waashk/dblp huggingface dataset:
semantic feature extraction using genetic programming in image retrieval. one of the big hurdles facing current content-based image retrieval (cbir) is the semantic gap between the low-level visual features and the high-level semantic features. we proposed an approach to describe and extract the global texture semantic features. according to tamura texture model, we utilize the linguistic variable to describe the texture semantics, so it becomes possible to depict the image in linguistic expression such as coarse, fine. and we use genetic programming to simulate the human visual perception and extract the semantic features value. our experiments show that the semantic features have good accordance with the human perception, and also have good retrieval performance. in some extent, our approach bridges the semantic gap in cbir.
chinese character recognition via gegenbauer moments. moment features have been applied in pattern recognition systems since the moment method was developed. in this paper, a set of moment features extracted from the newly developed gegenbauer moment method for chinese character recognition is proposed. compared with the results based on other moment methods, the gegenbauer moments can provide a modest improvement in terms of recognition for those chinese characters that are very similar in shapes. we conclude that the proposed new gegenbauer moment method can supplement the existing chinese character recognition techniques based on local structure features.
a machine learning approach for locating boundaries of liver tumors in ct images. in this paper, we propose a novel machine learning approach for locating boundaries of liver tumors in ct (computed tomography) images. given a marker indicating a rough location of a tumor, the proposed solution locates its boundary. our approach consists of training process and locating process. in training process, we train adaboosted histogram classifiers to classify true boundary positions and false ones on the 1-d intensity profiles of tumor regions. in locating process, we locate the boundaries by using the trained adaboosted histogram classifiers. the novelty of our approach is that we use adaboost in the training process to learn diverse intensity distributions of the tumor regions, and utilize the trained results successfully in locating process. experimental results show our approach locates the boundaries successfully, despite the diverse intensity distributions of the tumor regions, marker location variability and tumor region shape variability. our framework is also generic and can be applied for locating boundaries of blob-like targets with diverse intensity distributions in other applications.
classification of audio signals in all-night sleep studies. in this paper, we describe the classification of audio signals recorded in all-night sleep studies. our objective is to separate the episodes into snoring sounds and non-snoring sounds. to begin with, we employ hierarchical classification schemes to classify sounds into human sounds and non-human sounds. we then attempt to organize human sounds into snore and non-snore segments based on their acoustic properties. we perform further analysis of the extracted snoring sounds to check if the testee has apnea. experimental results have validated the efficacy of the proposed method.
behavior modeling and recognition based on space-time image features. a novel method based on space-time image features is proposed for automatic behavior modeling and recognition. the method is composed of the following three steps: (1). video sequences are converted into a space-time image, and the image is divided into equal length segments; (2). from these segments, an unsupervised technique based on dynamic time warping is used to determine the groups of different behaviors; (3). the behavior templates are built according to these groups, and are used for the recognition of behaviors. the method does not need to track the body parts and can model the behaviors without any prior knowledge. experiments also demonstrate the effectiveness of our new method.
cvml - an xml-based computer vision markup language. we propose an xml-based computer vision markup language for use in cognitive vision, to enable separate research groups to collaborate with each other as well as making their research results more available to other areas of science and industry, without having to reveal any proprietary ideas, algorithms or even software. the computer vision markup language can communicate any type and amount of information, making unavailable functionality accessible to anyone. in this paper we introduce the language and describe how we have implemented it in a very large cognitive vision project. we provide a free open source library for working with this language, which can easily be implemented into existing code providing seamless network communication abilities and multi-platform support. last we describe the future of cvml and how it might evolve to include other areas of research.
probabilistic image-based rendering with gaussian mixture model. one major challenge in traditional image-based rendering is 3d scene reconstruction by estimating accurate dense depth map, which suffers from the ambiguities in textureless or periodically textured regions. alternatively, statistical methods may be used to estimate a most likely color for each pixel for photorealistic rendering from multiple views of the same scene. such statistical methods normally require a relatively large number of input images to achieve reasonable quality for the synthesized image, if the estimation is purely nonparametric. in this paper, based on some reasonable assumptions on the configuration of the multiple views, we propose to use a two-component gaussian mixture model for the appearance of a given pixel in all the views so that both the problem of occlusion and the problem of noise can be considered simultaneously. then we use the expectation-maximization algorithm to estimate the model parameters. the virtual pixel is given as a maximum likelihood estimate for one of the mixture components. experiments shows that reasonable performance can be obtained even with only a few input images.
hierarchical identification of palmprint using line-based hough transform. the hough transform (ht) is a powerful technique for line detection. its main advantage is that it is relatively unaffected by noise or gaps in lines. this work proposes a line-based hough transform method, which extracts global features for coarse-level filtering in hierarchical palmprint identification system. the local information extracted from position and orientation of individual line is used for further finelevel identification. line-based hausdorff distance (lhd) algorithm is applied for local line matching. this is a novel approach to the application of the hough transform for feature extraction of palmprint. experiments had been conducted on a palmprint database collected by hong kong polytechnic university. 99% identification rate has been achieved with much reduced running time.
supervised training based hand gesture recognition system. we have developed a hand gesture recognition system, based on the shape analysis of static gestures, for human computer interaction purposes. our appearance-based recognition uses modified fourier descriptors for the classification of hand shapes. as always found in literature, such recognition systems consist of two phases: training and recognition. in our new practicalapproach, following the chosen appearance-based model, training and recognition is done in an interactive supervised way: the adaptation for untrained gestures is also solved by hand signals. our experimental results with three different users are reported. in this paper, besides describing the recognition itself, we demonstrate our interactive training method in a practicalapplication.
multi-resolution curve alignment based on salient features. in this paper, we present a novel approach to the problem of curve alignment, which measures and matches the similarity of two curves. our method extracts curve features with high priorities given to salient and general features, and therefore leads to a satisfied result that meets human being's perceptions. we investigate the bimorphism in the level of subsegment correspondences to ensure symmetric mapping. a solution to closed curve alignment is also given. finally, the coarse-to-fine aligning algorithm is introduced to match curves under different resolutions. the experiments showed satisfying and approving results, that our approach can capture the curve features and align them properly.
dynamic training of hand gesture recognition system. we developed an augmented reality tool for vision-based hand gesture recognition in a camera-projector system. our recognition method uses modified fourier descriptors for the classification of static hand gestures. hand segmentation is based on a background subtraction method, which is improved to handle background changes. most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. however, there are numerous situations when several untrained users would like to use gestures for the interaction. in our new practical approach the correction of faulty detected gestures is done during the recognition itself. our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.
a geometric active contour framework using multi-cue and local feature. there exist two key issues in the traditional geometric active contour (gac): only single cue based driven force and edge leakage. in this paper, we propose two new gacs, i.e., multi-cue based gac (mc-gac) and local-feature based gac (lf-gac), to deal with these two problems respectively. in mc-gac, a multi-cue based force is generated by introducing the customized vector flow into the model. to utilize local feature information, lf-gac is developed, in which local edge information is considered to produce continuous edge. further, we unify these two gacs into a novel general gac framework. extensive experiments showed the advantages of the proposed method.
multi-view-based cooperative tracking of multiple human objects in cluttered scenes. this paper presents a multi-view-based cooperative tracking of multiple human objects. occlusion is one of the difficult problems for human object tracking in cluttered scenes. based on the homographic relation between the two views, we proposed a so-called cooperative tracking which consists of particle filter tracking for the objects in different views. the multiple view tracking is modeled as different sequences of hidden process and observation. in addition, based on the interaction between targets, a hidden variable is added in to reveal the reliability of the tracking result in that specific view. with this hidden variable, the cooperative tracking allocates computational resources for tracking the objects in different views. experimental results show the efficiency of the proposed method.
multibaseline stereo in the presence of specular reflections. we address the problem of accurate depth estimation using multibaseline stereo in the presence of specular reflections. specular reflections can cause the intensity and color of corresponding points to change dramatically according to different viewpoints, thus producing severe matching errors for various stereo algorithms. in this paper, we propose a new method to deal with this problem by treating specular reflections as occlusions. our idea is to first detect specular pixels by computing the uncertainty of depth estimates. then we combine the use of flexible windows and an adaptively selected subset of images to avoid these specular areas in all the multibaseline stereo images. even though specularities may exist in the reference image, accurate depth is nevertheless estimated for all pixels. experiments show that our consideration of specular reflections leads to improved stereo results.
a fast mode decision method for h.264/avc using the spatial-temporal prediction scheme. in the h.264/avc coding standard, the high computation cost of the full searching in the reference software jm-9.3 makes the encoding process inefficient. in this paper, the spatial-temporal correlations between the current frame and reference frame are analyzed to develop a fast mode decision method in which no extra image processes are used. furthermore, the concept of drift compensation is adopted to avoid the error accumulation phenomenon during the mode decision process. the experimental results show that the computation cost may be reduced above 60% and the average psnr is only dropped about 0.04db. keywords: h.264, jm-9.3, mode decision.
new algorithm for geometric transformations of digital images and patterns. nonlinear geometric transformations, such as the splitting-shooting method (ssm), the splitting-integrating method (sim), and their advanced versions s¯s¯m, s¯im, cs¯i¯m have been developed in [4-6]. this paper proposes new versions of such nonlinear transformations, s¯s¯#m, s¯i¯#/m, cs¯#i¯#m, approximated by piecewise linear transformations to circumvent the necessity of finding the nonlinear solutions, and to obtain exact integration computationally. the absolute errors of pixel greyness are proven to be 0(h), h is the length of a pixel region. it is worth pointing out that the new algorithms in this paper do not produce any sequential errors as n = n0. apart from this distinctive feature, the absolute error bound 0(h) can be applied to all kinds of images with discontinuity, including fully isolated pixels.
entropic estimation of noise for medical volume restoration. this paper presents an unsupervised approach for medical volume restoration. to cope with various scanning modalities and strongly corrupted data, an original information tool is introduced: the entropic deviation. to validate the robustness of this estimation, a non-linear restoration filter based on markov random fields is proposed. no parameter tuning is required from the user thanks to the adaptive value of the entropy power. finally, the good quality of the filtered volumes are promising for any clustering application aiming at anatomical structures extraction in medical volume datasets.
selection of statistical features based on mutual information for classification of human coding and non-coding dna sequences. the classification of human gene sequences into exons and introns is an important but difficult problem. we study the discriminative power of various statistical features (22 in total) in term of their mutual information (mi). by performing correlation analysis, we are able to identify a set of features that has high mi value while at the same time is complementary in their information content. using the set of features, which consists of the three sz features, the ami feature, and the first stop codon feature, we are able to achieve classification accuracy as high as 92%.
a hybrid method of unsupervised feature selection based on ranking. feature selection is a key problem to pattern recognition. so far, most methods of feature selection focus on sample data where class information is available. for sample data without class labels, however, the related methods for feature selection are few. this paper proposes a new way of unsupervised feature selection. our method is a hybrid approach based on ranking the features according to their relevance to clustering using a new ranking index which belongs to exponential entropy. firstly a candidate feature subset is selected using a modified fuzzy feature evaluation index (ffei) with a new method to calculate the feature weight, which makes the algorithm to be robust and independent of domain knowledge. then a wrapper method is used to select compact feature subset from the candidate feature set based on the clustering performance. experimental results on benchmark data sets indicate the effectiveness of the proposed method.
rehashing for bayesian geometric hashing. geometric hashing is a model-based recognition technique based on matching of transformation-invariant object representations stored in a hash table. in the last decade a number of enhancements have been suggested to the basic method improving its performance and reliability. one of the important enhancements is rehashing, improving the computational performance by dealing with the problem of non-uniform occupancy of hash bins. however the proposed rehashing schemes aim to redistribute the hash entries uniformly, which is not appropriate for bayesian approach, another enhancement optimizing the recognition rate in presence of noise. in this paper we derive the rehashing for bayesian voting scheme, thus improving the computational performance by minimizing the hash table size and the number of bins accessed, while maintaining optimal recognition rate.
a novel framework for urban change detection using vhr satellite images. we present a novel framework for detecting urban changes from a pair of very high resolution (vhr) satellite images such as those taken by satellite quickbird-ii or ikonos. image differences due to variations of imaging conditions such as view angle and illumination are distinguished from significant urban changes in the scene. first, we adopt a new image registration method, which makes several useful geometrical constraints available. then we find changed line segments over time. after that we match scale invariant feature transform (sift) points and generate corresponding regions in order to exclude changed line segments due to parallax. we perform shadow detection to exclude changed line segments due to shadow change. finally we group the remaining changed line segments into clusters, among which the significant ones form the changed regions as output. our experiments with real quickbird-ii images show that the proposed method can well detect significant urban changes.
ldv remote voice acquisition and enhancement. laser doppler vibrometers (ldvs) have been widely used in industry inspection. one of the superior characteristics of an ldv is that it can detect and measure extremely tiny vibration of a target at a large distance, with sensitivity on the order of 1ìm/s. on the other hand, we have found that most objects nearby audio sources can be vibrated by the audio waves. these two aspects motivate our research in a new application of the ldvs, namely remote voice detection from surrounding vibrated objects. however, the detected speech signals may be corrupted by many noise sources, such as laser photon noises, target movements, and background acoustic noises (wind, engine sound, etc.). therefore, speech enhancement algorithms based on gaussian bandpass and wiener filters are designed to effectively improve the intelligibility of the noisy voice signals detected by the ldv system experimental results show that remote voice detection via an ldv is very promising, when choosing appropriate targets close to human subjects and using the proposed enhancement techniques.
jet based feature classification. in this paper, we investigate to which extent the "raw" mapping of taylor series coefficients into jet-space can be used as a "language" for describing local image structure in terms of geometrical image features. based on empirical data from the van hateren database, we discuss modelling of probability densities for different feature types, calculate feature posterior maps, and finally perform classification or simultaneous feature detection in a bayesian framework. we introduce the brownian image model as a generic background class and extend with empirically estimated densities for edges and blobs. we give examples of simultaneous feature detection across scale.
new method for sparse point-sets matching with underlying non-rigidity. we propose a novel method for matching two sparse point-sets of identical cardinality with distribution similarity. the point-sets are extracted from two subjects with underlying non-rigidity and non-uniform scaling, one being a model set with point identity and the other representing the observed data. there exists neither a global nor local affine transformations between the point-sets. to establish a one-to-one match, we introduce anew similarity k-dimensional tree which is well adapted and robust to such data. weconstruct a similarity k-d tree for the model set. then a corresponding tree of the data set is constructed following the structure information embedded in the model tree. matching sequences of the two point sets are generated by traversing the identically structured trees. experimental results based on synthetic data analysis and real data confirm this method is applicable for robust spatial matching of sparse point-sets under non-rigid distortion.
scene identification using discriminative patterns. with the proliferation of camera phones, new information retrieval applications will emerge. the image of a scene captured by a camera phone can be a query to a remote server to identify the scene and return relevant information. but unconstrained scene identification is an open problem. in this paper, we propose a discriminative measure to rank image patterns sampled from target scene classes. support vector classifiers are then trained using top discriminative patterns for scene identification using voting. we demonstrate our generic approach on two scene databases (zubud and stoic) with promising results.
a segmentation method for touching italic characters. segmentation is an essential part of a recognition system. it is difficult to handle touching characters, especially for italic fonts. we present a method to achieve the accurate segmentation of touching italic characters. it is free of slant correction, so extra noises will not be introduced. we use slant projection and contour analysis to find the segmentation points. then the shortest path approach is adopted for accurately locating the cut path of each candidate segmentation point. based on dynamic programming, we can find the best segmentation result from those cut paths. by this method, we can improve the recognition rate on italic fonts.
multimodal registration using the discrete wavelet frame transform. image registration is a critical step in medical image analysis. in this paper, a novel image registration method based on the discrete wavelet frame transform (dwft) and the sum of absolute distance (sad) method is proposed. first, the multimodal images are decomposed by dwft, which is shift-invariant compared to traditional dyadic wavelet transforms. then the energy maps are computed from the details sub-band images. finally, genetic algorithm (ga) is adopted to obtain the minimum sad between the two energy maps. the proposed method is tested on 50 pairs of two-dimensional t1-weighted and t2- weighted modal images. experimental results demonstrated that the proposed method can achieve high accuracy in the rigid transformation case.
consistent line clusters for building recognition in cbir. this paper introduces a new mid-level feature, the consistent line cluster, for use in content-based image retrieval. the color, orientation, and spatial features of line segments are exploited to group them into line clusters. the interrelationships among different clusters and the intrarelationships within single clusters are used to recognize and roughly locate buildings in photographic images. experiments are performed on a database of color images of outdoor scenes.