Object Recognition

This paper presents a haptic virtual reality tool developed to enhance the accessibility for the visually impaired. The proposed approach focuses on the development of a highly interactive haptic virtual reality system that allows visually impaired, to study and interact with various virtual objects in specially designed virtual environments. The system is based on the use of the CyberGrasp™ and the PHANToM™ haptic devices. A number of custom applications have been developed based on object recognition and manipulation, and utilizing the advantages of both haptic devices. The system has been tested and evaluated in three custom training applications for the visually impaired.

In this paper, a new algorithm for Automatic License Plate Localisation and Recognition (ALPR) is proposed on the basis of isotropic dilation that can be achieved using the binary image Euclidean distance transform. In a blob analysis problem, any two Region of Interest (RoIs) that is discontinuous are typically treated as separate blobs. However, the proposed algorithm combine with Connected Component Analysis (CCA) are coded to seek for RoI within a certain distance of other RoI to be treated as non-unique. This paper investigates the design and implementation of several pre-processing techniques and isotropic dilation algorithm to classify moving vehicles with different backgrounds and varying angles. A multi-layer feed-forward back-propagation Neural Network is used to train the segmented and refined characters. The results obtained can be used for implementation in the vehicle parking management system.

We describe a method of generating and utilizing visual landmarks that is well suited for SLAM applications. The landmarks created are highly distinctive and reliably detected, virtually eliminating the data association problem present in other landmark schemes. Upon subsequent detections of a landmark, a 3-D pose can be estimated. The scheme requires a single camera.

We present an efficient Hough transform for automatic detection of cylinders in point clouds. As cylinders are one of the most frequently used primitives for industrial design, automatic and robust methods for their detection and fitting are essential for reverse engineering from point clouds. The current methods employ automatic segmentation followed by geometric fitting, which requires a lot of manual interaction during modelling. Although Hough transform can be used for automatic detection of cylinders, the required 5D Hough space has a prohibitively high time and space complexity for most practical applications. We address this problem in this paper and present a sequential Hough transform for automatic detection of cylinders in point clouds. Our algorithm consists of two sequential steps of low dimensional Hough trans- forms. The first step, called Orientation Estimation, uses the Gaussian sphere of the input data and performs a 2D Hough Transform for finding strong hypotheses ...

MORSE is an object recognition system, based on geometric invariants of 3D structures taken from a single 2D intensity view. The system exploits the geometric constraints inherent in object classes such as polyhedra, rotational symmetry, bi-lateral symmetry and extruded surfaces. Invariants have been used in the past to index many of these classes, but MORSE is designed to treat multi-class recognition in a unform system architecture. The class constraints are also used to drive image feature extraction and grouping. 1 Invariant Representation The computer recognition of objects has attracted considerable research effort over the last 25 years. It is now widely accepted that object recognition, in the setting of real world scenes and based on a single perspective view, is a difficult problem and cannot be achieved without the use of object models to guide the processing of image data and to confirm object hypotheses. It is also accepted that the most reliable information which is av...

This paper introduces a successful approach for distinguishing abandoned luggage in surveillance recordings. We join short-and long-term foundation models to concentrate on closer view of objects, where every pixel in an information picture is named a 2 bit code. In this manner, we acquaint a structure with recognized static frontal areas in light of the worldly move of code designs, and to figure out if the applicant districts contain surrendered protests by breaking down the back-followed directions of baggage proprietors. The trial comes about acquired in light of video pictures from 2006 performance evaluation of tracking and surveillance, and 2007 advanced video and signal-based surveillance databases demonstrate that the proposed approach is successful for identifying relinquished gear, and that it outflanks past techniques.

Color-based object recognition is typically concerned with building statistical descriptions from pixels that correspond to an object class and then using these models to detect pixels that belong to previously seen objects. Specific instances of color-based classification occur in a number of computer vision problems including background modeling, image-based retrieval, and multi-view object recognition and tracking. Color-based models are dependent on the intrinsic parameters of the camera(s) used to acquire them. Rather than view this as a problem, we propose to utilize this relationship to control (to a degree) how color models are acquired by modifying camera intrinsics. In particular, we introduce an algorithm that searches for the best set of camera settings that will facilitate class separability for a given set of colored objects. The method searches the space of color settings including white balance, hue and saturation in order to maximize classification accuracy of examp...

This paper presents a color object recognition scheme which proceeds in three sequential steps: segmentation, features extraction and classification. We mainly focus on the first and the third steps here. A color watershed using global and local criteria is first described. A color contrast value is defined to select the best color space for segmenting color objects. Then, an architecture of binary neural networks is described. Its properties relies on the simplification of the recognition problem, leading to a noticeable increase in the classification rate. We conclude with the abilities of such a recognition scheme and present an automated cell sorting system.

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.

In this paper we present the main features of software modules dedicated to the aid of visually impaired or blind users. The main aim of developing this software is to reduce or eliminate the need of separate dedicated devices for object recognition and motion detection. The software modules are designed for Android operating system, used in majority of the smartphones today. There are two main trainable (ANN based)modules, namely, the object recognition module and the motion detection module. Image processing algorithms used to identify the objects and detect motion are described. Notification to the users is given by means of verbal messages in this system.

Vehicle detection and classification are daily challenges for computer vision algorithms. The wide range of applications, together with the large amount of data available, raises interest towards these topics up to the point at which new techniques with excellent performances are developed constantly. Still, while trying to generalize the results to different targets, issues arise due to the large number of variables that affect the scores. In this work, we describe how to approach the delicate choice of the best vision-based application for vehicle detection and classification on a reallife dataset, performing parameter evaluation and scoring for the GMG+SVM, MoG2+SVM and Faster R-CNN techniques. We also highlight how the best network choice is affected by the specific usage requirements.

Research in learning algorithms and sensor hardware has led to rapid advances in artificial systems over the past decade. However, their performance continues to fall short of the efficiency and versatility of human behavior. In many ways, a deeper understanding of how human perceptual systems process and act upon physical sensory information can contribute to the development of better artificial systems. In the presented research, we highlight how the latest tools in computer vision, computer graphics, and virtual reality technology can be used to systematically understand the factors that determine how humans perform in realistic scenarios of complex task-solving.

In modern times the quantity of on road vehicles is expanding very quickly. Most of the time, it is important to verify the identity of these vehicles for authorization of the transit regulation, overseeing parking garages. it is hard to check this colossal number of moving vehicles physically. Subsequently, building up a precise automatic license plate recognition model (ALPR) including character recognition is important to ease the issues mentioned above. We have developed a model based on multiple types of license plates from different countries. The dataset of images was trained using Yolov4 which uses CNN architectures. Character recognition was done using the Tesseract OCR after multiple image pre-processing techniques and morphological transformations. The proposed program has obtained an accuracy of 92% in license plate detection and 81% in character recognition.