1,822 research outputs found

    Object Tracking Using Local Binary Descriptors

    Get PDF
    Visual tracking has become an increasingly important topic of research in the field of Computer Vision (CV). There are currently many tracking methods based on the Detect-then-Track paradigm. This type of approach may allow for a system to track a random object with just one initialization phase, but may often rely on constructing models to follow the object. Another limitation of these methods is that they are computationally and memory intensive, which hinders their application to resource constrained platforms such as mobile devices. Under these conditions, the implementation of Augmented Reality (AR) or complex multi-part systems is not possible. In this thesis, we explore a variety of interest point descriptors for generic object tracking. The SIFT descriptor is considered a benchmark and will be compared with binary descriptors such as BRIEF, ORB, BRISK, and FREAK. The accuracy of these descriptors is benchmarked against the ground truth of the object\u27s location. We use dictionaries of descriptors to track regions with small error under variations due to occlusions, illumination changes, scaling, and rotation. This is accomplished by using Dense-to-Sparse Search Pattern, Locality Constraints, and Scale Adaptation. A benchmarking system is created to test the descriptors\u27 accuracy, speed, robustness, and distinctness. This data offers a comparison of the tracking system to current state of the art systems such as Multiple Instance Learning Tracker (MILTrack), Tracker Learned Detection (TLD), and Continuously Adaptive MeanShift (CAMSHIFT)

    Balanced crossover operators in Genetic Algorithms

    Get PDF
    In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA) introduced new balanced crossover operators which ensure that the offspring has the same balancedness characteristics of the parents. However, the use of such operators has never been thoroughly motivated, except for some generic considerations about search space reduction. In this paper, we undertake a rigorous statistical investigation on the effect of balanced and unbalanced crossover operators against three optimization problems from the area of cryptography and coding theory: nonlinear balanced Boolean functions, binary Orthogonal Arrays (OA) and bent functions. In particular, we consider three different balanced crossover operators (each with two variants: \u201cleft-to-right\u201d and \u201cshuffled\u201d), two of which have never been published before, and compare their performances with classic one-point crossover. We are able to confirm that the balanced crossover operators perform better than one-point crossover. Furthermore, in two out of three crossovers, the \u201cleft-to-right\u201d version performs better than the \u201cshuffled\u201d version

    Investigating the use of multi-label classification methods for the purpose of classifying electromyographic signals

    Get PDF
    The type of pattern recognition methods used for controlling modern prosthetics, referred to here as single-label classification methods, restricts users to a small amount of movements. One prominent reason for this is that the accuracy of these classification methods decreases as the number of allowed movements is increased. In this work a possible solution to this problem is presented by looking into the use of multi-label classification for classifying electromyographic signals. This was accomplished by going through the process of recording, processing, and classifying electromyographic data. In order to compare the performance of multi-label methods to that of single-label methods four classification methods from each category were selected. Both categories were then tested on their ability to classify finger flexion movements. The most commonly tested set of movements were the thumb, index, long, and ring finger movements in addition to all the possible combinations of these four fingers. The two categories were also tested on their ability to learn finger combination movements when only individual finger movements were used as training data. The results show that the tested single- and multi-label methods obtain similar classification accuracy when the training data consists of both individual finger movements and finger combination movements. The results also show that none of the tested single-label methods and only one of the tested multi-label methods, multi-label rbf neural networks, manages to learn finger combination movements when trained on only individual finger movements.Using multi-label classification methods to classify finger movements for hand prosthesis control Losing a limb is a traumatic experience that greatly impacts a person’s quality of life. To help the people who have suffered limb loss prosthetic devices were invented. The purpose of a prosthetic device is to mimic the function of the missing limb..

    Reed-Muller codes for random erasures and errors

    Full text link
    This paper studies the parameters for which Reed-Muller (RM) codes over GF(2)GF(2) can correct random erasures and random errors with high probability, and in particular when can they achieve capacity for these two classical channels. Necessarily, the paper also studies properties of evaluations of multi-variate GF(2)GF(2) polynomials on random sets of inputs. For erasures, we prove that RM codes achieve capacity both for very high rate and very low rate regimes. For errors, we prove that RM codes achieve capacity for very low rate regimes, and for very high rates, we show that they can uniquely decode at about square root of the number of errors at capacity. The proofs of these four results are based on different techniques, which we find interesting in their own right. In particular, we study the following questions about E(m,r)E(m,r), the matrix whose rows are truth tables of all monomials of degree r\leq r in mm variables. What is the most (resp. least) number of random columns in E(m,r)E(m,r) that define a submatrix having full column rank (resp. full row rank) with high probability? We obtain tight bounds for very small (resp. very large) degrees rr, which we use to show that RM codes achieve capacity for erasures in these regimes. Our decoding from random errors follows from the following novel reduction. For every linear code CC of sufficiently high rate we construct a new code CC', also of very high rate, such that for every subset SS of coordinates, if CC can recover from erasures in SS, then CC' can recover from errors in SS. Specializing this to RM codes and using our results for erasures imply our result on unique decoding of RM codes at high rate. Finally, two of our capacity achieving results require tight bounds on the weight distribution of RM codes. We obtain such bounds extending the recent \cite{KLP} bounds from constant degree to linear degree polynomials
    corecore