Ambiguous Recognition Research Articles

Recognition of fine-grained sow nursing behavior based on the SlowFast and hidden Markov models

Information on sow nursing behavior plays an important role in improving the survival rate of weaned piglets. To obtain more information on sow nursing processing, it is necessary to introduce beginning- and end-stage nursing behavior recognition. This is defined as fine-grained nursing behavior recognition. In this paper, we attempt to automate fine-grained nursing behavior recognition with surveillance video. First, nursing behavior is divided into three subbehaviors: before piglet sucking, piglet sucking and end piglet sucking. Then, a deep neural network model with a dual-stream structure, named SlowFast, is introduced to realize preliminary sow nursing behavior recognition by extracting spatiotemporal features. Finally, for some erroneous results in behavior recognition and the recognition ambiguity in behavior transition, a hidden Markov model (HMM) is introduced to modify the behavioral label sequence by using the Viterbi algorithm. To verify the effectiveness of our method, all experiments are carried out on sow nursing videos collected in an actual breeding environment. The results show that our method achieves a 90.50% sequence coincidence degree for the recognition results on long videos containing the whole nursing behavior, and the accuracy of behavior transition times reaches 87.05%. In summary, this study refines the automatic sow nursing behavior monitoring content to provide more information for precision livestock farming.

Review of: "Recognition ambiguity Kenya's foreign policy towards Afghanistan, Iraq, Israel and Palestine Under Moi Era (1978-1990)"

Review of: "Recognition ambiguity Kenya's foreign policy towards Afghanistan, Iraq, Israel and Palestine Under Moi Era (1978-1990)"

Facial expression recognition algorithm based on efficient channel attention

Facial expression recognition plays an increasingly important role in many fields. The richness of facial expressions and the ambiguity of expression recognition increase the difficulty of facial expression recognition. The recognition rate of existing algorithms is either high or complex. Either the complexity is low or the recognition rate is also low. Can a compromise be made between complexity and recognition rate? So, we designed a basic and effective convolutional neural networks (BASE) algorithm. In order to further improve the performance of the BASE algorithm, we propose a facial expression recognition algorithm based on efficient channel attention for deep convolutional neural networks (CECA-NET). We tested it on four commonly used facial expression databases. The efficient channel attention module increases the interactivity between channels and the key information that is not easy to be paid attention to is excavated. Experiments show that our method can obtain richer facial expression information and has stronger generalization ability.

Scale ambiguities in material recognition

SummaryMany natural materials have complex, multi-scale structures. Consequently, the inferred identity of a surface can vary with the assumed spatial scale of the scene: a plowed field seen from afar can resemble corduroy seen up close. We investigated this ‘material-scale ambiguity’ using 87 photographs of diverse materials (e.g., water, sand, stone, metal, and wood). Across two experiments, separate groups of participants (N = 72 adults) provided judgements of the material category depicted in each image, either with or without manipulations of apparent distance (by verbal instructions, or adding objects of familiar size). Our results demonstrate that these manipulations can cause identical images to be assigned to completely different material categories, depending on the assumed scale. Under challenging conditions, therefore, the categorization of materials is susceptible to simple manipulations of apparent distance, revealing a striking example of top-down effects in the interpretation of image features.

Hierarchical deep network with uncertainty-aware semi-supervised learning for vessel segmentation

The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not an easy task since the fine-detailed connected regions of organ vessel bring a lot of ambiguity in vessel segmentation and sub-type recognition, especially for the low-contrast capillary regions. Furthermore, recent two-staged approaches would accumulate and even amplify these inaccuracies from the first-stage whole vessel segmentation into the second-stage sub-type vessel pixel-wise classification. Moreover, the scarcity of manual annotation in organ vessels poses another challenge. In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels. In addition, we propose an uncertainty-aware semi-supervised training framework to alleviate the annotation-hungry limitation of deep models. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images. Our implementation is publicly available at https://github.com/XGGNet/Vessel-Seg.

Inherent limitation of digital imagery: spatial-phase vacillations and the ambiguity function

The spatial relationship of a scene image upon a pixilated focal-plane-array can temporally change due to scene or camera motion, microjitter of camera line-of-sight, etc. It was found that degradation caused by subpixel spatial-phase vacillations (SPV) upon the performance of matched filters was unexpectedly significant. Subpixel spatial-phase vacillations can cause degradations in matched filter performance to detect desired objects and discriminate other objects. SPV appear to be an inherent limitation of digital imagery when processed using matched filter methodology and can negatively impact the performance of systems. Mitigation of this degradation was found to be possible by utilizing one of several matched filter constructions such as a multi-filter enhanced matched filter (EMF) bank and a single-filter EMF. A significant conclusion of this investigation is that, for automatic target recognition applications, improved overall task performance should be realized by the use of an EMF bank. Dramatic reduction in computational resources for image matching multispectral imagery was accomplished by spectrally-collapsing multispectral imagery to form pseudoimages with the spectral information appearing as a texture in a grayscale image. The pattern recognition ambiguity function, which sets the fundamental performance limit of an image processing system, is introduced.

Effects of Shape Characteristics on Tactile Sensing Recognition and Brain Activation

Training tactile sensing for shape recognition is considered to be an effective rehabilitation technique. Previous studies in tactile sensing showed a tendency of recognition ambiguity, thus necessitating tactile sensing rehabilitation. Eleven subjects observed invisible objects using their fingers and were asked to identify the shape of the objects. The relationship between the degree of recognition and shape complexity was investigated. The results showed high self-confidence in recognizing high complexity shapes. The recognition process was confirmed in a second experiment measuring brain activation using near-infrared spectroscopy. Measurement of eight subjects showed the activation of verbal and visual processing regions, indicating that the act of handling the shape was translated to verbal expression and visual imaging. These results potentially quantify tactile sensing and contribute to the realization of personalized rehabilitation.

Multiple Hypothesis Semantic Mapping for Robust Data Association

In this paper, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomous systems. This is particularly evident in urban scenarios with several similar looking surroundings. Nevertheless, it requires the handling of a non-Gaussian and discrete random variable coming from object detectors. Previous methods facilitate semantic information for global localization and data association to reduce the instance ambiguity between the landmarks. However, many of these approaches do not deal with the creation of complete globally consistent representations of the environment and typically do not scale well. We utilize multiple hypothesis trees to derive a probabilistic data association for semantic measurements by means of position, instance and class to create a semantic representation. We propose an optimized mapping method and make use of a pose graph to derive a novel semantic SLAM solution. Furthermore, we show that semantic covisibility graphs allow for a precise place recognition in urban environments. We verify our approach using real-world outdoor dataset and demonstrate an average drift reduction of 33 % w.r.t. the raw odometry source. Moreover, our approach produces 55 % less hypotheses on average than a regular multiple hypotheses approach.

A New Method of the Pattern Storage and Recognition in Oscillatory Neural Networks Based on Resistive Switches

Development of neuromorphic systems based on new nanoelectronics materials and devices is of immediate interest for solving the problems of cognitive technology and cybernetics. Computational modeling of two- and three-oscillator schemes with thermally coupled VO2-switches is used to demonstrate a novel method of pattern storage and recognition in an impulse oscillator neural network (ONN), based on the high-order synchronization effect. The method allows storage of many patterns, and their number depends on the number of synchronous states Ns. The modeling demonstrates attainment of Ns of several orders both for a three-oscillator scheme Ns ~ 650 and for a two-oscillator scheme Ns ~ 260. A number of regularities are obtained, in particular, an optimal strength of oscillator coupling is revealed when Ns has a maximum. Algorithms of vector storage, network training, and test vector recognition are suggested, where the parameter of synchronization effectiveness is used as a degree of match. It is shown that, to reduce the ambiguity of recognition, the number coordinated in each vector should be at least one unit less than the number of oscillators. The demonstrated results are of a general character, and they may be applied in ONNs with various mechanisms and oscillator coupling topology.

Application of Object-Based Metrics for Recognition of Well-Designed Dashboards

ABSTRACTMeasuring the characteristics of visually emphasized objects displayed on a screen seems to be a promising way to rate user interface quality. On the other hand, it brings us problems regarding the ambiguity of object recognition caused by the subjective perception of the users. The goal of this research is to analyze the applicability of chosen object-based metrics for the evaluation of dashboard quality and the ability to distinguish well-design samples, with the focus on the subjective perception of the users. This article presents the model for the rating and classification of object-based metrics according to their ability to objectively distinguish well-designed dashboards. We use the model to rate 13 existing object-based metrics of aesthetics. Then, we present a new approach for the improvement of the rating of one object-based metric—Balance. We base the improvement on the combination of the object-based metric with the pixel-based analysis of color distribution on the screen.

Boundary Recognition of Light-Pause Marks via Grammar Testing Method

Boundary recognition is an important research of natural language processing, and it provides a basis for the application of Chinese word segmentation, chunk analysis, named entity recognition, etc. Based on ambiguity in boundary recognition of Chinese punctuation marks, this paper proposes grammar testing methods for boundary recognition of slight-pause marks and then calculates the annotation consistency of these methods. The statistical results show that grammar testing methods can greatly improve the annotation consistency of slight-pause marks boundary recognition. The consistency during the second time is 0.030 3 higher than during the first, which will help guarantee the consistency of large-scale corpus annotation and improve the quality of corpus annotation.

MCAW-DB: A glycan profile database capturing the ambiguity of glycan recognition patterns

Glycan-binding protein (GBP) interaction experiments, such as glycan microarrays, are often used to understand glycan recognition patterns. However, oftentimes the interpretation of glycan array experimental data makes it difficult to identify discrete GBP binding patterns due to their ambiguity. It is known that lectins, for example, are non-specific in their binding affinities; the same lectin can bind to different monosaccharides or even different glycan structures. In bioinformatics, several tools to mine the data generated from these sorts of experiments have been developed. These tools take a library of predefined motifs, which are commonly-found glycan patterns such as sialyl-Lewis X, and attempt to identify the motif(s) that are specific to the GBP being analyzed. In our previous work, as opposed to using predefined motifs, we developed the Multiple Carbohydrate Alignment with Weights (MCAW) tool to visualize the state of the glycans being recognized by the GBP under analysis. We previously reported on the effectiveness of our tool and algorithm by analyzing several glycan array datasets from the Consortium of Functional Glycomics (CFG). In this work, we report on our analysis of 1081 data sets which we collected from the CFG, the results of which we have made publicly and freely available as a database called MCAW-DB. We introduce this database, its usage and describe several analysis results. We show how MCAW-DB can be used to analyze glycan-binding patterns of GBPs amidst their ambiguity. For example, the visualization of glycan-binding patterns in MCAW-DB show how they correlate with the concentrations of the samples used in the array experiments. Using MCAW-DB, the patterns of glycans found to bind to various GBP-glycan binding proteins are visualized, indicating the binding “environment” of the glycans. Thus, the ambiguity of glycan recognition is numerically represented, along with the patterns of monosaccharides surrounding the binding region. The profiles in MCAW-DB could potentially be used as predictors of affinity of unknown or novel glycans to particular GBPs by comparing how well they match the existing profiles for those GBPs. Moreover, as the glycan profiles of diseased tissues become available, glycan alignments could also be used to identify glycan biomarkers unique to that tissue. Databases of these alignments may be of great use for drug discovery.

Enhanced dynamic programming approach for subunit modelling to handle segmentation and recognition ambiguities in sign language

Sign language serves as a primary means of communication among the deaf impaired community. The major challenges faced by the Sign Language Recognition (SLR) system are recognizing signs from large vocabularies in continuous video sequences. In this research paper, a novel subunit sign modelling framework is proposed for vision-based SLR which aims in solving the major issues in SLR systems. The problem of hand segmentation ambiguities and segregating epentheses movements between two adjacent signs in continuous video sequences are addressed. A novel subunit sign modelling framework is presented and illustrated to embark upon these problems while considering large-vocabularies. This framework is developed using a novel methodology of Enhanced Dynamic Programming (EDP) approach in subunit sign modelling. This EDP framework works with a combination of dynamic time warping and spatiotemporal clustering techniques. Since, sign language consists of both spatial and temporal feature vectors, dynamic time warping is used as a distance measure to compute the distance between two adjacent signs in sign trajectories. This distance is used as a temporal feature vector during the clustering of spatial feature vectors using Minimum Entropy Clustering (MEC). This process is done recursively to cluster all the epentheses movements dynamically without using any explicit or implicit modelling. Experimental results have confirmed that the computation cost of the proposed system is less because the epenthesis movements are eliminated before classification and the gesture base space utilized by the sign gestures is very low because the proposed system does not require any modelling to handle epenthesis movements. The results obtained from the proposed subunit sign modelling framework is compared with other existing models in order to prove that the proposed system is best among the existing systems.

An unsupervised meta-graph clustering based prototype-specific feature quantification for human re-identification in video surveillance

Human re-identification is an emerging research area in the field of visual surveillance. It refers to the task of associating the images of the persons captured by one camera (probe set) with the images captured by another camera (gallery set) at different locations in different time instances. The performance of these systems are often challenged by some factors—variation in articulated human pose and clothing, frequent occlusion with various objects, change in light illumination, and the cluttered background are to name a few. Besides, the ambiguity in recognition increases between individuals with similar appearance. In this paper, we present a novel framework for human re-identification that finds the correspondence image pair across non-overlapping camera views in the presence of the above challenging scenarios. The proposed framework handles the visual ambiguity having similar appearance by first segmenting the gallery instances into disjoint prototypes (groups), where each prototype represents the images with high commonality. Then, a weighing scheme is formulated that quantifies the selective and distinct information about the features concerning the level of contribution against each prototype. Finally, the prototype specific weights are utilized in the similarity measure and fused with the existing generic weighing to facilitates improvement in the re-identification. Exhaustive simulation on three benchmark datasets alongside the CMC (Cumulative Matching Characteristics) plot enumerate the efficacy of our proposed framework over the counterparts.

The Influence of Internet Use on Interpersonal Interaction among Chinese Urban Residents: The Mediating Effect of Social Identification

The instability of social norms on the Internet causes the diversity of social identification. Meanwhile, the anonymity of online social identity and the chaos of the role-playing among the interacting participants cause an ambiguity of identity recognition, which intensifies anxiety about interpersonal interaction. Methods that promote face-to-face interpersonal interaction through the reconstruction of the identification to the social system and intergroup trust is worth further research. Based on a telephone survey of urban residents in thirty-six cities in China (N=1080), the study focuses on the influence of Internet use on interpersonal interaction of urban residents and the mediation effect of social identification. The results show that Internet use has a negative effect on the interpersonal interactions of urban residents, and social identification plays a mediating effect between Internet use and interpersonal interaction. Implications of the results are discussed.

Object Recognition Based on the Context Aware Decision-Level Fusion in Multiviews Imagery

Spectral similarities and spatial adjacencies between various kinds of objects, shadow, and occluded areas behind high-rise objects as well as the complex relationships between various object types lead to the difficulties and ambiguities in object recognition in urban areas. Using a knowledge base containing the contextual information together with the multiviews imagery may improve the object recognition results in such a situation. The proposed object recognition strategy in this paper has two main stages: single view and multiviews processes. In the single view process, defining region’s properties for each of the segmented regions, the object-based image analysis (OBIA) is performed independently on the individual views. In the second stage, the classified objects of all views are fused together through a decision-level fusion based on the scene contextual information in order to refine the classification results. Sensory information, analyzing visibility maps, height, and the structural characteristics of the multiviews classified objects define the scene contextual information. Evaluation of the capabilities of the proposed context aware object recognition methodology is performed on two datasets: 1) multiangular Worldview-2 satellite images over Rio de Janeiro in Brazil and 2) multiviews digital modular camera (DMC) aerial images over a complex urban area in Germany. The obtained results represent that using the contextual information together with a decision-level fusion of multiviews, the object recognition difficulties and ambiguities are decreased and the overall accuracy and the kappa are gradually improved for both of the WorldView-2 and the DMC datasets.

Three orders in the organization of human action: On the interface between knowledge, power, and emotion in interaction and social relations

AbstractAll social life is based on people's ability to recognize what others are doing. Recently, the mechanisms underlying this human ability have become the focus of a growing multidisciplinary interest. This article contributes to this line of research by considering how people's orientations to who they are to each other are built-in in the organization action. We outline a unifying theoretical framework in which the basic facets of human social relations are seen as being anchored in three orders—epistemic order, deontic order, and emotional order—each of which, we argue, also pertains to action recognition. This framework allows us to account for common ambiguities in action recognition and to describe relationship negotiations involving a complex interface between knowledge, power, and emotion. (Action recognition, social relations, conversation analysis, status, stance, epistemic rights, deontic rights, emotion)*

Number Plate Recognition Based on Multi Feature and Character Recognition

Intelligent transportation system has become an integral part of the transportation system and it consists of number plate recognition. A recognition system implemented to control the payment of fees in parking areas, highways, airport or harbour cargo control etc. There are considered an approach to identify the vehicle through recognizing of its license plate using multi feature recognition and neural network techniques. Multi feature recognition is to enhance several features of for target. The proposed method use improved character recognition by through setting proper numbers of hidden layers in the character kinds. This method can improve the efficiency and accuracy of vehicle number plate recognition. Multi pattern recognition is an algorithm for recognition of multi feature concept into the information. Proposed method solves the number plate with colour features as well as texture features and back ground features. By using multi feature concept problem of the ambiguity of character recognition can be solved. After using multi feature concept for number plate positioning, it can be obtained the projection of histogram mainly through a threshold. The result of number plate recognition is to change the plate character in to text character. Using back propagation of neural networks to resolve complex characters number plate and defective characters, and avoid heavy data analysis and mathematical modelling works. Recognition problems of automobile number plate such as character kinds, the numbers, and confusing of words can also be solved Keywords: fuzzy theory; number plate recognition; character recognition.

Ambiguity in phoneme recognition when using a bone conduction microphone

Bone conduction microphones are often promoted as good means for recoding voice signals in very noisy and/or harsh environments. However, it is reported in the literature that the speech intelligibility when using contact microphones is, in all noise conditions, worse than when using a boom microphone. Therefore, long term spectra of speech signals simultaneously recorded with bone and air microphones have been analyzed. This analysis shows, for all measured subjects, that the module of the transfer function between the signal recorded at the air-microphone and, because of this, at the contact microphone increases toward higher frequencies. The results also show large interindividual differences. In order to get more perception related information, a corpus of French vowels has been recorded simultaneously with air and contact microphone. This corpus has been used for a listening test aiming to show confusion between different vowels. The main confusions are for vowels having the same frequency of the first formant (e.g., [i][y][u]) which then are perceived as the central vowel. These confusions could explain the systematically lower intelligibility reported for speech when recorded with contact microphones. Planned experiments using plosive and fricative consonants should give more information about transfer mechanisms of bone conducted voice.

Paraphrase Lattice for Statistical Machine Translation

Lattice decoding in statistical machine translation (SMT) is useful in speech translation and in the translation of German because it can handle input ambiguities such as speech recognition ambiguities and German word segmentation ambiguities. In this paper, we show that lattice decoding is also useful for handling input variations. “Input variations” refers to the differences in input texts with the same meaning. Given an input sentence, we build a lattice which represents paraphrases of the input sentence. We call this a paraphrase lattice. Then, we give the paraphrase lattice as an input to a lattice decoder. The lattice decoder searches for the best path of the paraphrase lattice and outputs the best translation. Experimental results using the IWSLT dataset and the Europarl dataset show that our proposed method obtains significant gains in BLEU scores.