Standard error correction codes (ECC) assume good data compression, thus expecting uniform apriori distribution of data. This assumption does not allow the exploitation of actual non-uniform priors, which may exist, to improve the threshold at which ECC decoding fails. This work presents a new scheme that builds a probabilistic model for data and uses this model to enhance ECC decoding performance. The approach is flexible because training is done online and does not assume any specific data type or structure but only the existence of some temporal correlation between codewords. This scheme can be helpful for a large class of systems, such as wireless sensors and autonomous platforms. The method was tested via simulation using standard ECC from the IEEE802.11 standard and in experiments in which an autonomous platform transmitted data to the base station. The results show significant improvement in decoding performance. Additionally, the article explains the nature of the performance gain.

Light conditions affect the performance of computer vision algorithms by creating spatial changes in color and intensity across a scene. Convolutional neural networks (CNNs) use color components of the input image and, as a result, are sensitive to ambient light conditions. This work analyzes the influence of ambient light conditions on CNN classifiers. We suggest a method for boosting the performance of CNN-based object detection and classification algorithms by using light invariant video imaging (LIVI). LIVI neutralizes the influence of ambient light conditions and renders the perceived object's appearance independent of the light conditions. Training sets consist mainly, if not only, of objects in natural light conditions. As such, using LIVI boosts CNN performance by matching object appearance to that expected by the CNN model, which was created according to the training set. We further investigate the use of LIVI as a general self-supervised learning framework for CNN. Faster region-based CNN (Faster R-CNN) was used as a case study in order to validate the importance of light conditions on CNN performance and on how it can be improved by using LIVI as an input or feedback mechanism in a self-supervised framework. We show that LIVI enables reduced CNN size, enhanced performance and improved training.

Controlled light conditions improve considerably the performance of most computer vision algorithms. Dynamic light conditions create varying spatial changes in color and intensity across the scene. These condition, caused by a moving shadow for example, force developers to create algorithms which are robust to such variations. We suggest a computational camera which produces images that are not influenced by environmental variations in light conditions. The key insight is that many years ago, similar difficulties were already solved in radio communication, As a result each channel is immune to interference from other radio channels. Amplitude Modulated (AM) video camera separates the influence of a modulated light from other unknown light sources in the scene, Causing the AM video camera frame to appear the same - independent of the light conditions in which it was taken. We built a prototype of the AM video camera by using off the shelf hardware and tested it. AM video camera was used to demonstrate color constancy, shadow removal and contrast enhancement in real time. We show theoretically and empirically that: 1. the proposed system can produce images with similar noise levels as a standard camera. 2. The images created by such camera are almost completely immune to temporal, spatial and spectral changes in the background light.

Autonomous vehicle technology is at an all-time peak, and forecasts estimate that humankind is less than a decade away from witnessing the first commercially sold autonomous vehicles. However, most of the platforms developed today are very restricting and are not transferable, and it will take a long time before the worldwide fleet of vehicles will be replaced by autonomous vehicles. To overcome this problem, we have designed a unique solution in the form of a robot driver that can transform any current standard vehicle platform into an autonomous vehicle. The robot can be mounted in the driver's seat in several minutes and can operate the steering wheel and the pedals. This paper outlines the architecture, mechanical design, and specific modules of the Intelligent Vehicle Operator (IVO) driver robot and demonstrates a proof of concept.

Spectral Matching (SM) is a computationally efficient approach to approximate the solution of pairwise matching problems that are np-hard. In this paper, we present a probabilistic interpretation of spectral matching schemes and derive a novel Probabilistic Matching (PM) scheme that is shown to outperform previous approaches. We show that spectral matching can be interpreted as a Maximum Likelihood (ML) estimate of the assignment probabilities and that the Graduated Assignment (GA) algorithm can be cast as a Maximum a Posteriori (MAP) estimator. Based on this analysis, we derive a ranking scheme for spectral matchings based on their reliability, and propose a novel iterative probabilistic matching algorithm that relaxes some of the implicit assumptions used in prior works. We experimentally show our approaches to outperform previous schemes when applied to exhaustive synthetic tests as well as the analysis of real image sequences.

Speaker diarization systems attempt to assign temporal segments from a conversation betweenRspeakers to an appropriate speakerr. This task is generally performed when no prior information is given regarding the speakers. The number of speakers is usually unknown and needs to be estimated. However, there are applications where the number of speakers is known in advance. The diarization process generally consists of change detection, clustering and labeling of a given audio stream. Speaker diarization can be performed using an iterative approach that is optimized by the selection of appropriate initial conditions. This study examines the influence of several common initialization algorithms including two variants of a recently proposed, K-means based initialization algorithm over the performance of an iterative-based speaker diarization system applied to two speaker telephone conversations. The suggested speaker diarization system employs either self organizing maps or Gaussian mixture models in order to model the speakers and non-speech in the conversation. The diarization system and initialization algorithms are tuned using 108 telephone conversations taken from LDC CallHome corpus, this is the development set. The evaluation subset is composed of 2048 telephone conversations extracted from the NIST 2005 Rich Transcription corpus. The results obtained show that by initializing the speaker diarization system using the K-means based algorithms provide a relative improvement of 10.4% for the LDC development set and 12.2% for the NIST evaluation subset when compared to random initialization after 12 iterations which are required for the convergence of the diarization process using random initialization. However, when using the K-means based initialization approach, only five iterations are required for the system to converge. Thus, using the new initialization allows us to improve the performances both in terms of diarization error rate and speed of convergence.

Speaker diarization systems attempt segmentation and labeling of a conversation between R speakers, while no prior information is given regarding the conversation. Most state of the art diarization systems require the full body of the conversation data prior to the application of some diarization approach. However, for some applications such as forensics, which handles vast amount of data, an on-line or incremental diarization is of high importance. For that purpose, a two-stage incremental diarization of telephone conversations algorithm is suggested. On the first stage, a fully unsupervised diarization algorithm is applied over an initial training segment from the conversation. The secondstage is composed of time-series clustering of increments of the conversation. Applying incremental diarization over 1802 telephone conversations from NIST 2005 SER generated an increase in diarization error of approximately 2% compared to the diarization error of an off-line diarization system.

We propose two computational approaches for improving the retrieval of planar shapes. First, we suggest a geometrically motivated quadratic similarity measure, that is optimized by way of spectral relaxation of a quadratic assignment. By utilizing state-of-the-art shape descriptors and a pairwise serialization constraint, we derive a formulation that is resilient to boundary noise, articulations and nonrigid deformations. This allows both shape matching and retrieval. We also introduce a shape meta-similarity measure that agglomerates pairwise shape similarities and improves the retrieval accuracy. When applied to the MPEG-7 shape dataset in conjunction with the proposed geometric matching scheme, we obtained a retrieval rate of 92.5%.

One inherent deficiency of most diarization systems is their inability to handle co-channel or overlapped speech. Most of the suggested algorithms perform under singular conditions, require high computational complexity in both time and frequency domains. In this study, frame based entropy analysis of the audio data in the time domain serves as a single feature for an overlapped speech detection algorithm. Identification of overlapped speech segments is performed using Gaussian Mixture Modeling (GMM) along with well known classification algorithms applied on two speaker conversations. By employing this methodology, the proposed method eliminates the need for setting a hard threshold for each conversation or database. LDC CALLHOME American English corpus is used for evaluation of the suggested algorithm. The proposed method successfully detects 63.2% of the frames labeled as overlapped speech by the manual segmentation, while keeping a 5.4% false-alarm rate.

A new approach for initial assignment of data in a speaker clustering application is presented. This approach employs Weighted Segmental K-Means clustering algorithm prior to competitive based learning. The clustering system relies on Self-Organizing Maps (SOM) for speaker modeling and likelihood estimation. Performance is evaluated on 108 two speaker conversations taken from LDC CALLHOME American English Speech corpus using NIST criterion and shows an improvement of approximately 48% in Cluster Error Rate (CER) relative to the randomly initialized clustering system. The number of iterations was reduced significantly, which contributes to both speed and efficiency of the clustering system.

In many time-series such as speech, biosignals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration, such data can be referred to as piecewise-dependent data (PDD). In clustering, it is frequently needed to minimize a given distance function. In this letter, we will show that in PDD clustering there is a contradiction between the desire for high resolution (short segments and low distance) and high accuracy (long segments and high distance), i.e., meaningful clustering.

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. No a priori assumptions whatsoever are made about the image (type, features, contents, stochastic model, etc.). Such an "universal" algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The proposed system can be readily employed, "as is," or as a basic building block by a more sophisticated and/or application-specific image segmentation algorithm. By monitoring the fuzzy entropy relaxation process, the system is able to detect edge pixels.

We present a method for clustering the speakers from unlabeled and unsegmented conversation (with known number of speakers), when no a priori knowledge about the identity of the participants is given. Each speaker was modeled by a self-organizing map (SOM). The SOMs were randomly initiated. An iterative algorithm allows the data move from one model to another and adjust the SOMs. The restriction that the data can move only in small groups but not by moving each and every feature vector separately force the SOMs to adjust to speakers (instead of phonemes or other vocal events). This method was applied to high-quality conversations with two to five participants and to two-speaker telephone-quality conversations. The results for two (both high- and telephone-quality) and three speakers were over 80% correct segmentation. The problem becomes even harder when the number of participants is also unknown. Based on the iterative clustering algorithm a validity criterion was also developed to estimate the number of speakers. In 16 out of 17 conversations of high-quality conversations between two and three participants, the estimation of the number of the participants was correct. In telephone-quality the results were poorer.

Feature extraction for exploratory data projection aims for data visualization by a projection of a high-dimensional space onto two or three-dimensional space, while feature extraction for classification generally requires more than two or three features. We study extraction of more than three features, using neural network (NN) implementation of Sammon's mapping to be applied for classification. The experiments reveal that Sammon's mapping, the multilayer perceptron (MLP) and the principal component analysis (PCA) based feature extractors yield similar classification performance. We investigate a random- and PCA-based initializations of Sammon's mapping. When the PCA is applied to initialize Sammon's projection, only one experiment is required and only a fraction of the training period is needed to achieve performance comparable with that of the random initialization. Furthermore, the PCA based initialization affords better human chromosome classification performance even when using a few eigenvectors.

A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10-20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.

A multilayer perceptron (MLP) neural network (NN) was used for human chromosome classification. The significance of relevant chromosome features to the classification procedure was evaluated using a feature selection mechanism. It yielded the benefit of using only a part of the available features to get performance close to the ultimate one, classifying chromosomes of 5 types. Only 10-20 examples were required for the MLP NN classifier to reach its supreme performance disregarding the number of features used. Furthermore, the empirical entropic error of the classifier was found to be highly comparable to the 1/t function that is a universal learning curve.