level, we employ the modified generalized likelihood ratio (mglr) metric to search for the potential change regions in continuous local windows. at the boundary level, we perform t2 and bayesian information criterion (bic) algorithm to detect segment boundaries within the potential windows. the experimental results on the 1997 broadcast news hub4-ne mandarin corpus show the efficiency of the proposed scheme.
type-2 fuzzy markov random fields to handwritten character recognition. this paper integratesmarkov random fields (mrfs) with type-2 fuzzy sets (t2 fss) referred to as t2 fmrfs, which can handle the fuzziness of the labeling space as well as the randomness of observations within the unified framework. because fuzzy and random uncertainties exist in many computer vision problems, we extend the maximum a posteriori (map) criterion for the best labeling configuration by t2 fss operations. we apply t2 fmrfs as character models to similar handwritten chinese character recognition on etl- 9b and kaist databases. experimental results show that t2 fmrfs have a better classification and generalization ability for similar patterns than classical mrfs.
robust carving for non-lambertian objects. this paper presents a new surface reconstruction method that extends previous carving methods for non-lambertian objects by integrating the smoothness and image information of different aspects. we introduce a robust multi-view photo-consistency function and a single-view visual-consistency function. the former considers a general specularity that may introduce both reflectance models and robust statistics, while the latter is based on pixel homogeneity and continuity in individual images. the new consistent shape, with both multi-view and single-view, extends previous photo hull and visual hull, and is proven to be included in both photo hull and visual hull. this approximate shape is then refined by a graph-cut optimization method. sample reconstructions from real sequences for both global and local optimizations are demonstrated.
super-resolving compressed video with large artifacts. we propose methods to super-resolve compressed video sequences that may consist of frames with missing blocks of pixels in addition to compression artifacts. different from traditional resolution enhancement algorithms, our methods include two key components that are crucial to handle compressed video sequences. the first component is dynamic masking that dynamically computes image masks used to reject outliers. the second component is flow-based image repairing that reconstructs missing blocks or a whole frame by exploring both temporal and spatial information. we demonstrate the proposed methods with real mpeg video sequences.
conversation detection in feature films using finite state machines. in this paper, we address the problem of detecting the conversation scenes from feature films and propose and efficient and robust method for the stated problem. this method utilizes the structural information of the movie scenes with the combination of the low-level and mid-level features. we propose and demonstrate that a finite state machine (fsm) is suitable for detecting movie scenes with conversational settings. tow major characteristics of motion pictures, motion and audio, are used in our approach. the transitions of the fsm are determined by two mid-level features of each shot in the scene: the activity intensity and the face identity. our fsm has been experimented on over 50 clips with both positive and negative examples and produces convincing results.
gait recognition using fractal scale and wavelet moments. video-based gait recognition is a challenging problem in computer vision. in this paper, fractal scale wavelet analysis is applied to describe and automatically recognize gait. fractal scale based on wavelet analysis represents the self-similarity of signals, and improves the flexibility of wavelet moments. optimal wavelets based on generalized multi-resolution analysis are used to improve the recognition rate. descriptors of fractal scale are translation, scale and rotation invariant. moreover, a combination of fractal scale and wavelet moments improves the recognition rate. experiments show that the proposed descriptor is efficient for gait recognition.
a robust split-and-merge text segmentation approach for images. in this paper we describe a robust approach to segment text from color images. the proposed approach mainly includes four steps. firstly, a preprocessing step is utilized to enhance text blocks in images; secondly, these image blocks are split into connected components and most of them are eliminated by a component filtering procedure; thirdly, the left connected components are merged into several text layers, and a set of appropriate constraints are applied to find the real text layer; finally, the text layer is refined through a post-processing step to generate a binary output. our experimental results show that the proposed approach has a good performance in character recognition rate and processing speed. moreover, it is robust to text color, font size, as well as different styles of characters in different languages.
iterative figure-ground discrimination. figure-ground discrimination is an important problem in computer vision. previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of gaussians. however, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. in this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. we derive an iterative sampling-expectation (se) algorithm for estimating the color distribution and segmentation. there are several advantages of kernel-density estimation. first, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. second, it does not require model parameter initialization and estimation. the experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.
multi-view active shape model with robust parameter estimation. active shape model is an efficient way for localizing objects with variable shapes. when asm is extended to multiview cases, the parameter estimation approaches in previous works are often sensitive to the initial view, as they do not handle the unreliability of local texture search, which can be caused by bad initialization or cluttered background. to overcome this problem, we propose a novel algorithm for parameter estimation, using robust estimators to remove outliers. by weighting dynamically, our method acts as a model selection method, which reveals the hidden shape and view parameters from noisy observations of local texture models. experiments and comparisons on multi-view face alignment are carried out to show the efficiency of our approach.
multiblock-fusion scheme for face recognition. a multiblock-fusion scheme for face recognition is proposed in this paper. three face recognition algorithms, i.e. probabilistic match, linear discriminant analysis (lda) and discrete cosine transform (dct) are compared under the fusion strategy. by combining global and local features, the multiblock-fusion enhances the robustness against variations of illumination, facial expressions and pose. different partitions and combinations show specific performancefor each method. the experimental results demonstrate that the fusion outperforms the single method. some other characteristics of the three methods are also verified by the experiments.
robust eye detection under active infrared illumination. eye detection is very important for automatic face recognition and gaze tracking. in this paper we propose an algorithm for eye detection under active infrared (ir) illumination. a simple hardware enables us to make use of a physiological property of the eyes. a new thresholding method is introduced in order to effectively search the regions of interest (roi). an appearance model is then used to verify the pupil candidates. however, the existence of eyeglasses has a negative effect on selection of candidates. regarding this the generalized symmetry transform (gst) is exploited. by using a simplified distance weight, we reduce the computational cost of the original transform. experimental results demonstrate the effectiveness of the proposed eye detection method.