Indoor positioning technologies have been widely used in many industrial applications such as intelligent inventory management and assembly control. Ultra-Wide Band (UWB) can provide sub-metre level positioning accuracy at a distance of several dozen metres with high robustness. However, UWB measurements can be contaminated by reflected, refracted and deflected signal in practice, the contaminated measurements are outliers in data processing and degrade the positioning performance if they are not treated properly. In indoor environments, UWB signals may penetrate some structures/materials and these refracted signals are outliers in data processing for position determination. This paper investigates the statistical distribution of errors due to refracted/penetrated signals. Classification and Regression random forests are used to detect outlier measurements and apply error mitigation, respectively. Two datasets are collected to cross-validate the proposed method. The results show that the proposed method can achieve a detection accuracy of about 80%. Besides, the datasets show that rejecting detected outlier measurements and applying error mitigation can improve distance measurement accuracy by 80%.

In this study, the authors present a new area‐based stereo matching algorithm that computes dense disparity maps for a real‐time vision system. Although many stereo matching algorithms have been proposed in recent years, correlation‐based algorithms still have an edge because of speed and less memory requirements. The selection of appropriate shape and size of the matching window is a difficult problem for correlation‐based algorithms. In the proposed approach, two correlation windows are used to improve the performance of the algorithm while maintaining its real‐time suitability. The CPU implementation of the proposed algorithm computes more than 10 frame/s. Unlike other area‐based stereo matching algorithms, this method works very well at disparity boundaries as well as in low textured image areas and computes a dense and sharp disparity map. Evaluations on the benchmark Middlebury stereo datasets have been performed to demonstrate the qualitative and quantitative performance of the proposed algorithm.

With recent advances in multimedia technology, the involvement of digital images/videos in crimes has been increasing significantly. Identification of individuals in these images/videos can be challenging. For example, in cases of child sexual abuse, child pornography, and masked gunmen, the faces of criminals or victims are often hidden or covered and only some body parts (e.g., back, thigh, and arm) can be observed from the digital evidence. Although tattoos and scars can be used for identification in some cases, they are neither universal nor unique. We propose a group of skin marks named Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) as a biometric trait for forensic identification. To support the scientific underpinnings of using RPPVSM patterns as a novel biometric trait, the individuality was studied. RPPVSM on the backs of 269 male subjects were examined. We found that RPPVSM in middle to low density patterns tend to form an independent and uniform distribution, while RPPVSM in high density patterns tend to form clusters. We present in this paper an individuality model for the independently and uniformly distributed RPPVSM patterns. When compared to the empirical results, this model fits the empirical distribution very well. Finally, the predicted error rates for verification and identification are reported.

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves.

Recent technological advances have allowed for a proliferation of digital images that may be involved in crimes. Using these images as evidence in legal cases like child pornography and masked gunmen can be challenging because usually the faces of the suspects are not visible. To perform personal identification in these images, we propose a biometric trait composed of a group of skin marks including, but not limited to, nevi, lentigines, cherry hemangiomas, and seborrheic keratoses. Due to their biological characteristics, we have grouped these as "Relatively Permanent Pigmented or Vascular Skin Marks," abbreviated as RPPVSM. As statistical study of RPPVSM is essential before investigating their discriminative power, we present in this paper the fundamental statistics of RPPVSM. Back torso images were collected from 144 Caucasian, Asian, and Latino males, and a researcher trained in dermatology manually identified their RPPVSMs. The statistical results show that Caucasians tend to have more RPPVSMs than Asians and Latinos, and over 80 percent of middle to low density RPPVSM patterns are independently and uniformly distributed.

Colorization of a grayscale photograph often requires considerable effort from the user, either by placing numerous color scribbles over the image to initialize a color propagation algorithm, or by looking for a suitable reference image from which color information can be transferred. Even with this user supplied data, colorized images may appear unnatural as a result of limited user skill or inaccurate transfer of colors. To address these problems, we propose a colorization system that leverages the rich image content on the internet. As input, the user needs only to provide a semantic text label and segmentation cues for major foreground objects in the scene. With this information, images are downloaded from photo sharing websites and filtered to obtain suitable reference images that are reliable for color transfer to the given grayscale photo. Different image colorizations are generated from the various reference images, and a graphical user interface is provided to easily select the desired result. Our experiments and user study demonstrate the greater effectiveness of this system in comparison to previous techniques.

We present a new disparity refinement algorithm that utilize color information of the reference image to generate sharp disparity maps. While existing methods use iterative approaches or require multiple disparity maps, the proposed algorithm uses a single pass approach to reduce errors at depth discontinuities. The experimental results are evaluated on the Middlebury benchmark dataset; show the effectiveness of the proposed algorithm.

Conventional shape from shading (SFS) algorithms are unable to deal with multi-color image satisfactory. This is because the assumption of constant surface albedo in the algorithms is not applicable to multi-color images. This paper proposes a new SFS approach for multi-color images through a segmentation-based shading recovery technique. With this technique a gray image is firstly extracted from the multi-color image containing better shading information compared with other color-to-gray conversion methods. The shading is recovered in the gray image as if the objects were made of single color. Shape of the multi-color object can then be recovered by classical gray-scaled SFS methods. Experimental results with synthetic and real multi-color images are presented. The obtained results corroborate that the proposed scheme is able to deliver better performance compared with other color SFS methods.

This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), ‘Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning’, Journal of Personality and Social Psychology, 66, 310–328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.

The aim of computational learning algorithm is to establish grounds that works for any types of data, once and for all. However, majority of the classifiers assume the datasets are balanced. This research is targeted towards obtaining a model that is able to handle imbalanced data well. This work progresses by examining the efficiency of the model in evaluating imbalanced medical data. The model adopted a derivation of support vector machines in selecting variables. The classification phase uses unsupervised learning algorithm of Emergent Self-Organizing Map. Experimental results show that the criterion based on weight vector derivative achieves good results and performs consistently well over imbalance data.

In general, a fuzzy neural network (FNN) is characterized by its learning algorithm and its linguistic knowledge representation. However, it does not necessarily interact with its environment when the training data is assumed to be an accurate description of the environment under consideration. In interactive problems, it would be more appropriate for an agent to learn from its own experience through interactions with the environment, i.e., reinforcement learning. In this paper, three clustering algorithms are developed based on the reinforcement learning paradigm. This allows a more accurate description of the clusters as the clustering process is influenced by the reinforcement signal. They are the REINFORCE clustering technique I (RCT-I), the REINFORCE clustering technique II (RCT-II), and the episodic REINFORCE clustering technique (ERCT). The integrations of the RCT-I, the RCT-II, and the ERCT within the pseudo-outer product truth value restriction (POPTVR), which is a fuzzy neural network integrated with the truth restriction value (TVR) inference scheme in its five layered feedforward neural network, form the RPOPTVR-I, the RPOPTVR-II, and the ERPOPTVR, respectively. The Iris, Phoneme, and Spiral data sets are used for benchmarking. For both Iris and Phoneme data, the RPOPTVR is able to yield better classification results which are higher than the original POPTVR and the modified POPTVR over the three test trials. For the Spiral data set, the RPOPTVR-II is able to outperform the others by at least a margin of 5.8% over multiple test trials. The three reinforcement-based clustering techniques applied to the POPTVR network are able to exhibit the trial-and-error search characteristic that yields higher qualitative performance.

This paper attempts to model human brain’s cognitive process at the primary visual cortex to comprehend road sign. The cortical maps in visual cortex have been widely focused in recent research. We propose a visual model that locates road sign in an image and identifies the localized road sign. Gabor wavelets are used to encode visual information and extract features. Self-organizing maps are used to cluster and classify the road sign images. We evaluate the system with various test sets. The experimental results show encouraging recognition hit rates. There are quite a number of literatures [1]–[13] introducing different approaches to classify road sign, but none has adopted unsupervised approach. This work makes use of two-tier topological maps to recognize road signs. First-tier map, called detecting map, filters out non-road sign images and regions. Second-tier map, called recognizing map, classifies a road sign into appropriate class.

This paper presents a modified version of U-tree (A.K. McCallum, 1996), a memory-based reinforcement learning (RL) algorithm that uses selective perception and short-term memory to handle partially observable Markovian decision processes (POMDP). Conventional RL algorithms rely on a set of pre-defined states to model the environment, even though it can learn the state transitions from experience. U-tree is not only able to do that, it can also build the state model by itself based on raw sensor inputs. This paper enhances U-Treepsilas model generation process. The paper also shows that because of the simplified and yet effective state model generated by U-tree, it is feasible and preferable to adopt the classical dynamic programming (DP) algorithm for average reward MDP to solve some difficult POMDP problems. The new U-tree is tested using a car-driving task with 31,224 world states, with the agent having very limited sensory information and little knowledge about the dynamics of the environment.

Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds superior performance. Extensive experiments have been conducted to test the effectiveness of these two networks, using various clustering algorithms. It follows that the SDCT and UDCT clustering algorithms are particularly suited to networks based on the Yager inference rule.

This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.

This paper applies relevance feedback technique in spoken language recognition task, in which we consider a test utterance as a test query. Assuming that we have a labeled multilingual corpus, we exploit the retrieved utterances from such a reference corpus to automatically augment the test query. Note that successful spoken language recognition relies on sufficient query data. The proposed method is especially effective for short query by expanding the query at a low cost. Experiments show that unsupervised relevance feedback reduces the relative equal-error-rate by 16.2%, 4.9% and 10.2% on NIST LRE 1996, 2003 and 2005 databases respectively for 3-second trials.

Recognizing human emotions from partial facial features is quite hard to achieve reasonable accuracy. In this paper, we propose to use a tree structure representation to simulate as human perceiving the real human face and both the entities and relationship could contribute to the facial expression features. Moreover, a new structural connectionist architecture based on a probabilistic approach to adaptive processing of data structures is presented to generalize the FacE Emotion Tree Structures (FEETS). We demonstrated the robustness of our proposed system in recognizing the correct emotion based on partial face features. The system yields an accuracy of about 90% for subjects with partial face covered by artifacts.

This paper describes a method of structural pattern recognition based on a genetic evolution processing of data structures with neural networks representation. Conventionally, one of the most popular learning formulations of data structure processing is backpropagation through structures (BPTS) [C. Goller et al., (1996)]. The BPTS algorithm has been successfully applied to a number of learning tasks that involved structural patterns such as image, shape, and texture classifications. However, this BPTS typed algorithm suffers from the long-term dependency problem in learning very deep tree structures. In this paper, we propose the genetic evolution for this data structures processing. The idea of this algorithm is to tune the learning parameters by the genetic evolution with specified chromosome structures. Also, the fitness evaluation as well as the adaptive crossover and mutation for this structural genetic processing are investigated in this paper. An application to flowers image classification by a structural representation is provided for the validation of our method. The obtained results significantly support the capabilities of our proposed approach to classify and recognize flowers in terms of generalization and noise robustness.

Many researchers have explored the use of neural-network representations for the adaptive processing of data structures. One of the most popular learning formulations of data structure processing is backpropagation through structure (BPTS). The BPTS algorithm has been successful applied to a number of learning tasks that involve structural patterns such as logo and natural scene classification. The main limitations of the BPTS algorithm are attributed to slow convergence speed and the long-term dependency problem for the adaptive processing of data structures. In this paper, an improved algorithm is proposed to solve these problems. The idea of this algorithm is to optimize the free learning parameters of the neural network in the node representation by using least-squares-based optimization methods in a layer-by-layer fashion. Not only can fast convergence speed be achieved, but the long-term dependency problem can also be overcome since the vanishing of gradient information is avoided when our approach is applied to very deep tree structures.

In this article, a new methodology for color shape from shading (SFS) problem is proposed. The problem of color SFS refers to the well-known fact that most real objects usually contain mixtures of diffuse and specular color reflections and are affected by the multicolored interreflection under unknown reflectivity. In this article, these limitations are addressed, and a new color SFS methodology is proposed. The proposed approach focuses on two main parts. First, a generalized neural-based color reflectance model is developed. Second, an iterative recursive method is developed to reconstruct a multicolor 3D surface. Experimental results on synthetic-colored objects and real-colored objects were performed to demonstrate the performance of the proposed methodology.

It is known that most real surfaces usually are neither perfectly Lambertian model nor ideally specular model; rather, they are formed by the hybrid structure of these two models. This hybrid reflectance model still suffers from the noise, strong specular, and unknown reflectivity conditions. In this article, these limitations are addressed, and a new neural-based hybrid reflectance model is proposed. The goal of this method is to optimize a proper reflectance model by learning the weight and parameters of the hybrid structure of feedforward neural networks and radial basis function networks and to recover the 3D object shape by the shape from shading technique with this resulting model. Experimental results, including synthetic and real images, were performed to demonstrate the performance of the proposed reflectance model in the case of different specular effects and noise environments.

The shape from shading problem refers to the well-known fact that most real images usually contain specular components and are affected by unknown reflectivity. In this paper, these limitations are addressed and a new neural-based 3D shape reconstruction model is proposed. The idea behind this approach is to optimize a proper reflectance model by learning the parameters of the proposed neural reflectance model. In order to do this, new neural-based reflectance models are presented. The feedforward neural network (FNN) model is able to generalize the diffuse term, while the RBF model is able to generalize the specular term. A hybrid structure of FNN-based and RBF-based models is also presented because most real surfaces are usually neither Lambertian models nor ideally specular models. Experimental results, including synthetic and real images, are presented to demonstrate the performance of our approach given different specular effects, unknown illuminate conditions, and different noise environments.

For the shape from shading problem, it is known that most real images usually contain specular components and are affected by unknown reflectivity. In this paper, these limitations are addressed and a new neural-based specular reflectance model is proposed. The idea of this method is to optimize a proper specular model by learning the parameters of a radial basis function network and to recover the object shape by the variational approach with this resulting model. The obtained results are very encouraging and the performance is demonstrated by using the synthetic and real images in the case of different specular effects and noisy environments.

In this paper, we present a neural-based reflectance model of which the physical parameters of the reflectivity under different lighting conditions are interpreted by the network weights. The idea of our method is to optimize a proper reflectance model by an effective learning algorithm and to recover the object surface by a simple shape from shading recursive algorithm with this resulting model. Experimental results, including synthetic and real images, were performed to demonstrate the performance of the proposed method for practical applications.

A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.